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merge</span></span></a></li></ul></div></li><li><div class="group__i-sw4v"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--monospace___QXHDQ Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--100___B2ueX Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__g2f4Ur" id="_R_dknd_">APPLICATION SECURITY</span><ul class="list__GYi3Jn" aria-labelledby="_R_dknd_"><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 link__3viuP9" href="https://github.com/security/advanced-security" data-analytics-event="{&quot;action&quot;:&quot;github_advanced_security&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;platform&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;github_advanced_security_link_platform_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD 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0 0 1 1.217-1.667l5.25-1.68a1.748 1.748 0 0 1 1.066 0Zm-.61 1.429.001.001-5.25 1.68a.251.251 0 0 0-.174.237V7c0 1.36.275 2.666 1.057 3.859.784 1.194 2.121 2.342 4.366 3.298a.196.196 0 0 0 .154 0c2.245-.957 3.582-2.103 4.366-3.297C13.225 9.666 13.5 8.358 13.5 7V3.48a.25.25 0 0 0-.174-.238l-5.25-1.68a.25.25 0 0 0-.153 0ZM11.28 6.28l-3.5 3.5a.75.75 0 0 1-1.06 0l-1.5-1.5a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215l.97.97 2.97-2.97a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path></svg>GitHub Advanced Security</span><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS subtitle__gxA0qn">Find and fix vulnerabilities</span></span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 link__3viuP9" href="https://github.com/security/advanced-security/code-security" data-analytics-event="{&quot;action&quot;:&quot;code_security&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;platform&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;code_security_link_platform_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__MTYMi3"><svg data-component="Octicon" aria-hidden="true" focusable="false" class="octicon octicon-code-square icon__Sf_Nku" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25Zm7.47 3.97a.75.75 0 0 1 1.06 0l2 2a.75.75 0 0 1 0 1.06l-2 2a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L10.69 8 9.22 6.53a.75.75 0 0 1 0-1.06ZM6.78 6.53 5.31 8l1.47 1.47a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215l-2-2a.75.75 0 0 1 0-1.06l2-2a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path></svg>Code security</span><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--200____P1wy 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Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__MTYMi3"><svg data-component="Octicon" aria-hidden="true" focusable="false" class="octicon octicon-lock icon__Sf_Nku" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M4 4a4 4 0 0 1 8 0v2h.25c.966 0 1.75.784 1.75 1.75v5.5A1.75 1.75 0 0 1 12.25 15h-8.5A1.75 1.75 0 0 1 2 13.25v-5.5C2 6.784 2.784 6 3.75 6H4Zm8.25 3.5h-8.5a.25.25 0 0 0-.25.25v5.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-5.5a.25.25 0 0 0-.25-.25ZM10.5 6V4a2.5 2.5 0 1 0-5 0v2Z"></path></svg>Secret protection</span><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--200____P1wy 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Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Enterprises</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/team" data-analytics-event="{&quot;action&quot;:&quot;small_and_medium_teams&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;small_and_medium_teams_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Small and medium teams</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/enterprise/startups" data-analytics-event="{&quot;action&quot;:&quot;startups&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;startups_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Startups</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/industry/nonprofits" data-analytics-event="{&quot;action&quot;:&quot;nonprofits&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;nonprofits_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Nonprofits</span></a></li></ul></div></li><li><div class="group__i-sw4v"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--monospace___QXHDQ Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--100___B2ueX Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__g2f4Ur" id="_R_9l7d_">BY USE CASE</span><ul class="list__GYi3Jn" aria-labelledby="_R_9l7d_"><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/use-case/app-modernization" data-analytics-event="{&quot;action&quot;:&quot;app_modernization&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;app_modernization_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">App Modernization</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/use-case/devsecops" data-analytics-event="{&quot;action&quot;:&quot;devsecops&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;devsecops_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">DevSecOps</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/use-case/devops" data-analytics-event="{&quot;action&quot;:&quot;devops&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;devops_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">DevOps</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/use-case/ci-cd" data-analytics-event="{&quot;action&quot;:&quot;ci/cd&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;ci/cd_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">CI/CD</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 Primer_Brand__Link-module__Link--arrow-end___esdN8" href="https://github.com/solutions/use-case" data-analytics-event="{&quot;action&quot;:&quot;view_all_use_cases&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;view_all_use_cases_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">View all use cases</span><svg class="Primer_Brand__ExpandableArrow-module__ExpandableArrow___aaZs9 Primer_Brand__Link-module__Link-arrow___yd78i" width="16" height="16" viewBox="0 0 16 16" fill="none" aria-hidden="true" focusable="false"><path fill="currentColor" d="M7.28033 3.21967C6.98744 2.92678 6.51256 2.92678 6.21967 3.21967C5.92678 3.51256 5.92678 3.98744 6.21967 4.28033L7.28033 3.21967ZM11 8L11.5303 8.53033C11.8232 8.23744 11.8232 7.76256 11.5303 7.46967L11 8ZM6.21967 11.7197C5.92678 12.0126 5.92678 12.4874 6.21967 12.7803C6.51256 13.0732 6.98744 13.0732 7.28033 12.7803L6.21967 11.7197ZM6.21967 4.28033L10.4697 8.53033L11.5303 7.46967L7.28033 3.21967L6.21967 4.28033ZM10.4697 7.46967L6.21967 11.7197L7.28033 12.7803L11.5303 8.53033L10.4697 7.46967Z"></path><path class="Primer_Brand__ExpandableArrow-module__ExpandableArrow-stem___0K8Hz" stroke="currentColor" d="M1.75 8H11" stroke-width="1.5" stroke-linecap="round"></path></svg></a></li></ul></div></li><li><div class="group__i-sw4v"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--monospace___QXHDQ Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--100___B2ueX Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__g2f4Ur" id="_R_dl7d_">BY INDUSTRY</span><ul class="list__GYi3Jn" aria-labelledby="_R_dl7d_"><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/industry/healthcare" data-analytics-event="{&quot;action&quot;:&quot;healthcare&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;healthcare_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Healthcare</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/industry/financial-services" data-analytics-event="{&quot;action&quot;:&quot;financial_services&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;financial_services_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Financial services</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/industry/manufacturing" data-analytics-event="{&quot;action&quot;:&quot;manufacturing&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;manufacturing_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Manufacturing</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/solutions/industry/government" data-analytics-event="{&quot;action&quot;:&quot;government&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;government_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">Government</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 Primer_Brand__Link-module__Link--arrow-end___esdN8" href="https://github.com/solutions/industry" data-analytics-event="{&quot;action&quot;:&quot;see_all_industries&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;see_all_industries_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">See all industries</span><svg class="Primer_Brand__ExpandableArrow-module__ExpandableArrow___aaZs9 Primer_Brand__Link-module__Link-arrow___yd78i" width="16" height="16" viewBox="0 0 16 16" fill="none" aria-hidden="true" focusable="false"><path fill="currentColor" d="M7.28033 3.21967C6.98744 2.92678 6.51256 2.92678 6.21967 3.21967C5.92678 3.51256 5.92678 3.98744 6.21967 4.28033L7.28033 3.21967ZM11 8L11.5303 8.53033C11.8232 8.23744 11.8232 7.76256 11.5303 7.46967L11 8ZM6.21967 11.7197C5.92678 12.0126 5.92678 12.4874 6.21967 12.7803C6.51256 13.0732 6.98744 13.0732 7.28033 12.7803L6.21967 11.7197ZM6.21967 4.28033L10.4697 8.53033L11.5303 7.46967L7.28033 3.21967L6.21967 4.28033ZM10.4697 7.46967L6.21967 11.7197L7.28033 12.7803L11.5303 8.53033L10.4697 7.46967Z"></path><path class="Primer_Brand__ExpandableArrow-module__ExpandableArrow-stem___0K8Hz" stroke="currentColor" d="M1.75 8H11" stroke-width="1.5" stroke-linecap="round"></path></svg></a></li></ul></div></li></ul><div class="trailingLinkContainer__bY9qr5"><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 Primer_Brand__Link-module__Link--arrow-end___esdN8" href="https://github.com/solutions" data-analytics-event="{&quot;action&quot;:&quot;view_all_solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;view_all_solutions_link_solutions_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">View all solutions</span><svg class="Primer_Brand__ExpandableArrow-module__ExpandableArrow___aaZs9 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aria-controls="_R_1nd_">Resources<svg data-component="Octicon" aria-hidden="true" focusable="false" class="octicon octicon-triangle-right buttonIcon__44FXu9" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m6.427 4.427 3.396 3.396a.25.25 0 0 1 0 .354l-3.396 3.396A.25.25 0 0 1 6 11.396V4.604a.25.25 0 0 1 .427-.177Z"></path></svg></button><div id="_R_1nd_" class="dropdown__jv_u56"><div><ul class="list__F2oFju"><li><div class="group__i-sw4v"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--monospace___QXHDQ Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--100___B2ueX Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__g2f4Ur" id="_R_5lnd_">EXPLORE BY TOPIC</span><ul class="list__GYi3Jn" aria-labelledby="_R_5lnd_"><li><a class="Primer_Brand__Link-module__Link___lF11y 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data-analytics-event="{&quot;action&quot;:&quot;devops&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;devops_link_resources_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty">DevOps</span></a></li><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0" href="https://github.com/resources/articles?topic=security" data-analytics-event="{&quot;action&quot;:&quot;security&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;security_link_resources_navbar&quot;}"><span 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display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m6.427 4.427 3.396 3.396a.25.25 0 0 1 0 .354l-3.396 3.396A.25.25 0 0 1 6 11.396V4.604a.25.25 0 0 1 .427-.177Z"></path></svg></button><div id="_R_2nd_" class="dropdown__jv_u56"><div><ul class="list__F2oFju"><li><div class="group__i-sw4v"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--monospace___QXHDQ Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--100___B2ueX Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__g2f4Ur" id="_R_5mnd_">ENTERPRISE SOLUTIONS</span><ul class="list__GYi3Jn" aria-labelledby="_R_5mnd_"><li><a class="Primer_Brand__Link-module__Link___lF11y Primer_Brand__Link-module__Link--default___VRVW0 link__3viuP9" href="https://github.com/enterprise" data-analytics-event="{&quot;action&quot;:&quot;enterprise_platform&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;location&quot;:&quot;navbar&quot;,&quot;label&quot;:&quot;enterprise_platform_link_enterprise_navbar&quot;}"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Link-module__Link--label___jM8Ty"><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--default___GhPh_ Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS Primer_Brand__Text-module__Text--weight-medium___qJKf_ title__MTYMi3"><svg data-component="Octicon" aria-hidden="true" focusable="false" class="octicon octicon-stack 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3.855a1.697 1.697 0 0 1-1.33 0c-2.447-1.042-4.049-2.357-5.032-3.855C1.32 10.182 1 8.566 1 7V3.48a1.75 1.75 0 0 1 1.217-1.667l5.25-1.68a1.748 1.748 0 0 1 1.066 0Zm-.61 1.429.001.001-5.25 1.68a.251.251 0 0 0-.174.237V7c0 1.36.275 2.666 1.057 3.859.784 1.194 2.121 2.342 4.366 3.298a.196.196 0 0 0 .154 0c2.245-.957 3.582-2.103 4.366-3.297C13.225 9.666 13.5 8.358 13.5 7V3.48a.25.25 0 0 0-.174-.238l-5.25-1.68a.25.25 0 0 0-.153 0ZM11.28 6.28l-3.5 3.5a.75.75 0 0 1-1.06 0l-1.5-1.5a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215l.97.97 2.97-2.97a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z"></path></svg>GitHub Advanced Security</span><span class="Primer_Brand__Text-module__Text___XeGJJ Primer_Brand__Text-module__Text-font--mona-sans___a8XJD Primer_Brand__Text-module__Text--muted___rE6mh Primer_Brand__Text-module__Text--200____P1wy Primer_Brand__Text-module__Text--antialiased___TYoXS subtitle__gxA0qn">Enterprise-grade security features</span></span></a></li><li><a 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Reasoning","anchor":"3d-reasoning","htmlText":"3D Reasoning"},{"level":2,"text":"3D Generation","anchor":"3d-generation","htmlText":"3D Generation"},{"level":2,"text":"3D Embodied Agent","anchor":"3d-embodied-agent","htmlText":"3D Embodied Agent"},{"level":2,"text":"3D Benchmarks","anchor":"3d-benchmarks","htmlText":"3D Benchmarks"},{"level":2,"text":"Contributing","anchor":"contributing","htmlText":"Contributing"},{"level":2,"text":"Star History","anchor":"star-history","htmlText":"Star History"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Acknowledgement","anchor":"acknowledgement","htmlText":"Acknowledgement"}]},"issueTemplate":null,"discussionTemplate":null,"richText":"\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAwesome-LLM-3D \u003ca href=\"https://awesome.re\" 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href=\"https://arxiv.org/abs/2405.10255v2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04020e776412af872beebb06fc104497c6af587f314c59b6351ca5e53c4f9d99/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323430352e313032353576322d6233316231622e737667\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-2405.10255v2-b31b1b.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"user-content-awesome-llm-3d-------\" class=\"anchor\" aria-label=\"Permalink: Awesome-LLM-3D \" href=\"#awesome-llm-3d-------\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n    \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch/assets/Figure1_v8_25.6.9.png\"\u003e\u003cimg src=\"/jkhu29/Awesome-LLM-3D/raw/avl-branch/assets/Figure1_v8_25.6.9.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🏠 About\u003c/h2\u003e\u003ca id=\"user-content--about\" class=\"anchor\" aria-label=\"Permalink: 🏠 About\" href=\"#-about\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere is a curated list of papers about 3D-Related Tasks empowered by Large Language Models (LLMs).\nIt contains various tasks including 3D understanding, reasoning, generation, and embodied agents. Also, we include other Foundation Models (CLIP, SAM) for the whole picture of this area.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis is an active repository, you can watch for following the latest advances. If you find it useful, please kindly star ⭐ this repo and \u003ca href=\"#citation\"\u003ecite\u003c/a\u003e the paper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔥 News\u003c/h2\u003e\u003ca id=\"user-content--news\" class=\"anchor\" aria-label=\"Permalink: 🔥 News\" href=\"#-news\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2026-03-20] Our benchmark paper \u003cstrong\u003eReal-3DQA\u003c/strong\u003e is now available at ICLR 2026! Following our survey paper, we now release the benchmark paper on genuine 3D spatial understanding. \u003ca href=\"https://real-3dqa.github.io/\" rel=\"nofollow\"\u003eProject Page\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2025-10-21] 📢 We have released the \u003cstrong\u003esecond version\u003c/strong\u003e of our survey, updated to include literature up to \u003cstrong\u003eJuly 2025\u003c/strong\u003e:\u003cbr\u003e\n👉 \u003ca href=\"https://arxiv.org/pdf/2405.10255v2\" rel=\"nofollow\"\u003e\u003cem\u003eWhen LLMs Step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024-05-16] Check out the first survey paper in the 3D-LLM domain: \u003ca href=\"https://arxiv.org/pdf/2405.10255\" rel=\"nofollow\"\u003eWhen LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024-01-06] \u003ca href=\"https://runsenxu.com/\" rel=\"nofollow\"\u003eRunsen Xu\u003c/a\u003e added chronological information and \u003ca href=\"https://xianzhengma.github.io/\" rel=\"nofollow\"\u003eXianzheng Ma\u003c/a\u003e reorganized it in Z-A order for better following the latest advances.\u003c/li\u003e\n\u003cli\u003e[2023-12-16] \u003ca href=\"https://xianzhengma.github.io/\" rel=\"nofollow\"\u003eXianzheng Ma\u003c/a\u003e and \u003ca href=\"https://yashbhalgat.github.io/\" rel=\"nofollow\"\u003eYash Bhalgat\u003c/a\u003e curated this list and published the first version;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#awesome-llm-3D\"\u003eAwesome-LLM-3D\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#3d-unified-understanding-and-generation-via-llm\"\u003e3D Unified Understanding and Generation (LLM)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-understanding-via-llm\"\u003e3D Understanding (LLM)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-understanding-via-other-foundation-models\"\u003e3D Understanding (other Foundation Models)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-reasoning\"\u003e3D Reasoning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-generation\"\u003e3D Generation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-embodied-agent\"\u003e3D Embodied Agent\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3d-benchmarks\"\u003e3D Benchmarks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Unified Understanding and Generation via LLM\u003c/h2\u003e\u003ca id=\"user-content-3d-unified-understanding-and-generation-via-llm\" class=\"anchor\" aria-label=\"Permalink: 3D Unified Understanding and Generation via LLM\" href=\"#3d-unified-understanding-and-generation-via-llm\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003eKeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute (first)\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOmni-View\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2511.07222\" rel=\"nofollow\"\u003eOmni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AIDC-AI/Omni-View\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-08-16\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniUGG\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFDU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2508.11952\" rel=\"nofollow\"\u003eUniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/fudan-zvg/UniUGG\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Understanding via LLM\u003c/h2\u003e\u003ca id=\"user-content-3d-understanding-via-llm\" class=\"anchor\" aria-label=\"Permalink: 3D Understanding via LLM\" href=\"#3d-understanding-via-llm\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003eKeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute (first)\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003eG\u003csup\u003e2\u003c/sup\u003eVLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2511.21688\" rel=\"nofollow\"\u003eG\u003csup\u003e2\u003c/sup\u003eVLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/InternRobotics/G2VLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOmni-View\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2511.07222\" rel=\"nofollow\"\u003eOmni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AIDC-AI/Omni-View\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-08-16\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniUGG\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFDU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2508.11952\" rel=\"nofollow\"\u003eUniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/fudan-zvg/UniUGG\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-07-31\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-R1\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2507.23478\" rel=\"nofollow\"\u003e3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://aigeeksgroup.github.io/3D-R1/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-11\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLEO-VL\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.09935\" rel=\"nofollow\"\u003eLEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://leo-vl.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-02\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3DRS\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/html/2506.01946v1\" rel=\"nofollow\"\u003eMLLMs Need 3D-Aware Representation Supervision for Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://visual-ai.github.io/3drs/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-30\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVG LLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2505.24625\" rel=\"nofollow\"\u003eLearning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://lavi-lab.github.io/VG-LLM/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatial-MLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTHU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2505.23747\" rel=\"nofollow\"\u003eSpatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://diankun-wu.github.io/Spatial-MLLM/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSeeGround\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKUST(GZ)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2505.22429\" rel=\"nofollow\"\u003eZero-Shot 3D Visual Grounding from Vision-Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPRW'25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://seeground.github.io\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3DLLM-Mem\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCLA/Google\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2505.22657\" rel=\"nofollow\"\u003e3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS'25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://3dllm-mem.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-04-24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-LLaVA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eU of Adelaide\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2501.01163\" rel=\"nofollow\"\u003e3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/djiajunustc/3D-LLaVA\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-04-03\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoss3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCASIA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2504.01901\" rel=\"nofollow\"\u003eRoss3D: Reconstructive Visual Instruction Tuning with 3D-Awareness\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://haochen-wang409.github.io/ross3d/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-03-08\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSplatTalk\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGIT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2503.06271\" rel=\"nofollow\"\u003eSplatTalk: 3D VQA with Gaussian Splatting\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-03-01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eInst3D-LMM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eZJU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2503.00513\" rel=\"nofollow\"\u003eInst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/hanxunyu/Inst3D-LMM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-02-13\u003c/td\u003e\n\u003ctd align=\"center\"\u003eENEL\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSH AILab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2502.09620v1\" rel=\"nofollow\"\u003eENEL: Exploring the Potential of Encoder-free Architectures in 3D LMMs\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Ivan-Tang-3D/ENEL/tree/main?tab=readme-ov-file\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-02-02\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLSceneLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSCUT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2412.01292\" rel=\"nofollow\"\u003eLSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Hoyyyaard/LSceneLLM\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-01-02\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGPT4Scene\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2501.01428\" rel=\"nofollow\"\u003eGPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://gpt4scene.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-12-05\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSeeGround\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKUST(GZ)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2412.04383\" rel=\"nofollow\"\u003eSeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://seeground.github.io\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-12-03\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVideo-3D LLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2412.00493\" rel=\"nofollow\"\u003eVideo-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LaVi-Lab/Video-3D-LLM\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-11-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePerLA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFondazione Bruno Kessler\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2411.19774\" rel=\"nofollow\"\u003ePerLA: Perceptive 3D Language Assistant\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://gfmei.github.io/PerLA/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-10-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSituation3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUIUC\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2406.07544\" rel=\"nofollow\"\u003eSituational Awareness Matters in 3D Vision Language Reasoning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://yunzeman.github.io/situation3d/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-09-30\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRobin3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2410.00255\" rel=\"nofollow\"\u003eRobin3D: Improving 3D Large Language Model via Robust Instruction Tuning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/WeitaiKang/Robin3D\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-09-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLaVA-3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2409.18125\" rel=\"nofollow\"\u003eLLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zcmax.github.io/projects/LLaVA-3D/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-09-08\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMSR3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2409.02389\" rel=\"nofollow\"\u003eMulti-modal Situated Reasoning in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://msr3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-08-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGreenPLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2408.15966\" rel=\"nofollow\"\u003e More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/TangYuan96/GreenPLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-17\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLaNA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniBO\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2406.11840\" rel=\"nofollow\"\u003eLLaNA: Large Language and NeRF Assistant\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://andreamaduzzi.github.io/llana/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialPIN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOxford\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2403.13438\" rel=\"nofollow\"\u003eSpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://dannymcy.github.io/zeroshot_task_hallucination/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-03\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialRGPT\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCSD\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2406.01584\" rel=\"nofollow\"\u003eSpatialRGPT: Grounded Spatial Reasoning in Vision Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AnjieCheng/SpatialRGPT\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-05-02\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMiniGPT-3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2405.01413\" rel=\"nofollow\"\u003eMiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eACM MM '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://tangyuan96.github.io/minigpt_3d_project_page/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-02-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShapeLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eXJTU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2402.17766\" rel=\"nofollow\"\u003eShapeLLM: Universal 3D Object Understanding for Embodied Interaction\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://qizekun.github.io/shapellm/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-01-22\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialVLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGoogle DeepMind\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2401.12168\" rel=\"nofollow\"\u003eSpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://spatial-vlm.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-21\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLiDAR-LLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.14074.pdf\" rel=\"nofollow\"\u003eLiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://sites.google.com/view/lidar-llm\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-15\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3DAP\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.09738.pdf\" rel=\"nofollow\"\u003e3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-13\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat-Scene\u003c/td\u003e\n\u003ctd align=\"center\"\u003eZJU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.08168.pdf\" rel=\"nofollow\"\u003eChat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZzZZCHS/Chat-Scene\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-5\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGPT4Point\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.02980.pdf\" rel=\"nofollow\"\u003eGPT4Point: A Unified Framework for Point-Language Understanding and Generation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Pointcept/GPT4Point\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-30\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLL3DA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFudan University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.18651.pdf\" rel=\"nofollow\"\u003eLL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Open3DA/LL3DA\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-26\u003c/td\u003e\n\u003ctd align=\"center\"\u003eZSVG3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK(SZ)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.15383.pdf\" rel=\"nofollow\"\u003eVisual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://curryyuan.github.io/ZSVG3D/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-18\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLEO\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.12871.pdf\" rel=\"nofollow\"\u003eAn Embodied Generalist Agent in 3D World\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICML '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/embodied-generalist/embodied-generalist\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-10-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eJM3D-LLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eXiamen University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2310.09503v2.pdf\" rel=\"nofollow\"\u003eJM3D \u0026amp; JM3D-LLM: Elevating 3D Representation with Joint Multi-modal Cues\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eACM MM '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/mr-neko/jm3d\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-10-10\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUni3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBAAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2310.06773\" rel=\"nofollow\"\u003eUni3D: Exploring Unified 3D Representation at Scale\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/baaivision/Uni3D\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKAUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2306.03253\" rel=\"nofollow\"\u003eZero-Shot 3D Shape Correspondence\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSiggraph Asia '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-21\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLM-Grounder\u003c/td\u003e\n\u003ctd align=\"center\"\u003eU-Mich\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.12311.pdf\" rel=\"nofollow\"\u003eLLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICRA '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/sled-group/chat-with-nerf\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-1\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePoint-Bind\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.00615.pdf\" rel=\"nofollow\"\u003ePoint-Bind \u0026amp; Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ZiyuGuo99/Point-Bind_Point-LLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-8-31\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePointLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2308.16911.pdf\" rel=\"nofollow\"\u003ePointLLM: Empowering Large Language Models to Understand Point Clouds\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenRobotLab/PointLLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-8-17\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChat-3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eZJU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2308.08769v1.pdf\" rel=\"nofollow\"\u003eChat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Chat-3D/Chat-3D\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-8-8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-VisTA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://Arxiv.org/abs/2308.04352\" rel=\"nofollow\"\u003e3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-7-24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-LLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCLA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2307.12981.pdf\" rel=\"nofollow\"\u003e3D-LLM: Injecting the 3D World into Large Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/UMass-Foundation-Model/3D-LLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-3-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003eViewRefer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2303.16894.pdf\" rel=\"nofollow\"\u003eViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Ivan-Tang-3D/ViewRefer3D\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-9-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMIT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2209.05629.pdf\" rel=\"nofollow\"\u003eLeveraging Large (Visual) Language Models for Robot 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/MIT-SPARK/llm_scene_understanding\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Understanding via other Foundation Models\u003c/h2\u003e\u003ca id=\"user-content-3d-understanding-via-other-foundation-models\" class=\"anchor\" aria-label=\"Permalink: 3D Understanding via other Foundation Models\" href=\"#3d-understanding-via-other-foundation-models\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eID\u003c/th\u003e\n\u003cth align=\"center\"\u003ekeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute (first)\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePOMA-3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eImperial\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2511.16567\" rel=\"nofollow\"\u003ePOMA-3D: The Point Map Way to 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://matchlab-imperial.github.io/poma3d/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-07-26\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOV-3DDet\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2406.00830\" rel=\"nofollow\"\u003eCoDAv2: Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTPAMI '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/yangcaoai/CoDA_NeurIPS2023\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-02-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCrossOver\u003c/td\u003e\n\u003ctd align=\"center\"\u003eStanford\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2502.15011\" rel=\"nofollow\"\u003eCrossOver: 3D Scene Cross-Modal Alignment\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://sayands.github.io/crossover/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-02-05\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSAGA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSJTU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2312.00860\" rel=\"nofollow\"\u003eSegment Any 3D Gaussians\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eAAAI '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://jumpat.github.io/SAGA/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-10-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLexicon3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUIUC\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2409.03757\" rel=\"nofollow\"\u003eLexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://yunzeman.github.io/lexicon3d/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-10-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDiff2Scene\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCMU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2407.13642\" rel=\"nofollow\"\u003eOpen-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV'24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://diff2scene.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-07-19\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenSU3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2409.03757\" rel=\"nofollow\"\u003eOpenSU3D: Open World 3D Scene Understanding using Foundation Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICRA '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://opensu3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-04-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eAny2Point\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2404.07989\" rel=\"nofollow\"\u003eAny2Point: Empowering Any-modality Large Models for Efficient 3D Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV'24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Ivan-Tang-3D/Any2Point\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-03-16\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN2F2\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOxford-VGG\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2403.10997.pdf\" rel=\"nofollow\"\u003eN2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-17\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSAI3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.11557.pdf\" rel=\"nofollow\"\u003eSAI3D: Segment Any Instance in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://yd-yin.github.io/SAI3D\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-17\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpen3DIS\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVinAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.10671.pdf\" rel=\"nofollow\"\u003eOpen3DIS: Open-vocabulary 3D Instance Segmentation with 2D Mask Guidance\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open3dis.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-6\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOVIR-3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRutgers University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.02873.pdf\" rel=\"nofollow\"\u003eOVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCoRL '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/shiyoung77/OVIR-3D/\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-10-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenMask3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eETH\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openmask3d.github.io/static/pdf/openmask3d.pdf\" rel=\"nofollow\"\u003eOpenMask3D: Open-Vocabulary 3D Instance Segmentation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://openmask3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-10-5\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpen-Fusion\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2310.03923.pdf\" rel=\"nofollow\"\u003eOpen-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/UARK-AICV/OpenFusion\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-22\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOV-3DDet\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2310.02960.pdf\" rel=\"nofollow\"\u003eCoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/yangcaoai/CoDA_NeurIPS2023\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-19\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLAMP\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openreview.net/forum?id=H49g8rRIiF\" rel=\"nofollow\"\u003eFrom Language to 3D Worlds: Adapting Language Model for Point Cloud Perception\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenReview\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-15\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenNerf\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openreview.net/pdf?id=SgjAojPKb3\" rel=\"nofollow\"\u003eOpenNerf: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenReview\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenIns3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCambridge\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.00616.pdf\" rel=\"nofollow\"\u003eOpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zheninghuang.github.io/OpenIns3D/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-6-7\u003c/td\u003e\n\u003ctd align=\"center\"\u003eContrastive Lift\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOxford-VGG\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2306.04633.pdf\" rel=\"nofollow\"\u003eContrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/yashbhalgat/Contrastive-Lift\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-6-4\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMulti-CLIP\u003c/td\u003e\n\u003ctd align=\"center\"\u003eETH\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2306.02329.pdf\" rel=\"nofollow\"\u003eMulti-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-5-23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-OVS\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNTU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2305.14093.pdf\" rel=\"nofollow\"\u003eWeakly Supervised 3D Open-vocabulary Segmentation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/Kunhao-Liu/3D-OVS\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-5-21\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVL-Fields\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniversity of Edinburgh\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2305.12427.pdf\" rel=\"nofollow\"\u003eVL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICRA '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://tsagkas.github.io/vl-fields/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-5-8\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCLIP-FO3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTsinghua University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2303.04748.pdf\" rel=\"nofollow\"\u003eCLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCVW '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-4-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-VQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eETH\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2304.06061.pdf\" rel=\"nofollow\"\u003eCLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPRW '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AlexDelitzas/3D-VQA\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-4-3\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRegionPLC\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2304.00962.pdf\" rel=\"nofollow\"\u003eRegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://jihanyang.github.io/projects/RegionPLC\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-3-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCG3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eJHU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2303.11313.pdf\" rel=\"nofollow\"\u003eCLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/deeptibhegde/CLIP-goes-3D\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-3-16\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLERF\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUC Berkeley\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2303.09553.pdf\" rel=\"nofollow\"\u003eLERF: Language Embedded Radiance Fields\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/kerrj/lerf\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-2-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eConceptFusion\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMIT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2302.07241.pdf\" rel=\"nofollow\"\u003eConceptFusion: Open-set Multimodal 3D Mapping\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRSS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://concept-fusion.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-1-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCLIP2Scene\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2301.04926.pdf\" rel=\"nofollow\"\u003eCLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/runnanchen/CLIP2Scene\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-12-1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniT3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_UniT3D_A_Unified_Transformer_for_3D_Dense_Captioning_and_Visual_ICCV_2023_paper.pdf\" rel=\"nofollow\"\u003eUniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-11-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePLA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2211.16312.pdf\" rel=\"nofollow\"\u003ePLA: Language-Driven Open-Vocabulary 3D Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/CVMI-Lab/PLA\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-11-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOpenScene\u003c/td\u003e\n\u003ctd align=\"center\"\u003eETHz\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2211.15654.pdf\" rel=\"nofollow\"\u003eOpenScene: 3D Scene Understanding with Open Vocabularies\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/pengsongyou/openscene\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-10-11\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCLIP-Fields\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNYU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2210.05663.pdf\" rel=\"nofollow\"\u003eCLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://mahis.life/clip-fields/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-7-23\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSemantic Abstraction\u003c/td\u003e\n\u003ctd align=\"center\"\u003eColumbia\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2207.11514.pdf\" rel=\"nofollow\"\u003eSemantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCoRL '22\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://semantic-abstraction.cs.columbia.edu/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-4-26\u003c/td\u003e\n\u003ctd align=\"center\"\u003eScanNet200\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2204.07761.pdf\" rel=\"nofollow\"\u003eLanguage-Grounded Indoor 3D Semantic Segmentation in the Wild\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV '22\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://rozdavid.github.io/scannet200\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Reasoning\u003c/h2\u003e\u003ca id=\"user-content-3d-reasoning\" class=\"anchor\" aria-label=\"Permalink: 3D Reasoning\" href=\"#3d-reasoning\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003ekeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute (first)\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-12-15\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboTracer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2512.13660\" rel=\"nofollow\"\u003eRoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboTracer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-11\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSceneCOT\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://scenecot.github.io/\" rel=\"nofollow\"\u003eSceneCOT: Eliciting Chain-of-Thought Reasoning in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://scenecot.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-04\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboRefer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.04308\" rel=\"nofollow\"\u003eRoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboRefer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-09-08\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMSR3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2409.02389\" rel=\"nofollow\"\u003eMulti-modal Situated Reasoning in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://msr3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-5-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-CLR\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCLA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2303.11327.pdf\" rel=\"nofollow\"\u003e3D Concept Learning and Reasoning from Multi-View Images\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/evelinehong/3D-CLR-Official\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTranscribe3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTTI, Chicago\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openreview.net/pdf?id=7j3sdUZMTF\" rel=\"nofollow\"\u003eTranscribe3D: Grounding LLMs Using Transcribed Information for 3D Referential Reasoning with Self-Corrected Finetuning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCoRL '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Generation\u003c/h2\u003e\u003ca id=\"user-content-3d-generation\" class=\"anchor\" aria-label=\"Permalink: 3D Generation\" href=\"#3d-generation\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003ekeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-07\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOmni-View\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePKU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2511.07222\" rel=\"nofollow\"\u003eOmni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AIDC-AI/Omni-View\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-08-16\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniUGG\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFDU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2508.11952\" rel=\"nofollow\"\u003eUniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/fudan-zvg/UniUGG\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-11-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLaMA-Mesh\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTHU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/html/2411.09595v1\" rel=\"nofollow\"\u003eLLaMA-Mesh: Unifying 3D Mesh Generation with Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShapeGPT\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFudan University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.17618.pdf\" rel=\"nofollow\"\u003eShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenShapeLab/ShapeGPT\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMeshGPT\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.15475.pdf\" rel=\"nofollow\"\u003eMeshGPT: Generating Triangle Meshes with Decoder-Only Transformers\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nihalsid.github.io/mesh-gpt/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-10-19\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-GPT\u003c/td\u003e\n\u003ctd align=\"center\"\u003eANU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2310.12945.pdf\" rel=\"nofollow\"\u003e3D-GPT: Procedural 3D Modeling with Large Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://dreamllm.github.io/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-21\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLMR\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMIT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.12276.pdf\" rel=\"nofollow\"\u003eLLMR: Real-time Prompting of Interactive Worlds using Large Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDreamLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMEGVII\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.11499.pdf\" rel=\"nofollow\"\u003eDreamLLM: Synergistic Multimodal Comprehension and Creation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/RunpeiDong/DreamLLM\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-4-1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eChatAvatar\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDeemos Tech\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3592094\" rel=\"nofollow\"\u003eDreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eACM TOG\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://hyperhuman.deemos.com/\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Embodied Agent\u003c/h2\u003e\u003ca id=\"user-content-3d-embodied-agent\" class=\"anchor\" aria-label=\"Permalink: 3D Embodied Agent\" href=\"#3d-embodied-agent\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003ekeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-12-15\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboTracer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2512.13660\" rel=\"nofollow\"\u003eRoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboTracer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-04\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboRefer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.04308\" rel=\"nofollow\"\u003eRoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboRefer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-30\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVeBrain\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2506.00123v1\" rel=\"nofollow\"\u003eVisual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://internvl.github.io/blog/2025-05-26-VeBrain/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3DLLM-Mem\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCLA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2505.22657\" rel=\"nofollow\"\u003e3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://3dllm-mem.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-01-22\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialVLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDeepmind\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2401.12168\" rel=\"nofollow\"\u003eSpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://spatial-vlm.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-05\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNaviLLM\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCUHK\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2312.02010\" rel=\"nofollow\"\u003eTowards Learning a Generalist Model for Embodied Navigation\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/zd11024/NaviLLM\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDobb-E\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNYU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.16098.pdf\" rel=\"nofollow\"\u003eOn Bringing Robots Home\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/notmahi/dobb-e\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-26\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSTEVE\u003c/td\u003e\n\u003ctd align=\"center\"\u003eZJU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2311.15209\" rel=\"nofollow\"\u003eSee and Think: Embodied Agent in Virtual Environment\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/rese1f/STEVE\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-18\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLEO\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.12871.pdf\" rel=\"nofollow\"\u003eAn Embodied Generalist Agent in 3D World\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICML '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/embodied-generalist/embodied-generalist\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-9-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniHSI\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.07918.pdf\" rel=\"nofollow\"\u003eUnified Human-Scene Interaction via Prompted Chain-of-Contacts\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenRobotLab/UniHSI\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-7-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRT-2\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGoogle-DeepMind\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2307.15818.pdf\" rel=\"nofollow\"\u003eRT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://robotics-transformer2.github.io/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-7-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSayPlan\u003c/td\u003e\n\u003ctd align=\"center\"\u003eQUT Centre for Robotics\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2307.06135.pdf\" rel=\"nofollow\"\u003eSayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCoRL '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://sayplan.github.io/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-7-12\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVoxPoser\u003c/td\u003e\n\u003ctd align=\"center\"\u003eStanford\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://voxposer.github.io/voxposer.pdf\" rel=\"nofollow\"\u003eVoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/huangwl18/VoxPoser\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-12-13\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRT-1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGoogle\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://robotics-transformer1.github.io/assets/rt1.pdf\" rel=\"nofollow\"\u003eRT-1: Robotics Transformer for Real-World Control at Scale\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://robotics-transformer1.github.io/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-12-8\u003c/td\u003e\n\u003ctd align=\"center\"\u003eLLM-Planner\u003c/td\u003e\n\u003ctd align=\"center\"\u003eThe Ohio State University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2212.04088.pdf\" rel=\"nofollow\"\u003eLLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OSU-NLP-Group/LLM-Planner/\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-10-11\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCLIP-Fields\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNYU, Meta\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2210.05663.pdf\" rel=\"nofollow\"\u003eCLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRSS '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/notmahi/clip-fields\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-09-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNLMap-SayCan\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGoogle\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2209.09874\" rel=\"nofollow\"\u003eOpen-vocabulary Queryable Scene Representations for Real World Planning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICRA '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nlmap-saycan.github.io/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3D Benchmarks\u003c/h2\u003e\u003ca id=\"user-content-3d-benchmarks\" class=\"anchor\" aria-label=\"Permalink: 3D Benchmarks\" href=\"#3d-benchmarks\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDate\u003c/th\u003e\n\u003cth align=\"center\"\u003ekeywords\u003c/th\u003e\n\u003cth align=\"center\"\u003eInstitute\u003c/th\u003e\n\u003cth align=\"left\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003ePublication\u003c/th\u003e\n\u003cth align=\"center\"\u003eOthers\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2026-01-22\u003c/td\u003e\n\u003ctd align=\"center\"\u003eReal-3DQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVGG, Oxford\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://openreview.net/forum?id=3vlMiJwo8b\" rel=\"nofollow\"\u003eDo 3D Large Language Models Really Understand 3D Spatial Relationships?\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR 2026\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://real-3dqa.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-12-15\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboTracer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2512.13660\" rel=\"nofollow\"\u003eRoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboTracer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e:-----:\u003c/td\u003e\n\u003ctd align=\"center\"\u003e:------------------:\u003c/td\u003e\n\u003ctd align=\"center\"\u003e:--------------:\u003c/td\u003e\n\u003ctd align=\"left\"\u003e:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\u003c/td\u003e\n\u003ctd align=\"center\"\u003e:---------:\u003c/td\u003e\n\u003ctd align=\"center\"\u003e:---------:\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-11-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eORS3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2511.19430\" rel=\"nofollow\"\u003eCook and Clean Together: Teaching Embodied Agents for Parallel Task Execution\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eAAAI ‘26\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://h-embodvis.github.io/GRANT/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-04\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRoboRefer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBUAA\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.04308\" rel=\"nofollow\"\u003eRoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://zhoues.github.io/RoboRefer/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-09\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpaCE-10\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSJTU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.07966\" rel=\"nofollow\"\u003eSpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/VisionXLab/SpaCE-10\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHypo3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eImperial\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2502.00954\" rel=\"nofollow\"\u003eHypo3D: Exploring Hypothetical Reasoning in 3D\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICML'25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/MatchLab-Imperial/Hypo3D\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-06-04\u003c/td\u003e\n\u003ctd align=\"center\"\u003eAnywhere3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2506.04897\" rel=\"nofollow\"\u003eFrom Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://anywhere-3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-05-01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialVQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eJHU\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2505.00788\" rel=\"nofollow\"\u003eSpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR'25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"/jkhu29/Awesome-LLM-3D/blob/avl-branch\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-04-03\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSPAR\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFudan University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2503.22976\" rel=\"nofollow\"\u003eFrom Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://fudan-zvg.github.io/spar\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-03-28\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBeacon3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2503.22420\" rel=\"nofollow\"\u003eUnveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://beacon-3d.github.io\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2025-03-08\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-CoT\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePolyU, EIT\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2503.06232\" rel=\"nofollow\"\u003eIntegrating Chain-of-Thought for Multimodal Alignment: A Study on 3D Vision-Language Learning\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://huggingface.co/datasets/Battam/3D-CoT\" rel=\"nofollow\"\u003edataset\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-09-08\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMSQA / MSNN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2409.02389\" rel=\"nofollow\"\u003eMulti-modal Situated Reasoning in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://msr3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-08-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpace3D-Bench\u003c/td\u003e\n\u003ctd align=\"center\"\u003eETHz\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2408.16662\" rel=\"nofollow\"\u003eSpace3D-Bench: Spatial 3D Question Answering Benchmark\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://space3d-bench.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-07-24\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCity-3DQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHKUST\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2407.17398\" rel=\"nofollow\"\u003e3D Question Answering for City Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eACM MM'24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://sites.google.com/view/city3dqa/?pli=1\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-13\u003c/td\u003e\n\u003ctd align=\"center\"\u003eMMScan\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2406.09401\" rel=\"nofollow\"\u003eMMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenRobotLab/EmbodiedScan\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-10\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3D-GRAND / 3D-POPE\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUMich\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2406.05132.pdf\" rel=\"nofollow\"\u003e3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://3d-grand.github.io\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-06-03\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpatialRGPT-Bench\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUCSD\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2406.01584\" rel=\"nofollow\"\u003eSpatialRGPT: Grounded Spatial Reasoning in Vision Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNeurIPS '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AnjieCheng/SpatialRGPT\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-05-27\u003c/td\u003e\n\u003ctd align=\"center\"\u003eReason3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUC Merced\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2405.17427\" rel=\"nofollow\"\u003eReason3D: Searching and Reasoning 3D Segmentation via Large Language Model\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3DV'25\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://reason3d.github.io/\" rel=\"nofollow\"\u003eproject\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2024-1-18\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSceneVerse\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2401.09340.pdf\" rel=\"nofollow\"\u003eSceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/scene-verse/sceneverse\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-26\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEmbodiedScan\u003c/td\u003e\n\u003ctd align=\"center\"\u003eShanghai AI Lab\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2312.16170.pdf\" rel=\"nofollow\"\u003eEmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenRobotLab/EmbodiedScan\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-12-17\u003c/td\u003e\n\u003ctd align=\"center\"\u003eM3DBench\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFudan University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/abs/2312.10763\" rel=\"nofollow\"\u003eM3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eArxiv\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/OpenM3D/M3DBench\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-11-29\u003c/td\u003e\n\u003ctd align=\"center\"\u003e-\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDeepMind\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2311.17851.pdf\" rel=\"nofollow\"\u003eLeveraging VLM-Based Pipelines to Annotate 3D Objects\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICML '24\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/google-deepmind/objaverse_annotations\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2023-09-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCrossCoherence\u003c/td\u003e\n\u003ctd align=\"center\"\u003eUniBO\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2309.07917\" rel=\"nofollow\"\u003eLooking at words and points with attention: a benchmark for text-to-shape coherence\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICCV '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/AndreAmaduzzi/CrossCoherence\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-10-14\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSQA3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBIGAI\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2210.07474.pdf\" rel=\"nofollow\"\u003eSQA3D: Situated Question Answering in 3D Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eICLR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/SilongYong/SQA3D\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2022-09-24\u003c/td\u003e\n\u003ctd align=\"center\"\u003eFE-3DGQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eBeihang University\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2209.12028\" rel=\"nofollow\"\u003eTowards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTCSVT\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/zlccccc/3DVL_Codebase\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2021-12-20\u003c/td\u003e\n\u003ctd align=\"center\"\u003eScanQA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRIKEN AIP\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2112.10482.pdf\" rel=\"nofollow\"\u003eScanQA: 3D Question Answering for Spatial Scene Understanding\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '23\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ATR-DBI/ScanQA\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2020-12-3\u003c/td\u003e\n\u003ctd align=\"center\"\u003eScan2Cap\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2012.02206.pdf\" rel=\"nofollow\"\u003eScan2Cap: Context-aware Dense Captioning in RGB-D Scans\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCVPR '21\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/daveredrum/Scan2Cap\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2020-8-23\u003c/td\u003e\n\u003ctd align=\"center\"\u003eReferIt3D\u003c/td\u003e\n\u003ctd align=\"center\"\u003eStanford\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460409.pdf\" rel=\"nofollow\"\u003eReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV '20\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/referit3d/referit3d\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e2019-12-18\u003c/td\u003e\n\u003ctd align=\"center\"\u003eScanRefer\u003c/td\u003e\n\u003ctd align=\"center\"\u003eTUM\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://arxiv.org/pdf/2112.10482.pdf\" rel=\"nofollow\"\u003eScanRefer: 3D Object Localization in RGB-D Scans using Natural Language\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eECCV '20\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://daveredrum.github.io/ScanRefer/\" rel=\"nofollow\"\u003egithub\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYour contributions are always welcome!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eI will keep some pull requests open if I'm not sure if they are awesome for 3D LLMs, you could vote for them by adding 👍 to them.\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003eIf you have any questions about this opinionated list, please get in touch at \u003ca href=\"mailto:xianzheng@robots.ox.ac.uk\"\u003exianzheng@robots.ox.ac.uk\u003c/a\u003e or Wechat ID: mxz1997112.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStar History\u003c/h2\u003e\u003ca id=\"user-content-star-history\" class=\"anchor\" aria-label=\"Permalink: Star History\" href=\"#star-history\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://star-history.com/#ActiveVisionLab/Awesome-LLM-3D\u0026amp;Date\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff0ecc99ec768fe095656f1d397538212d46fa8ab2e5419fb0f086d1fcac0dc6/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d416374697665566973696f6e4c61622f417765736f6d652d4c4c4d2d334426747970653d44617465\" alt=\"Star History Chart\" data-canonical-src=\"https://api.star-history.com/svg?repos=ActiveVisionLab/Awesome-LLM-3D\u0026amp;type=Date\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you find this repository useful, please consider citing this paper:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{ma2024llmsstep3dworld,\n      title={When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models}, \n      author={Xianzheng Ma and Yash Bhalgat and Brandon Smart and Shuai Chen and Xinghui Li and Jian Ding and Jindong Gu and Dave Zhenyu Chen and Songyou Peng and Jia-Wang Bian and Philip H Torr and Marc Pollefeys and Matthias Nießner and Ian D Reid and Angel X. Chang and Iro Laina and Victor Adrian Prisacariu},\n      year={2024},\n      journal={arXiv preprint arXiv:2405.10255},\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{ma2024llmsstep3dworld,\n      title={When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models}, \n      author={Xianzheng Ma and Yash Bhalgat and Brandon Smart and Shuai Chen and Xinghui Li and Jian Ding and Jindong Gu and Dave Zhenyu Chen and Songyou Peng and Jia-Wang Bian and Philip H Torr and Marc Pollefeys and Matthias Nießner and Ian D Reid and Angel X. Chang and Iro Laina and Victor Adrian Prisacariu},\n      year={2024},\n      journal={arXiv preprint arXiv:2405.10255},\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgement\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003csvg data-component=\"Octicon\" class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repo is inspired by \u003ca href=\"https://github.com/Hannibal046/Awesome-LLM?tab=readme-ov-file#other-awesome-lists\"\u003eAwesome-LLM\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","richTextTruncated":false,"renderedFileInfo":null,"symbols":{"timed_out":false,"not_analyzed":false,"symbols":[{"name":"Awesome-LLM-3D [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [![PR's Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com)  \u003ca href=\"\" target='_blank'\u003e\u003cimg 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[![arXiv](https://img.shields.io/badge/arXiv-2405.10255v2-b31b1b.svg)](https://arxiv.org/abs/2405.10255v2)","kind":"section_1","ident_start":3,"ident_end":588,"extent_start":1,"extent_end":45555,"ident_utf16":{"start":{"line_number":1,"utf16_col":2},"end":{"line_number":1,"utf16_col":587}},"extent_utf16":{"start":{"line_number":1,"utf16_col":0},"end":{"line_number":247,"utf16_col":0}}},{"name":"🏠 About","fully_qualified_name":"🏠 About","kind":"section_2","ident_start":682,"ident_end":692,"extent_start":679,"extent_end":1150,"ident_utf16":{"start":{"line_number":9,"utf16_col":3},"end":{"line_number":9,"utf16_col":11}},"extent_utf16":{"start":{"line_number":9,"utf16_col":0},"end":{"line_number":15,"utf16_col":0}}},{"name":"🔥 News","fully_qualified_name":"🔥 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Awesome-LLM-3D [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [![PR's Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com)  \u003ca href=\"\" target='_blank'\u003e\u003cimg src=\"https://visitor-badge.laobi.icu/badge?page_id=activevisionlab.llm3d\u0026left_color=gray\u0026right_color=blue\"\u003e \u003c/a\u003e [![arXiv](https://img.shields.io/badge/arXiv-2405.10255v2-b31b1b.svg)](https://arxiv.org/abs/2405.10255v2)","","\u003cdiv align=\"center\"\u003e","    \u003cimg src=\"assets/Figure1_v8_25.6.9.png\" width=\"100%\"\u003e","\u003c/div\u003e","","","","## 🏠 About","Here is a curated list of papers about 3D-Related Tasks empowered by Large Language Models (LLMs). ","It contains various tasks including 3D understanding, reasoning, generation, and embodied agents. Also, we include other Foundation Models (CLIP, SAM) for the whole picture of this area.","","This is an active repository, you can watch for following the latest advances. If you find it useful, please kindly star ⭐ this repo and [cite](#citation) the paper.","","## 🔥 News","- [2026-03-20] Our benchmark paper **Real-3DQA** is now available at ICLR 2026! Following our survey paper, we now release the benchmark paper on genuine 3D spatial understanding. [Project Page](https://real-3dqa.github.io/)","- [2025-10-21] 📢 We have released the **second version** of our survey, updated to include literature up to **July 2025**:  ","👉 [*When LLMs Step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models*](https://arxiv.org/pdf/2405.10255v2)","- [2024-05-16] Check out the first survey paper in the 3D-LLM domain: [When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models](https://arxiv.org/pdf/2405.10255) ","- [2024-01-06] [Runsen Xu](https://runsenxu.com/) added chronological information and [Xianzheng Ma](https://xianzhengma.github.io/) reorganized it in Z-A order for better following the latest advances.","- [2023-12-16] [Xianzheng Ma](https://xianzhengma.github.io/) and [Yash Bhalgat](https://yashbhalgat.github.io/) curated this list and published the first version;","","## Table of Contents","","- [Awesome-LLM-3D](#awesome-llm-3D)","  - [3D Unified Understanding and Generation (LLM)](#3d-unified-understanding-and-generation-via-llm)","  - [3D Understanding (LLM)](#3d-understanding-via-llm)","  - [3D Understanding (other Foundation Models)](#3d-understanding-via-other-foundation-models)","  - [3D Reasoning](#3d-reasoning)","  - [3D Generation](#3d-generation)","  - [3D Embodied Agent](#3d-embodied-agent)","  - [3D Benchmarks](#3d-benchmarks)","  - [Contributing](#contributing)","","","## 3D Unified Understanding and Generation via LLM","","|  Date |       Keywords       |    Institute (first)   | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------:","| 2025-11-07 | Omni-View | PKU | [Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images](https://arxiv.org/abs/2511.07222) | ICLR 2026 | [github](https://github.com/AIDC-AI/Omni-View) |","| 2025-08-16 | UniUGG | FDU | [UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding](https://arxiv.org/abs/2508.11952) | ICLR 2026 | [github](https://github.com/fudan-zvg/UniUGG) |","","## 3D Understanding via LLM","","|  Date |       Keywords       |    Institute (first)   | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------:","| 2025-11-27 | G\u003csup\u003e2\u003c/sup\u003eVLM | Shanghai AI Lab | [G\u003csup\u003e2\u003c/sup\u003eVLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning](https://arxiv.org/pdf/2511.21688) | Arxiv | [github](https://github.com/InternRobotics/G2VLM) |","| 2025-11-07 | Omni-View | PKU | [Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images](https://arxiv.org/abs/2511.07222) | ICLR 2026 | [github](https://github.com/AIDC-AI/Omni-View) |","| 2025-08-16 | UniUGG | FDU | [UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding](https://arxiv.org/abs/2508.11952) | ICLR 2026 | [github](https://github.com/fudan-zvg/UniUGG) |","| 2025-07-31 | 3D-R1 | PKU | [3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding](https://arxiv.org/abs/2507.23478) | Arxiv | [project](https://aigeeksgroup.github.io/3D-R1/) |","| 2025-06-11 | LEO-VL | BIGAI | [LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning](https://arxiv.org/abs/2506.09935) | Arxiv | [project](https://leo-vl.github.io/) |","| 2025-06-02 | 3DRS | HKU | [MLLMs Need 3D-Aware Representation Supervision for Scene Understanding](https://arxiv.org/html/2506.01946v1) | Arxiv | [project](https://visual-ai.github.io/3drs/) |","| 2025-05-30 | VG LLM | CUHK | [Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors](https://arxiv.org/abs/2505.24625) | Arxiv | [project](https://lavi-lab.github.io/VG-LLM/) |","| 2025-05-29 | Spatial-MLLM | THU | [Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence](https://arxiv.org/pdf/2505.23747) | Arxiv | [project](https://diankun-wu.github.io/Spatial-MLLM/) |","| 2025-05-28 | SeeGround | HKUST(GZ) | [Zero-Shot 3D Visual Grounding from Vision-Language Models](https://arxiv.org/abs/2505.22429) | CVPRW'25 | [project](https://seeground.github.io) |","| 2025-05-28 | 3DLLM-Mem | UCLA/Google | [3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model](https://arxiv.org/abs/2505.22657) | NeurIPS'25 | [project](https://3dllm-mem.github.io/) |","| 2025-04-24 | 3D-LLaVA | U of Adelaide | [3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer](https://arxiv.org/pdf/2501.01163) | CVPR '25 | [github](https://github.com/djiajunustc/3D-LLaVA) |","| 2025-04-03 | Ross3D | CASIA| [Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness](https://arxiv.org/pdf/2504.01901) | Arxiv | [project](https://haochen-wang409.github.io/ross3d/) |","| 2025-03-08 | SplatTalk | GIT| [SplatTalk: 3D VQA with Gaussian Splatting](https://arxiv.org/pdf/2503.06271) | Arxiv | [github]() |","| 2025-03-01 | Inst3D-LMM | ZJU| [Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning](https://arxiv.org/abs/2503.00513) | CVPR '25 | [github](https://github.com/hanxunyu/Inst3D-LMM) |","| 2025-02-13 | ENEL | SH AILab | [ENEL: Exploring the Potential of Encoder-free Architectures in 3D LMMs](https://arxiv.org/pdf/2502.09620v1) | Arxiv | [project](https://github.com/Ivan-Tang-3D/ENEL/tree/main?tab=readme-ov-file) |","| 2025-02-02 | LSceneLLM | SCUT| [LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences](https://arxiv.org/pdf/2412.01292) | CVPR '25 | [project](https://github.com/Hoyyyaard/LSceneLLM) |","| 2025-01-02 | GPT4Scene | HKU | [GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models](https://arxiv.org/pdf/2501.01428) | Arxiv | [project](https://gpt4scene.github.io/) |","| 2024-12-05 | SeeGround | HKUST(GZ) | [SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding](https://arxiv.org/abs/2412.04383) | CVPR '25 | [project](https://seeground.github.io) |","| 2024-12-03 | Video-3D LLM | CUHK | [Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding](https://arxiv.org/abs/2412.00493) | CVPR '25 | [project](https://github.com/LaVi-Lab/Video-3D-LLM) |","| 2024-11-29 | PerLA | Fondazione Bruno Kessler | [PerLA: Perceptive 3D Language Assistant](https://arxiv.org/abs/2411.19774) | CVPR '25 | [project](https://gfmei.github.io/PerLA/) |","| 2024-10-12 | Situation3D | UIUC | [Situational Awareness Matters in 3D Vision Language Reasoning](https://arxiv.org/abs/2406.07544) | CVPR '24 | [project](https://yunzeman.github.io/situation3d/) |","| 2024-09-30 | Robin3D | HKU | [Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning](https://arxiv.org/abs/2410.00255) | ICCV '25 | [github](https://github.com/WeitaiKang/Robin3D) |","| 2024-09-28 | LLaVA-3D | HKU | [LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness](https://arxiv.org/pdf/2409.18125) | Arxiv | [project](https://zcmax.github.io/projects/LLaVA-3D/) |","| 2024-09-08 | MSR3D | BIGAI | [Multi-modal Situated Reasoning in 3D Scenes](https://arxiv.org/abs/2409.02389) | NeurIPS '24| [project](https://msr3d.github.io/) |","| 2024-08-28 | GreenPLM | HUST | [ More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding]( https://arxiv.org/pdf/2408.15966) | Arxiv| [github](https://github.com/TangYuan96/GreenPLM) |","| 2024-06-17 | LLaNA | UniBO | [LLaNA: Large Language and NeRF Assistant](https://arxiv.org/pdf/2406.11840)| NeurIPS '24 | [project](https://andreamaduzzi.github.io/llana/) |","| 2024-06-07  | SpatialPIN           | Oxford                 | [SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors](https://arxiv.org/abs/2403.13438)                         | NeurIPS '24       | [project](https://dannymcy.github.io/zeroshot_task_hallucination/) |","| 2024-06-03 | SpatialRGPT | UCSD | [SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models](https://arxiv.org/abs/2406.01584) | NeurIPS '24 | [github](https://github.com/AnjieCheng/SpatialRGPT) |","| 2024-05-02 | MiniGPT-3D | HUST| [MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors](https://arxiv.org/pdf/2405.01413)| ACM MM '24 | [project](https://tangyuan96.github.io/minigpt_3d_project_page/) |","| 2024-02-27 |  ShapeLLM |    XJTU  | [ShapeLLM: Universal 3D Object Understanding for Embodied Interaction](https://arxiv.org/pdf/2402.17766)                                                                                | Arxiv | [project](https://qizekun.github.io/shapellm/) |","| 2024-01-22  | SpatialVLM           | Google DeepMind        | [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://arxiv.org/abs/2401.12168)                                                                  | CVPR '24    | [project](https://spatial-vlm.github.io/) |","| 2023-12-21 |  LiDAR-LLM |    PKU  | [LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding](https://arxiv.org/pdf/2312.14074.pdf)                                                                                | Arxiv | [project](https://sites.google.com/view/lidar-llm) |","| 2023-12-15 |  3DAP |    Shanghai AI Lab  | [3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V](https://arxiv.org/pdf/2312.09738.pdf)                                                                                | Arxiv | [project]() |","| 2023-12-13 |  Chat-Scene |    ZJU | [Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers](https://arxiv.org/pdf/2312.08168.pdf)                                                                                | NeurIPS '24 | [github](https://github.com/ZzZZCHS/Chat-Scene) |","| 2023-12-5 | GPT4Point | HKU | [GPT4Point: A Unified Framework for Point-Language Understanding and Generation](https://arxiv.org/pdf/2312.02980.pdf) |Arxiv |  [github](https://github.com/Pointcept/GPT4Point) |","| 2023-11-30 |         LL3DA        |     Fudan University    | [LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning](https://arxiv.org/pdf/2311.18651.pdf)                                                                                                  |Arxiv|  [github](https://github.com/Open3DA/LL3DA) |","| 2023-11-26 | ZSVG3D | CUHK(SZ) | [Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding](https://arxiv.org/pdf/2311.15383.pdf) | Arxiv | [project](https://curryyuan.github.io/ZSVG3D/) | Arxiv | ","| 2023-11-18 |          LEO          |      BIGAI      | [An Embodied Generalist Agent in 3D World](https://arxiv.org/pdf/2311.12871.pdf)                                                           |    ICML '24  |  [github](https://github.com/embodied-generalist/embodied-generalist) |","| 2023-10-14 | JM3D-LLM | Xiamen University | [JM3D \u0026 JM3D-LLM: Elevating 3D Representation with Joint Multi-modal Cues](https://arxiv.org/pdf/2310.09503v2.pdf)                                        | ACM MM '23 |  [github](https://github.com/mr-neko/jm3d) |","| 2023-10-10 |  Uni3D |    BAAI  | [Uni3D: Exploring Unified 3D Representation at Scale](https://arxiv.org/abs/2310.06773)                                                                                | ICLR '24 | [project](https://github.com/baaivision/Uni3D) |","| 2023-9-27 |  - |    KAUST  | [Zero-Shot 3D Shape Correspondence](https://arxiv.org/abs/2306.03253)                                                                                | Siggraph Asia '23 | - |","| 2023-9-21|       LLM-Grounder       |      U-Mich      | [LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent](https://arxiv.org/pdf/2309.12311.pdf)              |     ICRA '24     |  [github](https://github.com/sled-group/chat-with-nerf) |","| 2023-9-1 |        Point-Bind       |      CUHK     | [Point-Bind \u0026 Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following](https://arxiv.org/pdf/2309.00615.pdf)             |  Arxiv   |  [github](https://github.com/ZiyuGuo99/Point-Bind_Point-LLM) |","| 2023-8-31 |         PointLLM         |      CUHK      | [PointLLM: Empowering Large Language Models to Understand Point Clouds](https://arxiv.org/pdf/2308.16911.pdf)                                                                             |   ECCV '24 |  [github](https://github.com/OpenRobotLab/PointLLM) |","| 2023-8-17|     Chat-3D     |      ZJU     | [Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes](https://arxiv.org/pdf/2308.08769v1.pdf)                                                          |  Arxiv      |  [github](https://github.com/Chat-3D/Chat-3D)|","| 2023-8-8 |         3D-VisTA          |      BIGAI      | [3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment](https://Arxiv.org/abs/2308.04352)                                                           |    ICCV '23  | [github]() |","| 2023-7-24 |     3D-LLM     |      UCLA    | [3D-LLM: Injecting the 3D World into Large Language Models](https://arxiv.org/pdf/2307.12981.pdf)                                                                                                                      |   NeurIPS '23|  [github](https://github.com/UMass-Foundation-Model/3D-LLM) |","| 2023-3-29 |       ViewRefer       |      CUHK      | [ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding](https://arxiv.org/pdf/2303.16894.pdf)                                                                                               |ICCV '23 |[github](https://github.com/Ivan-Tang-3D/ViewRefer3D) |","| 2022-9-12 |        -        |      MIT      | [Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding](https://arxiv.org/pdf/2209.05629.pdf)                                                                      |Arxiv|  [github](https://github.com/MIT-SPARK/llm_scene_understanding) |","","","## 3D Understanding via other Foundation Models","|  ID |       keywords       |    Institute (first)    | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: ","| 2025-11-20 | POMA-3D | Imperial | [POMA-3D: The Point Map Way to 3D Scene Understanding](https://arxiv.org/abs/2511.16567) | Arxiv | [project](https://matchlab-imperial.github.io/poma3d/) |","| 2025-07-26 |  OV-3DDet |    HKUST  | [CoDAv2: Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection](https://arxiv.org/abs/2406.00830)                                                                                | TPAMI '25 | [github](https://github.com/yangcaoai/CoDA_NeurIPS2023) |","| 2025-02-20 |  CrossOver |  Stanford | [CrossOver: 3D Scene Cross-Modal Alignment](https://arxiv.org/abs/2502.15011) | CVPR '25 | [project](https://sayands.github.io/crossover/) |","| 2025-02-05 |  SAGA |  SJTU | [Segment Any 3D Gaussians](https://arxiv.org/abs/2312.00860) | AAAI '25 | [project](https://jumpat.github.io/SAGA/) |","| 2024-10-12 |  Lexicon3D |    UIUC  | [Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding](https://arxiv.org/abs/2409.03757) | NeurIPS '24 | [project](https://yunzeman.github.io/lexicon3d/) |","| 2024-10-07 |  Diff2Scene |    CMU  | [Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models](https://arxiv.org/pdf/2407.13642) | ECCV'24 | [project](https://diff2scene.github.io/) |","| 2024-07-19 |  OpenSU3D |  TUM  | [OpenSU3D: Open World 3D Scene Understanding using Foundation Models](https://arxiv.org/abs/2409.03757) | ICRA '25 | [project](https://opensu3d.github.io/) |","| 2024-04-07 |  Any2Point |    Shanghai AI Lab  | [Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding](https://arxiv.org/pdf/2404.07989) | ECCV'24 | [github](https://github.com/Ivan-Tang-3D/Any2Point) |","| 2024-03-16 |  N2F2 |    Oxford-VGG  | [N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields](https://arxiv.org/pdf/2403.10997.pdf) | Arxiv | - |","| 2023-12-17 |  SAI3D |    PKU  | [SAI3D: Segment Any Instance in 3D Scenes](https://arxiv.org/pdf/2312.11557.pdf)                                                                                | Arxiv | [project](https://yd-yin.github.io/SAI3D) |","| 2023-12-17 |  Open3DIS |    VinAI  | [Open3DIS: Open-vocabulary 3D Instance Segmentation with 2D Mask Guidance](https://arxiv.org/pdf/2312.10671.pdf)                                                                                | Arxiv | [project](https://open3dis.github.io/) |","| 2023-11-6 |  OVIR-3D |    Rutgers University  | [OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data](https://arxiv.org/pdf/2311.02873.pdf) | CoRL '23 | [github](https://github.com/shiyoung77/OVIR-3D/) |","| 2023-10-29|  OpenMask3D |    ETH  | [OpenMask3D: Open-Vocabulary 3D Instance Segmentation](https://openmask3d.github.io/static/pdf/openmask3d.pdf)                                                                                | NeurIPS '23 | [project](https://openmask3d.github.io/) |","| 2023-10-5 |     Open-Fusion     |      -     | [Open-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation](https://arxiv.org/pdf/2310.03923.pdf)                                                                            |Arxiv|  [github](https://github.com/UARK-AICV/OpenFusion) |","| 2023-9-22 |  OV-3DDet |    HKUST  | [CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection](https://arxiv.org/pdf/2310.02960.pdf)                                                                                | NeurIPS '23 | [github](https://github.com/yangcaoai/CoDA_NeurIPS2023) |","| 2023-9-19 | LAMP |      -      | [From Language to 3D Worlds: Adapting Language Model for Point Cloud Perception](https://openreview.net/forum?id=H49g8rRIiF)                                                              |    OpenReview     | - |","| 2023-9-15 |  OpenNerf |    -    | [OpenNerf: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views](https://openreview.net/pdf?id=SgjAojPKb3)                                                                                | OpenReview | [github]() |","| 2023-9-1|  OpenIns3D |    Cambridge  | [OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation](https://arxiv.org/pdf/2309.00616.pdf)                                                                                | Arxiv | [project](https://zheninghuang.github.io/OpenIns3D/) |","| 2023-6-7 |         Contrastive Lift         |     Oxford-VGG     | [Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion](https://arxiv.org/pdf/2306.04633.pdf)                                                                                        |   NeurIPS '23| [github](https://github.com/yashbhalgat/Contrastive-Lift) |","| 2023-6-4 |  Multi-CLIP |    ETH  | [Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes](https://arxiv.org/pdf/2306.02329.pdf)                                                                                | Arxiv | - |","| 2023-5-23 |  3D-OVS |    NTU  | [Weakly Supervised 3D Open-vocabulary Segmentation](https://arxiv.org/pdf/2305.14093.pdf)                                                                                | NeurIPS '23 | [github](https://github.com/Kunhao-Liu/3D-OVS) |","| 2023-5-21 |  VL-Fields |    University of Edinburgh  | [VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations](https://arxiv.org/pdf/2305.12427.pdf)                                                                                | ICRA '23 | [project](https://tsagkas.github.io/vl-fields/)  |","| 2023-5-8 |  CLIP-FO3D |    Tsinghua University  | [CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP](https://arxiv.org/pdf/2303.04748.pdf)                                                                                | ICCVW '23 | - |","| 2023-4-12 |  3D-VQA |    ETH  | [CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes](https://arxiv.org/pdf/2304.06061.pdf)                                                                                | CVPRW '23 | [github](https://github.com/AlexDelitzas/3D-VQA) |","| 2023-4-3 |  RegionPLC |    HKU | [RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding](https://arxiv.org/pdf/2304.00962.pdf)                                                                                | Arxiv | [project](https://jihanyang.github.io/projects/RegionPLC) |","| 2023-3-20 |        CG3D        |      JHU      | [CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition](https://arxiv.org/pdf/2303.11313.pdf)                                                                                                 |Arxiv|  [github](https://github.com/deeptibhegde/CLIP-goes-3D) |","| 2023-3-16 | LERF |     UC Berkeley     | [LERF: Language Embedded Radiance Fields](https://arxiv.org/pdf/2303.09553.pdf)                                                   | ICCV '23   | [github](https://github.com/kerrj/lerf) |","| 2023-2-14 |  ConceptFusion |    MIT  | [ConceptFusion: Open-set Multimodal 3D Mapping](https://arxiv.org/pdf/2302.07241.pdf)                                                                                | RSS '23 | [project](https://concept-fusion.github.io/) |","| 2023-1-12 |         CLIP2Scene         |      HKU      | [CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP](https://arxiv.org/pdf/2301.04926.pdf)                                                                                        |    CVPR '23 | [github](https://github.com/runnanchen/CLIP2Scene) |","| 2022-12-1 |         UniT3D         |      TUM     | [UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding](https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_UniT3D_A_Unified_Transformer_for_3D_Dense_Captioning_and_Visual_ICCV_2023_paper.pdf)                                                                          |   ICCV '23| [github]() |","| 2022-11-29 |        PLA        |     HKU    | [PLA: Language-Driven Open-Vocabulary 3D Scene Understanding](https://arxiv.org/pdf/2211.16312.pdf)                                                                 |CVPR '23|  [github](https://github.com/CVMI-Lab/PLA) |","| 2022-11-28 |       OpenScene       |      ETHz      | [OpenScene: 3D Scene Understanding with Open Vocabularies](https://arxiv.org/pdf/2211.15654.pdf)                                                             |   CVPR '23  | [github](https://github.com/pengsongyou/openscene) |","| 2022-10-11 |  CLIP-Fields |    NYU  | [CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory](https://arxiv.org/pdf/2210.05663.pdf)                                                                                | Arxiv | [project](https://mahis.life/clip-fields/) |","| 2022-7-23 |  Semantic Abstraction |    Columbia  | [Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models](https://arxiv.org/pdf/2207.11514.pdf)                                                                                | CoRL '22 | [project](https://semantic-abstraction.cs.columbia.edu/) |","| 2022-4-26 |   ScanNet200 |    TUM  | [Language-Grounded Indoor 3D Semantic Segmentation in the Wild](https://arxiv.org/pdf/2204.07761.pdf)                                                                                | ECCV '22 | [project](https://rozdavid.github.io/scannet200) |","","","","","## 3D Reasoning","|  Date |       keywords       |    Institute (first)    | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: |","| 2025-12-15 | RoboTracer | BUAA | [RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2512.13660) | Arxiv | [project](https://zhoues.github.io/RoboTracer/) |","| 2025-06-11 | SceneCOT | BIGAI | [SceneCOT: Eliciting Chain-of-Thought Reasoning in 3D Scenes](https://scenecot.github.io/) | Arxiv | [project](https://scenecot.github.io/) |","| 2025-06-04 | RoboRefer | BUAA | [RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2506.04308) | Arxiv | [project](https://zhoues.github.io/RoboRefer/) |","| 2024-09-08 | MSR3D | BIGAI | [Multi-modal Situated Reasoning in 3D Scenes](https://arxiv.org/abs/2409.02389) | NeurIPS '24| [project](https://msr3d.github.io/) |","| 2023-5-20|       3D-CLR      |      UCLA     | [3D Concept Learning and Reasoning from Multi-View Images](https://arxiv.org/pdf/2303.11327.pdf)                                                 |   CVPR '23  | [github](https://github.com/evelinehong/3D-CLR-Official) |","| - |         Transcribe3D         |     TTI, Chicago     | [Transcribe3D: Grounding LLMs Using Transcribed Information for 3D Referential Reasoning with Self-Corrected Finetuning](https://openreview.net/pdf?id=7j3sdUZMTF)                                                                                                  |CoRL '23|  [github]() |","","","## 3D Generation","|  Date |       keywords       |    Institute    | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: ","| 2025-11-07 | Omni-View | PKU | [Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images](https://arxiv.org/abs/2511.07222) | ICLR 2026 | [github](https://github.com/AIDC-AI/Omni-View) |","| 2025-08-16 | UniUGG | FDU | [UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding](https://arxiv.org/abs/2508.11952) | ICLR 2026 | [github](https://github.com/fudan-zvg/UniUGG) |","| 2024-11-14 |         LLaMA-Mesh         |     THU     | [LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models](https://arxiv.org/html/2411.09595v1)                                                                                                  |Arxiv|  [project](https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/) |  ","| 2023-11-29 |         ShapeGPT         |     Fudan University     | [ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model](https://arxiv.org/pdf/2311.17618.pdf)                                                                                                  |Arxiv|  [github](https://github.com/OpenShapeLab/ShapeGPT) |                                                                                              | Arxiv  |  [github]() |","| 2023-11-27|         MeshGPT         |     TUM     | [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://arxiv.org/pdf/2311.15475.pdf)                                                                                                  |Arxiv |  [project](https://nihalsid.github.io/mesh-gpt/) |","| 2023-10-19 |         3D-GPT        |     ANU   | [3D-GPT: Procedural 3D Modeling with Large Language Models](https://arxiv.org/pdf/2310.12945.pdf)                                                                                                   |Arxiv|  [github](https://dreamllm.github.io/) |","| 2023-9-21 |         LLMR         |     MIT     | [LLMR: Real-time Prompting of Interactive Worlds using Large Language Models](https://arxiv.org/pdf/2309.12276.pdf)                                                                                                  |Arxiv| - |","| 2023-9-20 |         DreamLLM         |     MEGVII    | [DreamLLM: Synergistic Multimodal Comprehension and Creation](https://arxiv.org/pdf/2309.11499.pdf) | Arxiv | [github](https://github.com/RunpeiDong/DreamLLM)","| 2023-4-1 |      ChatAvatar      |       Deemos Tech            | [DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance](https://dl.acm.org/doi/abs/10.1145/3592094)                                               |  ACM TOG    | [website](https://hyperhuman.deemos.com/) |","","## 3D Embodied Agent","|  Date |       keywords       |    Institute   | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: |","| 2025-12-15 | RoboTracer | BUAA | [RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2512.13660) | Arxiv | [project](https://zhoues.github.io/RoboTracer/) |","| 2025-06-04 | RoboRefer | BUAA | [RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2506.04308) | Arxiv | [project](https://zhoues.github.io/RoboRefer/) |","| 2025-05-30 | VeBrain | Shanghai AI Lab | [Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces](https://arxiv.org/pdf/2506.00123v1) | Arxiv | [project](https://internvl.github.io/blog/2025-05-26-VeBrain/) |","| 2025-05-28 | 3DLLM-Mem | UCLA | [3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model](https://arxiv.org/abs/2505.22657) | Arxiv | [project](https://3dllm-mem.github.io/) |","| 2024-01-22 |  SpatialVLM |    Deepmind  | [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://arxiv.org/abs/2401.12168)                                                                                | CVPR '24 | [project](https://spatial-vlm.github.io/) |","| 2023-12-05 | NaviLLM | CUHK | [Towards Learning a Generalist Model for Embodied Navigation](https://arxiv.org/abs/2312.02010) | CVPR '24 | [project](https://github.com/zd11024/NaviLLM) |","| 2023-11-27 | Dobb-E | NYU | [On Bringing Robots Home](https://arxiv.org/pdf/2311.16098.pdf)        |    Arxiv  |  [github](https://github.com/notmahi/dobb-e) |","| 2023-11-26 | STEVE | ZJU | [See and Think: Embodied Agent in Virtual Environment](https://arxiv.org/abs/2311.15209) | Arxiv | [github](https://github.com/rese1f/STEVE) |","| 2023-11-18 | LEO  |   BIGAI  | [An Embodied Generalist Agent in 3D World](https://arxiv.org/pdf/2311.12871.pdf)   |    ICML '24  |  [github](https://github.com/embodied-generalist/embodied-generalist) |","| 2023-9-14 |        UniHSI      |      Shanghai AI Lab     | [Unified Human-Scene Interaction via Prompted Chain-of-Contacts](https://arxiv.org/pdf/2309.07918.pdf)                                                                                                 |   Arxiv |  [github](https://github.com/OpenRobotLab/UniHSI) |","| 2023-7-28 |         RT-2         |     Google-DeepMind     | [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://arxiv.org/pdf/2307.15818.pdf)                                                                                                  |Arxiv|  [github](https://robotics-transformer2.github.io/) |","| 2023-7-12 |         SayPlan        |     QUT Centre for Robotics    | [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://arxiv.org/pdf/2307.06135.pdf)                                                                                                  |CoRL '23|  [github](https://sayplan.github.io/) |","| 2023-7-12 |          VoxPoser          |      Stanford      | [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://voxposer.github.io/voxposer.pdf)                                                           |    Arxiv  |  [github](https://github.com/huangwl18/VoxPoser) |","| 2022-12-13|         RT-1         |     Google     | [RT-1: Robotics Transformer for Real-World Control at Scale](https://robotics-transformer1.github.io/assets/rt1.pdf)                                                                                                  |Arxiv|  [github](https://robotics-transformer1.github.io/) |","| 2022-12-8 |         LLM-Planner         |     The Ohio State University    | [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://arxiv.org/pdf/2212.04088.pdf)                                                                                                  |ICCV '23|  [github](https://github.com/OSU-NLP-Group/LLM-Planner/) |","| 2022-10-11 |          CLIP-Fields          |      NYU, Meta      | [CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory](https://arxiv.org/pdf/2210.05663.pdf)                                                           |    RSS '23  |  [github](https://github.com/notmahi/clip-fields) |","| 2022-09-20|       NLMap-SayCan       |     Google     | [Open-vocabulary Queryable Scene Representations for Real World Planning](https://arxiv.org/abs/2209.09874)                                                                    | ICRA '23|  [github](https://nlmap-saycan.github.io/) |","","## 3D Benchmarks","|  Date |       keywords       |    Institute    | Paper                                                                                                                                                                               | Publication | Others |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: |","| 2026-01-22 | Real-3DQA | VGG, Oxford | [Do 3D Large Language Models Really Understand 3D Spatial Relationships?](https://openreview.net/forum?id=3vlMiJwo8b) | ICLR 2026 | [project](https://real-3dqa.github.io/) |","| 2025-12-15 | RoboTracer | BUAA | [RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2512.13660) | Arxiv | [project](https://zhoues.github.io/RoboTracer/) |","| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: | :---------: ","| 2025-11-28 | ORS3D | HUST | [Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution](https://arxiv.org/abs/2511.19430) | AAAI ‘26 | [project](https://h-embodvis.github.io/GRANT/) |","| 2025-06-04 | RoboRefer | BUAA | [RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics](https://arxiv.org/abs/2506.04308) | Arxiv | [project](https://zhoues.github.io/RoboRefer/) |","| 2025-06-09 | SpaCE-10 | SJTU | [SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence](https://arxiv.org/abs/2506.07966) | Arxiv | [project](https://github.com/VisionXLab/SpaCE-10) |","| 2025-05-01 | Hypo3D | Imperial | [Hypo3D: Exploring Hypothetical Reasoning in 3D](https://arxiv.org/abs/2502.00954) | ICML'25| [project](https://github.com/MatchLab-Imperial/Hypo3D) |","| 2025-06-04 | Anywhere3D | BIGAI | [From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes](https://arxiv.org/abs/2506.04897) | NeurIPS '25 | [project](https://anywhere-3d.github.io/) |","| 2025-05-01 | SpatialVQA | JHU | [SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models](https://arxiv.org/abs/2505.00788) | CVPR'25| [project]() |","| 2025-04-03 | SPAR | Fudan University | [From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D](https://arxiv.org/abs/2503.22976) | Arxiv| [project](https://fudan-zvg.github.io/spar) |","| 2025-03-28 | Beacon3D | BIGAI | [Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis](https://arxiv.org/abs/2503.22420) | CVPR '25| [project](https://beacon-3d.github.io) |","| 2025-03-08 | 3D-CoT | PolyU, EIT | [Integrating Chain-of-Thought for Multimodal Alignment: A Study on 3D Vision-Language Learning](https://arxiv.org/abs/2503.06232) | Arxiv | [dataset](https://huggingface.co/datasets/Battam/3D-CoT) |","| 2024-09-08 | MSQA / MSNN | BIGAI | [Multi-modal Situated Reasoning in 3D Scenes](https://arxiv.org/abs/2409.02389) | NeurIPS '24| [project](https://msr3d.github.io/) |","| 2024-08-29 | Space3D-Bench | ETHz | [Space3D-Bench: Spatial 3D Question Answering Benchmark](https://arxiv.org/abs/2408.16662) | Arxiv | [project](https://space3d-bench.github.io/) |","| 2024-07-24 | City-3DQA | HKUST | [3D Question Answering for City Scene Understanding](https://arxiv.org/pdf/2407.17398) | ACM MM'24 | [project](https://sites.google.com/view/city3dqa/?pli=1) |","| 2024-06-13 | MMScan | Shanghai AI Lab | [MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations](https://arxiv.org/pdf/2406.09401) | Arxiv | [github](https://github.com/OpenRobotLab/EmbodiedScan) |","| 2024-06-10 | 3D-GRAND / 3D-POPE | UMich | [3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination](https://arxiv.org/pdf/2406.05132.pdf) | Arxiv | [project](https://3d-grand.github.io) |","| 2024-06-03 | SpatialRGPT-Bench | UCSD | [SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models](https://arxiv.org/abs/2406.01584) | NeurIPS '24 | [github](https://github.com/AnjieCheng/SpatialRGPT) |","| 2024-05-27 | Reason3D | UC Merced | [Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model](https://arxiv.org/abs/2405.17427) | 3DV'25 | [project](https://reason3d.github.io/) |","| 2024-1-18 | SceneVerse | BIGAI | [SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding](https://arxiv.org/pdf/2401.09340.pdf) | ECCV '24 | [github](https://github.com/scene-verse/sceneverse) |","| 2023-12-26 | EmbodiedScan | Shanghai AI Lab | [EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI](https://arxiv.org/pdf/2312.16170.pdf) | Arxiv | [github](https://github.com/OpenRobotLab/EmbodiedScan) |","| 2023-12-17 |         M3DBench        |     Fudan University     | [M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts](https://arxiv.org/abs/2312.10763)                                                                                                  |Arxiv|  [github](https://github.com/OpenM3D/M3DBench) |","| 2023-11-29 |         -         |     DeepMind  | [Leveraging VLM-Based Pipelines to Annotate 3D Objects](https://arxiv.org/pdf/2311.17851.pdf)                                                                                                  |ICML '24|  [github](https://github.com/google-deepmind/objaverse_annotations) |","| 2023-09-14 |CrossCoherence   |     UniBO  | [Looking at words and points with attention: a benchmark for text-to-shape coherence](https://arxiv.org/pdf/2309.07917)                                                                                                  |ICCV '23|  [github](https://github.com/AndreAmaduzzi/CrossCoherence) |","| 2022-10-14 |     SQA3D     |      BIGAI    | [SQA3D: Situated Question Answering in 3D Scenes](https://arxiv.org/pdf/2210.07474.pdf)                                                                                                        | ICLR '23| [github](https://github.com/SilongYong/SQA3D) |","| 2022-09-24 |     FE-3DGQA     |      Beihang University    | [Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline](https://arxiv.org/pdf/2209.12028)                                                                                                        | TCSVT | [github](https://github.com/zlccccc/3DVL_Codebase) |","| 2021-12-20|     ScanQA     |      RIKEN AIP    | [ScanQA: 3D Question Answering for Spatial Scene Understanding](https://arxiv.org/pdf/2112.10482.pdf)                                                                                                        | CVPR '23| [github](https://github.com/ATR-DBI/ScanQA) |","| 2020-12-3 |     Scan2Cap     |      TUM    | [Scan2Cap: Context-aware Dense Captioning in RGB-D Scans](https://arxiv.org/pdf/2012.02206.pdf)                                                                                                        | CVPR '21| [github](https://github.com/daveredrum/Scan2Cap) |","| 2020-8-23 | ReferIt3D | Stanford | [ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460409.pdf) | ECCV '20 | [github](https://github.com/referit3d/referit3d) |","| 2019-12-18 |     ScanRefer     |      TUM   | [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](https://arxiv.org/pdf/2112.10482.pdf)                                                                                                        | ECCV '20 | [github](https://daveredrum.github.io/ScanRefer/) |","","## Contributing","","Your contributions are always welcome!","","I will keep some pull requests open if I'm not sure if they are awesome for 3D LLMs, you could vote for them by adding 👍 to them.","","---","","If you have any questions about this opinionated list, please get in touch at xianzheng@robots.ox.ac.uk or Wechat ID: mxz1997112.","","## Star History","","[![Star History Chart](https://api.star-history.com/svg?repos=ActiveVisionLab/Awesome-LLM-3D\u0026type=Date)](https://star-history.com/#ActiveVisionLab/Awesome-LLM-3D\u0026Date)","","## Citation","If you find this repository useful, please consider citing this paper:","```","@misc{ma2024llmsstep3dworld,","      title={When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models}, ","      author={Xianzheng Ma and Yash Bhalgat and Brandon Smart and Shuai Chen and Xinghui Li and Jian Ding and Jindong Gu and Dave Zhenyu Chen and Songyou Peng and Jia-Wang Bian and Philip H Torr and Marc Pollefeys and Matthias Nießner and Ian D Reid and Angel X. Chang and Iro Laina and Victor Adrian Prisacariu},","      year={2024},","      journal={arXiv preprint arXiv:2405.10255},","}","```","","## Acknowledgement","This repo is inspired by 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<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🏠 About</h2><a id="user-content--about" class="anchor" aria-label="Permalink: 🏠 About" href="#-about"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<p dir="auto">Here is a curated list of papers about 3D-Related Tasks empowered by Large Language Models (LLMs).
It contains various tasks including 3D understanding, reasoning, generation, and embodied agents. Also, we include other Foundation Models (CLIP, SAM) for the whole picture of this area.</p>
<p dir="auto">This is an active repository, you can watch for following the latest advances. If you find it useful, please kindly star ⭐ this repo and <a href="#citation">cite</a> the paper.</p>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔥 News</h2><a id="user-content--news" class="anchor" aria-label="Permalink: 🔥 News" href="#-news"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<ul dir="auto">
<li>[2026-03-20] Our benchmark paper <strong>Real-3DQA</strong> is now available at ICLR 2026! Following our survey paper, we now release the benchmark paper on genuine 3D spatial understanding. <a href="https://real-3dqa.github.io/" rel="nofollow">Project Page</a></li>
<li>[2025-10-21] 📢 We have released the <strong>second version</strong> of our survey, updated to include literature up to <strong>July 2025</strong>:<br>
👉 <a href="https://arxiv.org/pdf/2405.10255v2" rel="nofollow"><em>When LLMs Step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models</em></a></li>
<li>[2024-05-16] Check out the first survey paper in the 3D-LLM domain: <a href="https://arxiv.org/pdf/2405.10255" rel="nofollow">When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models</a></li>
<li>[2024-01-06] <a href="https://runsenxu.com/" rel="nofollow">Runsen Xu</a> added chronological information and <a href="https://xianzhengma.github.io/" rel="nofollow">Xianzheng Ma</a> reorganized it in Z-A order for better following the latest advances.</li>
<li>[2023-12-16] <a href="https://xianzhengma.github.io/" rel="nofollow">Xianzheng Ma</a> and <a href="https://yashbhalgat.github.io/" rel="nofollow">Yash Bhalgat</a> curated this list and published the first version;</li>
</ul>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of Contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of Contents" href="#table-of-contents"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<ul dir="auto">
<li><a href="#awesome-llm-3D">Awesome-LLM-3D</a>
<ul dir="auto">
<li><a href="#3d-unified-understanding-and-generation-via-llm">3D Unified Understanding and Generation (LLM)</a></li>
<li><a href="#3d-understanding-via-llm">3D Understanding (LLM)</a></li>
<li><a href="#3d-understanding-via-other-foundation-models">3D Understanding (other Foundation Models)</a></li>
<li><a href="#3d-reasoning">3D Reasoning</a></li>
<li><a href="#3d-generation">3D Generation</a></li>
<li><a href="#3d-embodied-agent">3D Embodied Agent</a></li>
<li><a href="#3d-benchmarks">3D Benchmarks</a></li>
<li><a href="#contributing">Contributing</a></li>
</ul>
</li>
</ul>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Unified Understanding and Generation via LLM</h2><a id="user-content-3d-unified-understanding-and-generation-via-llm" class="anchor" aria-label="Permalink: 3D Unified Understanding and Generation via LLM" href="#3d-unified-understanding-and-generation-via-llm"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">Keywords</th>
<th align="center">Institute (first)</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-11-07</td>
<td align="center">Omni-View</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/abs/2511.07222" rel="nofollow">Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/AIDC-AI/Omni-View">github</a></td>
</tr>
<tr>
<td align="center">2025-08-16</td>
<td align="center">UniUGG</td>
<td align="center">FDU</td>
<td align="left"><a href="https://arxiv.org/abs/2508.11952" rel="nofollow">UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/fudan-zvg/UniUGG">github</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Understanding via LLM</h2><a id="user-content-3d-understanding-via-llm" class="anchor" aria-label="Permalink: 3D Understanding via LLM" href="#3d-understanding-via-llm"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">Keywords</th>
<th align="center">Institute (first)</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-11-27</td>
<td align="center">G<sup>2</sup>VLM</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2511.21688" rel="nofollow">G<sup>2</sup>VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/InternRobotics/G2VLM">github</a></td>
</tr>
<tr>
<td align="center">2025-11-07</td>
<td align="center">Omni-View</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/abs/2511.07222" rel="nofollow">Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/AIDC-AI/Omni-View">github</a></td>
</tr>
<tr>
<td align="center">2025-08-16</td>
<td align="center">UniUGG</td>
<td align="center">FDU</td>
<td align="left"><a href="https://arxiv.org/abs/2508.11952" rel="nofollow">UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/fudan-zvg/UniUGG">github</a></td>
</tr>
<tr>
<td align="center">2025-07-31</td>
<td align="center">3D-R1</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/abs/2507.23478" rel="nofollow">3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://aigeeksgroup.github.io/3D-R1/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-11</td>
<td align="center">LEO-VL</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2506.09935" rel="nofollow">LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://leo-vl.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-02</td>
<td align="center">3DRS</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/html/2506.01946v1" rel="nofollow">MLLMs Need 3D-Aware Representation Supervision for Scene Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://visual-ai.github.io/3drs/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-30</td>
<td align="center">VG LLM</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/abs/2505.24625" rel="nofollow">Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://lavi-lab.github.io/VG-LLM/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-29</td>
<td align="center">Spatial-MLLM</td>
<td align="center">THU</td>
<td align="left"><a href="https://arxiv.org/pdf/2505.23747" rel="nofollow">Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://diankun-wu.github.io/Spatial-MLLM/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-28</td>
<td align="center">SeeGround</td>
<td align="center">HKUST(GZ)</td>
<td align="left"><a href="https://arxiv.org/abs/2505.22429" rel="nofollow">Zero-Shot 3D Visual Grounding from Vision-Language Models</a></td>
<td align="center">CVPRW'25</td>
<td align="center"><a href="https://seeground.github.io" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-28</td>
<td align="center">3DLLM-Mem</td>
<td align="center">UCLA/Google</td>
<td align="left"><a href="https://arxiv.org/abs/2505.22657" rel="nofollow">3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model</a></td>
<td align="center">NeurIPS'25</td>
<td align="center"><a href="https://3dllm-mem.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-04-24</td>
<td align="center">3D-LLaVA</td>
<td align="center">U of Adelaide</td>
<td align="left"><a href="https://arxiv.org/pdf/2501.01163" rel="nofollow">3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://github.com/djiajunustc/3D-LLaVA">github</a></td>
</tr>
<tr>
<td align="center">2025-04-03</td>
<td align="center">Ross3D</td>
<td align="center">CASIA</td>
<td align="left"><a href="https://arxiv.org/pdf/2504.01901" rel="nofollow">Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://haochen-wang409.github.io/ross3d/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-03-08</td>
<td align="center">SplatTalk</td>
<td align="center">GIT</td>
<td align="left"><a href="https://arxiv.org/pdf/2503.06271" rel="nofollow">SplatTalk: 3D VQA with Gaussian Splatting</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">github</a></td>
</tr>
<tr>
<td align="center">2025-03-01</td>
<td align="center">Inst3D-LMM</td>
<td align="center">ZJU</td>
<td align="left"><a href="https://arxiv.org/abs/2503.00513" rel="nofollow">Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://github.com/hanxunyu/Inst3D-LMM">github</a></td>
</tr>
<tr>
<td align="center">2025-02-13</td>
<td align="center">ENEL</td>
<td align="center">SH AILab</td>
<td align="left"><a href="https://arxiv.org/pdf/2502.09620v1" rel="nofollow">ENEL: Exploring the Potential of Encoder-free Architectures in 3D LMMs</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/Ivan-Tang-3D/ENEL/tree/main?tab=readme-ov-file">project</a></td>
</tr>
<tr>
<td align="center">2025-02-02</td>
<td align="center">LSceneLLM</td>
<td align="center">SCUT</td>
<td align="left"><a href="https://arxiv.org/pdf/2412.01292" rel="nofollow">LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://github.com/Hoyyyaard/LSceneLLM">project</a></td>
</tr>
<tr>
<td align="center">2025-01-02</td>
<td align="center">GPT4Scene</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2501.01428" rel="nofollow">GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://gpt4scene.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-12-05</td>
<td align="center">SeeGround</td>
<td align="center">HKUST(GZ)</td>
<td align="left"><a href="https://arxiv.org/abs/2412.04383" rel="nofollow">SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://seeground.github.io" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-12-03</td>
<td align="center">Video-3D LLM</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/abs/2412.00493" rel="nofollow">Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://github.com/LaVi-Lab/Video-3D-LLM">project</a></td>
</tr>
<tr>
<td align="center">2024-11-29</td>
<td align="center">PerLA</td>
<td align="center">Fondazione Bruno Kessler</td>
<td align="left"><a href="https://arxiv.org/abs/2411.19774" rel="nofollow">PerLA: Perceptive 3D Language Assistant</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://gfmei.github.io/PerLA/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-10-12</td>
<td align="center">Situation3D</td>
<td align="center">UIUC</td>
<td align="left"><a href="https://arxiv.org/abs/2406.07544" rel="nofollow">Situational Awareness Matters in 3D Vision Language Reasoning</a></td>
<td align="center">CVPR '24</td>
<td align="center"><a href="https://yunzeman.github.io/situation3d/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-09-30</td>
<td align="center">Robin3D</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/abs/2410.00255" rel="nofollow">Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning</a></td>
<td align="center">ICCV '25</td>
<td align="center"><a href="https://github.com/WeitaiKang/Robin3D">github</a></td>
</tr>
<tr>
<td align="center">2024-09-28</td>
<td align="center">LLaVA-3D</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2409.18125" rel="nofollow">LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zcmax.github.io/projects/LLaVA-3D/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-09-08</td>
<td align="center">MSR3D</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2409.02389" rel="nofollow">Multi-modal Situated Reasoning in 3D Scenes</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://msr3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-08-28</td>
<td align="center">GreenPLM</td>
<td align="center">HUST</td>
<td align="left"><a href="https://arxiv.org/pdf/2408.15966" rel="nofollow"> More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/TangYuan96/GreenPLM">github</a></td>
</tr>
<tr>
<td align="center">2024-06-17</td>
<td align="center">LLaNA</td>
<td align="center">UniBO</td>
<td align="left"><a href="https://arxiv.org/pdf/2406.11840" rel="nofollow">LLaNA: Large Language and NeRF Assistant</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://andreamaduzzi.github.io/llana/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-06-07</td>
<td align="center">SpatialPIN</td>
<td align="center">Oxford</td>
<td align="left"><a href="https://arxiv.org/abs/2403.13438" rel="nofollow">SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://dannymcy.github.io/zeroshot_task_hallucination/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-06-03</td>
<td align="center">SpatialRGPT</td>
<td align="center">UCSD</td>
<td align="left"><a href="https://arxiv.org/abs/2406.01584" rel="nofollow">SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://github.com/AnjieCheng/SpatialRGPT">github</a></td>
</tr>
<tr>
<td align="center">2024-05-02</td>
<td align="center">MiniGPT-3D</td>
<td align="center">HUST</td>
<td align="left"><a href="https://arxiv.org/pdf/2405.01413" rel="nofollow">MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors</a></td>
<td align="center">ACM MM '24</td>
<td align="center"><a href="https://tangyuan96.github.io/minigpt_3d_project_page/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-02-27</td>
<td align="center">ShapeLLM</td>
<td align="center">XJTU</td>
<td align="left"><a href="https://arxiv.org/pdf/2402.17766" rel="nofollow">ShapeLLM: Universal 3D Object Understanding for Embodied Interaction</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://qizekun.github.io/shapellm/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-01-22</td>
<td align="center">SpatialVLM</td>
<td align="center">Google DeepMind</td>
<td align="left"><a href="https://arxiv.org/abs/2401.12168" rel="nofollow">SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities</a></td>
<td align="center">CVPR '24</td>
<td align="center"><a href="https://spatial-vlm.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-12-21</td>
<td align="center">LiDAR-LLM</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.14074.pdf" rel="nofollow">LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://sites.google.com/view/lidar-llm" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-12-15</td>
<td align="center">3DAP</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.09738.pdf" rel="nofollow">3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">project</a></td>
</tr>
<tr>
<td align="center">2023-12-13</td>
<td align="center">Chat-Scene</td>
<td align="center">ZJU</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.08168.pdf" rel="nofollow">Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://github.com/ZzZZCHS/Chat-Scene">github</a></td>
</tr>
<tr>
<td align="center">2023-12-5</td>
<td align="center">GPT4Point</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.02980.pdf" rel="nofollow">GPT4Point: A Unified Framework for Point-Language Understanding and Generation</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/Pointcept/GPT4Point">github</a></td>
</tr>
<tr>
<td align="center">2023-11-30</td>
<td align="center">LL3DA</td>
<td align="center">Fudan University</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.18651.pdf" rel="nofollow">LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/Open3DA/LL3DA">github</a></td>
</tr>
<tr>
<td align="center">2023-11-26</td>
<td align="center">ZSVG3D</td>
<td align="center">CUHK(SZ)</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.15383.pdf" rel="nofollow">Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://curryyuan.github.io/ZSVG3D/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-11-18</td>
<td align="center">LEO</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.12871.pdf" rel="nofollow">An Embodied Generalist Agent in 3D World</a></td>
<td align="center">ICML '24</td>
<td align="center"><a href="https://github.com/embodied-generalist/embodied-generalist">github</a></td>
</tr>
<tr>
<td align="center">2023-10-14</td>
<td align="center">JM3D-LLM</td>
<td align="center">Xiamen University</td>
<td align="left"><a href="https://arxiv.org/pdf/2310.09503v2.pdf" rel="nofollow">JM3D &amp; JM3D-LLM: Elevating 3D Representation with Joint Multi-modal Cues</a></td>
<td align="center">ACM MM '23</td>
<td align="center"><a href="https://github.com/mr-neko/jm3d">github</a></td>
</tr>
<tr>
<td align="center">2023-10-10</td>
<td align="center">Uni3D</td>
<td align="center">BAAI</td>
<td align="left"><a href="https://arxiv.org/abs/2310.06773" rel="nofollow">Uni3D: Exploring Unified 3D Representation at Scale</a></td>
<td align="center">ICLR '24</td>
<td align="center"><a href="https://github.com/baaivision/Uni3D">project</a></td>
</tr>
<tr>
<td align="center">2023-9-27</td>
<td align="center">-</td>
<td align="center">KAUST</td>
<td align="left"><a href="https://arxiv.org/abs/2306.03253" rel="nofollow">Zero-Shot 3D Shape Correspondence</a></td>
<td align="center">Siggraph Asia '23</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-9-21</td>
<td align="center">LLM-Grounder</td>
<td align="center">U-Mich</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.12311.pdf" rel="nofollow">LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent</a></td>
<td align="center">ICRA '24</td>
<td align="center"><a href="https://github.com/sled-group/chat-with-nerf">github</a></td>
</tr>
<tr>
<td align="center">2023-9-1</td>
<td align="center">Point-Bind</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.00615.pdf" rel="nofollow">Point-Bind &amp; Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/ZiyuGuo99/Point-Bind_Point-LLM">github</a></td>
</tr>
<tr>
<td align="center">2023-8-31</td>
<td align="center">PointLLM</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/pdf/2308.16911.pdf" rel="nofollow">PointLLM: Empowering Large Language Models to Understand Point Clouds</a></td>
<td align="center">ECCV '24</td>
<td align="center"><a href="https://github.com/OpenRobotLab/PointLLM">github</a></td>
</tr>
<tr>
<td align="center">2023-8-17</td>
<td align="center">Chat-3D</td>
<td align="center">ZJU</td>
<td align="left"><a href="https://arxiv.org/pdf/2308.08769v1.pdf" rel="nofollow">Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/Chat-3D/Chat-3D">github</a></td>
</tr>
<tr>
<td align="center">2023-8-8</td>
<td align="center">3D-VisTA</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://Arxiv.org/abs/2308.04352" rel="nofollow">3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">github</a></td>
</tr>
<tr>
<td align="center">2023-7-24</td>
<td align="center">3D-LLM</td>
<td align="center">UCLA</td>
<td align="left"><a href="https://arxiv.org/pdf/2307.12981.pdf" rel="nofollow">3D-LLM: Injecting the 3D World into Large Language Models</a></td>
<td align="center">NeurIPS '23</td>
<td align="center"><a href="https://github.com/UMass-Foundation-Model/3D-LLM">github</a></td>
</tr>
<tr>
<td align="center">2023-3-29</td>
<td align="center">ViewRefer</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/pdf/2303.16894.pdf" rel="nofollow">ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="https://github.com/Ivan-Tang-3D/ViewRefer3D">github</a></td>
</tr>
<tr>
<td align="center">2022-9-12</td>
<td align="center">-</td>
<td align="center">MIT</td>
<td align="left"><a href="https://arxiv.org/pdf/2209.05629.pdf" rel="nofollow">Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/MIT-SPARK/llm_scene_understanding">github</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Understanding via other Foundation Models</h2><a id="user-content-3d-understanding-via-other-foundation-models" class="anchor" aria-label="Permalink: 3D Understanding via other Foundation Models" href="#3d-understanding-via-other-foundation-models"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">ID</th>
<th align="center">keywords</th>
<th align="center">Institute (first)</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-11-20</td>
<td align="center">POMA-3D</td>
<td align="center">Imperial</td>
<td align="left"><a href="https://arxiv.org/abs/2511.16567" rel="nofollow">POMA-3D: The Point Map Way to 3D Scene Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://matchlab-imperial.github.io/poma3d/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-07-26</td>
<td align="center">OV-3DDet</td>
<td align="center">HKUST</td>
<td align="left"><a href="https://arxiv.org/abs/2406.00830" rel="nofollow">CoDAv2: Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection</a></td>
<td align="center">TPAMI '25</td>
<td align="center"><a href="https://github.com/yangcaoai/CoDA_NeurIPS2023">github</a></td>
</tr>
<tr>
<td align="center">2025-02-20</td>
<td align="center">CrossOver</td>
<td align="center">Stanford</td>
<td align="left"><a href="https://arxiv.org/abs/2502.15011" rel="nofollow">CrossOver: 3D Scene Cross-Modal Alignment</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://sayands.github.io/crossover/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-02-05</td>
<td align="center">SAGA</td>
<td align="center">SJTU</td>
<td align="left"><a href="https://arxiv.org/abs/2312.00860" rel="nofollow">Segment Any 3D Gaussians</a></td>
<td align="center">AAAI '25</td>
<td align="center"><a href="https://jumpat.github.io/SAGA/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-10-12</td>
<td align="center">Lexicon3D</td>
<td align="center">UIUC</td>
<td align="left"><a href="https://arxiv.org/abs/2409.03757" rel="nofollow">Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://yunzeman.github.io/lexicon3d/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-10-07</td>
<td align="center">Diff2Scene</td>
<td align="center">CMU</td>
<td align="left"><a href="https://arxiv.org/pdf/2407.13642" rel="nofollow">Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models</a></td>
<td align="center">ECCV'24</td>
<td align="center"><a href="https://diff2scene.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-07-19</td>
<td align="center">OpenSU3D</td>
<td align="center">TUM</td>
<td align="left"><a href="https://arxiv.org/abs/2409.03757" rel="nofollow">OpenSU3D: Open World 3D Scene Understanding using Foundation Models</a></td>
<td align="center">ICRA '25</td>
<td align="center"><a href="https://opensu3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-04-07</td>
<td align="center">Any2Point</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2404.07989" rel="nofollow">Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding</a></td>
<td align="center">ECCV'24</td>
<td align="center"><a href="https://github.com/Ivan-Tang-3D/Any2Point">github</a></td>
</tr>
<tr>
<td align="center">2024-03-16</td>
<td align="center">N2F2</td>
<td align="center">Oxford-VGG</td>
<td align="left"><a href="https://arxiv.org/pdf/2403.10997.pdf" rel="nofollow">N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields</a></td>
<td align="center">Arxiv</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-12-17</td>
<td align="center">SAI3D</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.11557.pdf" rel="nofollow">SAI3D: Segment Any Instance in 3D Scenes</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://yd-yin.github.io/SAI3D" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-12-17</td>
<td align="center">Open3DIS</td>
<td align="center">VinAI</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.10671.pdf" rel="nofollow">Open3DIS: Open-vocabulary 3D Instance Segmentation with 2D Mask Guidance</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://open3dis.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-11-6</td>
<td align="center">OVIR-3D</td>
<td align="center">Rutgers University</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.02873.pdf" rel="nofollow">OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data</a></td>
<td align="center">CoRL '23</td>
<td align="center"><a href="https://github.com/shiyoung77/OVIR-3D/">github</a></td>
</tr>
<tr>
<td align="center">2023-10-29</td>
<td align="center">OpenMask3D</td>
<td align="center">ETH</td>
<td align="left"><a href="https://openmask3d.github.io/static/pdf/openmask3d.pdf" rel="nofollow">OpenMask3D: Open-Vocabulary 3D Instance Segmentation</a></td>
<td align="center">NeurIPS '23</td>
<td align="center"><a href="https://openmask3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-10-5</td>
<td align="center">Open-Fusion</td>
<td align="center">-</td>
<td align="left"><a href="https://arxiv.org/pdf/2310.03923.pdf" rel="nofollow">Open-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/UARK-AICV/OpenFusion">github</a></td>
</tr>
<tr>
<td align="center">2023-9-22</td>
<td align="center">OV-3DDet</td>
<td align="center">HKUST</td>
<td align="left"><a href="https://arxiv.org/pdf/2310.02960.pdf" rel="nofollow">CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection</a></td>
<td align="center">NeurIPS '23</td>
<td align="center"><a href="https://github.com/yangcaoai/CoDA_NeurIPS2023">github</a></td>
</tr>
<tr>
<td align="center">2023-9-19</td>
<td align="center">LAMP</td>
<td align="center">-</td>
<td align="left"><a href="https://openreview.net/forum?id=H49g8rRIiF" rel="nofollow">From Language to 3D Worlds: Adapting Language Model for Point Cloud Perception</a></td>
<td align="center">OpenReview</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-9-15</td>
<td align="center">OpenNerf</td>
<td align="center">-</td>
<td align="left"><a href="https://openreview.net/pdf?id=SgjAojPKb3" rel="nofollow">OpenNerf: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views</a></td>
<td align="center">OpenReview</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">github</a></td>
</tr>
<tr>
<td align="center">2023-9-1</td>
<td align="center">OpenIns3D</td>
<td align="center">Cambridge</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.00616.pdf" rel="nofollow">OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zheninghuang.github.io/OpenIns3D/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-6-7</td>
<td align="center">Contrastive Lift</td>
<td align="center">Oxford-VGG</td>
<td align="left"><a href="https://arxiv.org/pdf/2306.04633.pdf" rel="nofollow">Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion</a></td>
<td align="center">NeurIPS '23</td>
<td align="center"><a href="https://github.com/yashbhalgat/Contrastive-Lift">github</a></td>
</tr>
<tr>
<td align="center">2023-6-4</td>
<td align="center">Multi-CLIP</td>
<td align="center">ETH</td>
<td align="left"><a href="https://arxiv.org/pdf/2306.02329.pdf" rel="nofollow">Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes</a></td>
<td align="center">Arxiv</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-5-23</td>
<td align="center">3D-OVS</td>
<td align="center">NTU</td>
<td align="left"><a href="https://arxiv.org/pdf/2305.14093.pdf" rel="nofollow">Weakly Supervised 3D Open-vocabulary Segmentation</a></td>
<td align="center">NeurIPS '23</td>
<td align="center"><a href="https://github.com/Kunhao-Liu/3D-OVS">github</a></td>
</tr>
<tr>
<td align="center">2023-5-21</td>
<td align="center">VL-Fields</td>
<td align="center">University of Edinburgh</td>
<td align="left"><a href="https://arxiv.org/pdf/2305.12427.pdf" rel="nofollow">VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations</a></td>
<td align="center">ICRA '23</td>
<td align="center"><a href="https://tsagkas.github.io/vl-fields/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-5-8</td>
<td align="center">CLIP-FO3D</td>
<td align="center">Tsinghua University</td>
<td align="left"><a href="https://arxiv.org/pdf/2303.04748.pdf" rel="nofollow">CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP</a></td>
<td align="center">ICCVW '23</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-4-12</td>
<td align="center">3D-VQA</td>
<td align="center">ETH</td>
<td align="left"><a href="https://arxiv.org/pdf/2304.06061.pdf" rel="nofollow">CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes</a></td>
<td align="center">CVPRW '23</td>
<td align="center"><a href="https://github.com/AlexDelitzas/3D-VQA">github</a></td>
</tr>
<tr>
<td align="center">2023-4-3</td>
<td align="center">RegionPLC</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2304.00962.pdf" rel="nofollow">RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://jihanyang.github.io/projects/RegionPLC" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-3-20</td>
<td align="center">CG3D</td>
<td align="center">JHU</td>
<td align="left"><a href="https://arxiv.org/pdf/2303.11313.pdf" rel="nofollow">CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/deeptibhegde/CLIP-goes-3D">github</a></td>
</tr>
<tr>
<td align="center">2023-3-16</td>
<td align="center">LERF</td>
<td align="center">UC Berkeley</td>
<td align="left"><a href="https://arxiv.org/pdf/2303.09553.pdf" rel="nofollow">LERF: Language Embedded Radiance Fields</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="https://github.com/kerrj/lerf">github</a></td>
</tr>
<tr>
<td align="center">2023-2-14</td>
<td align="center">ConceptFusion</td>
<td align="center">MIT</td>
<td align="left"><a href="https://arxiv.org/pdf/2302.07241.pdf" rel="nofollow">ConceptFusion: Open-set Multimodal 3D Mapping</a></td>
<td align="center">RSS '23</td>
<td align="center"><a href="https://concept-fusion.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-1-12</td>
<td align="center">CLIP2Scene</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2301.04926.pdf" rel="nofollow">CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP</a></td>
<td align="center">CVPR '23</td>
<td align="center"><a href="https://github.com/runnanchen/CLIP2Scene">github</a></td>
</tr>
<tr>
<td align="center">2022-12-1</td>
<td align="center">UniT3D</td>
<td align="center">TUM</td>
<td align="left"><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_UniT3D_A_Unified_Transformer_for_3D_Dense_Captioning_and_Visual_ICCV_2023_paper.pdf" rel="nofollow">UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">github</a></td>
</tr>
<tr>
<td align="center">2022-11-29</td>
<td align="center">PLA</td>
<td align="center">HKU</td>
<td align="left"><a href="https://arxiv.org/pdf/2211.16312.pdf" rel="nofollow">PLA: Language-Driven Open-Vocabulary 3D Scene Understanding</a></td>
<td align="center">CVPR '23</td>
<td align="center"><a href="https://github.com/CVMI-Lab/PLA">github</a></td>
</tr>
<tr>
<td align="center">2022-11-28</td>
<td align="center">OpenScene</td>
<td align="center">ETHz</td>
<td align="left"><a href="https://arxiv.org/pdf/2211.15654.pdf" rel="nofollow">OpenScene: 3D Scene Understanding with Open Vocabularies</a></td>
<td align="center">CVPR '23</td>
<td align="center"><a href="https://github.com/pengsongyou/openscene">github</a></td>
</tr>
<tr>
<td align="center">2022-10-11</td>
<td align="center">CLIP-Fields</td>
<td align="center">NYU</td>
<td align="left"><a href="https://arxiv.org/pdf/2210.05663.pdf" rel="nofollow">CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://mahis.life/clip-fields/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2022-7-23</td>
<td align="center">Semantic Abstraction</td>
<td align="center">Columbia</td>
<td align="left"><a href="https://arxiv.org/pdf/2207.11514.pdf" rel="nofollow">Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models</a></td>
<td align="center">CoRL '22</td>
<td align="center"><a href="https://semantic-abstraction.cs.columbia.edu/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2022-4-26</td>
<td align="center">ScanNet200</td>
<td align="center">TUM</td>
<td align="left"><a href="https://arxiv.org/pdf/2204.07761.pdf" rel="nofollow">Language-Grounded Indoor 3D Semantic Segmentation in the Wild</a></td>
<td align="center">ECCV '22</td>
<td align="center"><a href="https://rozdavid.github.io/scannet200" rel="nofollow">project</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Reasoning</h2><a id="user-content-3d-reasoning" class="anchor" aria-label="Permalink: 3D Reasoning" href="#3d-reasoning"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">keywords</th>
<th align="center">Institute (first)</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-12-15</td>
<td align="center">RoboTracer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2512.13660" rel="nofollow">RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboTracer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-11</td>
<td align="center">SceneCOT</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://scenecot.github.io/" rel="nofollow">SceneCOT: Eliciting Chain-of-Thought Reasoning in 3D Scenes</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://scenecot.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-04</td>
<td align="center">RoboRefer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2506.04308" rel="nofollow">RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboRefer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-09-08</td>
<td align="center">MSR3D</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2409.02389" rel="nofollow">Multi-modal Situated Reasoning in 3D Scenes</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://msr3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-5-20</td>
<td align="center">3D-CLR</td>
<td align="center">UCLA</td>
<td align="left"><a href="https://arxiv.org/pdf/2303.11327.pdf" rel="nofollow">3D Concept Learning and Reasoning from Multi-View Images</a></td>
<td align="center">CVPR '23</td>
<td align="center"><a href="https://github.com/evelinehong/3D-CLR-Official">github</a></td>
</tr>
<tr>
<td align="center">-</td>
<td align="center">Transcribe3D</td>
<td align="center">TTI, Chicago</td>
<td align="left"><a href="https://openreview.net/pdf?id=7j3sdUZMTF" rel="nofollow">Transcribe3D: Grounding LLMs Using Transcribed Information for 3D Referential Reasoning with Self-Corrected Finetuning</a></td>
<td align="center">CoRL '23</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">github</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Generation</h2><a id="user-content-3d-generation" class="anchor" aria-label="Permalink: 3D Generation" href="#3d-generation"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">keywords</th>
<th align="center">Institute</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-11-07</td>
<td align="center">Omni-View</td>
<td align="center">PKU</td>
<td align="left"><a href="https://arxiv.org/abs/2511.07222" rel="nofollow">Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/AIDC-AI/Omni-View">github</a></td>
</tr>
<tr>
<td align="center">2025-08-16</td>
<td align="center">UniUGG</td>
<td align="center">FDU</td>
<td align="left"><a href="https://arxiv.org/abs/2508.11952" rel="nofollow">UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://github.com/fudan-zvg/UniUGG">github</a></td>
</tr>
<tr>
<td align="center">2024-11-14</td>
<td align="center">LLaMA-Mesh</td>
<td align="center">THU</td>
<td align="left"><a href="https://arxiv.org/html/2411.09595v1" rel="nofollow">LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-11-29</td>
<td align="center">ShapeGPT</td>
<td align="center">Fudan University</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.17618.pdf" rel="nofollow">ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/OpenShapeLab/ShapeGPT">github</a></td>
</tr>
<tr>
<td align="center">2023-11-27</td>
<td align="center">MeshGPT</td>
<td align="center">TUM</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.15475.pdf" rel="nofollow">MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://nihalsid.github.io/mesh-gpt/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-10-19</td>
<td align="center">3D-GPT</td>
<td align="center">ANU</td>
<td align="left"><a href="https://arxiv.org/pdf/2310.12945.pdf" rel="nofollow">3D-GPT: Procedural 3D Modeling with Large Language Models</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://dreamllm.github.io/" rel="nofollow">github</a></td>
</tr>
<tr>
<td align="center">2023-9-21</td>
<td align="center">LLMR</td>
<td align="center">MIT</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.12276.pdf" rel="nofollow">LLMR: Real-time Prompting of Interactive Worlds using Large Language Models</a></td>
<td align="center">Arxiv</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">2023-9-20</td>
<td align="center">DreamLLM</td>
<td align="center">MEGVII</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.11499.pdf" rel="nofollow">DreamLLM: Synergistic Multimodal Comprehension and Creation</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/RunpeiDong/DreamLLM">github</a></td>
</tr>
<tr>
<td align="center">2023-4-1</td>
<td align="center">ChatAvatar</td>
<td align="center">Deemos Tech</td>
<td align="left"><a href="https://dl.acm.org/doi/abs/10.1145/3592094" rel="nofollow">DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance</a></td>
<td align="center">ACM TOG</td>
<td align="center"><a href="https://hyperhuman.deemos.com/" rel="nofollow">website</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Embodied Agent</h2><a id="user-content-3d-embodied-agent" class="anchor" aria-label="Permalink: 3D Embodied Agent" href="#3d-embodied-agent"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">keywords</th>
<th align="center">Institute</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2025-12-15</td>
<td align="center">RoboTracer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2512.13660" rel="nofollow">RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboTracer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-04</td>
<td align="center">RoboRefer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2506.04308" rel="nofollow">RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboRefer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-30</td>
<td align="center">VeBrain</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2506.00123v1" rel="nofollow">Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://internvl.github.io/blog/2025-05-26-VeBrain/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-28</td>
<td align="center">3DLLM-Mem</td>
<td align="center">UCLA</td>
<td align="left"><a href="https://arxiv.org/abs/2505.22657" rel="nofollow">3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://3dllm-mem.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-01-22</td>
<td align="center">SpatialVLM</td>
<td align="center">Deepmind</td>
<td align="left"><a href="https://arxiv.org/abs/2401.12168" rel="nofollow">SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities</a></td>
<td align="center">CVPR '24</td>
<td align="center"><a href="https://spatial-vlm.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2023-12-05</td>
<td align="center">NaviLLM</td>
<td align="center">CUHK</td>
<td align="left"><a href="https://arxiv.org/abs/2312.02010" rel="nofollow">Towards Learning a Generalist Model for Embodied Navigation</a></td>
<td align="center">CVPR '24</td>
<td align="center"><a href="https://github.com/zd11024/NaviLLM">project</a></td>
</tr>
<tr>
<td align="center">2023-11-27</td>
<td align="center">Dobb-E</td>
<td align="center">NYU</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.16098.pdf" rel="nofollow">On Bringing Robots Home</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/notmahi/dobb-e">github</a></td>
</tr>
<tr>
<td align="center">2023-11-26</td>
<td align="center">STEVE</td>
<td align="center">ZJU</td>
<td align="left"><a href="https://arxiv.org/abs/2311.15209" rel="nofollow">See and Think: Embodied Agent in Virtual Environment</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/rese1f/STEVE">github</a></td>
</tr>
<tr>
<td align="center">2023-11-18</td>
<td align="center">LEO</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.12871.pdf" rel="nofollow">An Embodied Generalist Agent in 3D World</a></td>
<td align="center">ICML '24</td>
<td align="center"><a href="https://github.com/embodied-generalist/embodied-generalist">github</a></td>
</tr>
<tr>
<td align="center">2023-9-14</td>
<td align="center">UniHSI</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.07918.pdf" rel="nofollow">Unified Human-Scene Interaction via Prompted Chain-of-Contacts</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/OpenRobotLab/UniHSI">github</a></td>
</tr>
<tr>
<td align="center">2023-7-28</td>
<td align="center">RT-2</td>
<td align="center">Google-DeepMind</td>
<td align="left"><a href="https://arxiv.org/pdf/2307.15818.pdf" rel="nofollow">RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://robotics-transformer2.github.io/" rel="nofollow">github</a></td>
</tr>
<tr>
<td align="center">2023-7-12</td>
<td align="center">SayPlan</td>
<td align="center">QUT Centre for Robotics</td>
<td align="left"><a href="https://arxiv.org/pdf/2307.06135.pdf" rel="nofollow">SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning</a></td>
<td align="center">CoRL '23</td>
<td align="center"><a href="https://sayplan.github.io/" rel="nofollow">github</a></td>
</tr>
<tr>
<td align="center">2023-7-12</td>
<td align="center">VoxPoser</td>
<td align="center">Stanford</td>
<td align="left"><a href="https://voxposer.github.io/voxposer.pdf" rel="nofollow">VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/huangwl18/VoxPoser">github</a></td>
</tr>
<tr>
<td align="center">2022-12-13</td>
<td align="center">RT-1</td>
<td align="center">Google</td>
<td align="left"><a href="https://robotics-transformer1.github.io/assets/rt1.pdf" rel="nofollow">RT-1: Robotics Transformer for Real-World Control at Scale</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://robotics-transformer1.github.io/" rel="nofollow">github</a></td>
</tr>
<tr>
<td align="center">2022-12-8</td>
<td align="center">LLM-Planner</td>
<td align="center">The Ohio State University</td>
<td align="left"><a href="https://arxiv.org/pdf/2212.04088.pdf" rel="nofollow">LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="https://github.com/OSU-NLP-Group/LLM-Planner/">github</a></td>
</tr>
<tr>
<td align="center">2022-10-11</td>
<td align="center">CLIP-Fields</td>
<td align="center">NYU, Meta</td>
<td align="left"><a href="https://arxiv.org/pdf/2210.05663.pdf" rel="nofollow">CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory</a></td>
<td align="center">RSS '23</td>
<td align="center"><a href="https://github.com/notmahi/clip-fields">github</a></td>
</tr>
<tr>
<td align="center">2022-09-20</td>
<td align="center">NLMap-SayCan</td>
<td align="center">Google</td>
<td align="left"><a href="https://arxiv.org/abs/2209.09874" rel="nofollow">Open-vocabulary Queryable Scene Representations for Real World Planning</a></td>
<td align="center">ICRA '23</td>
<td align="center"><a href="https://nlmap-saycan.github.io/" rel="nofollow">github</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3D Benchmarks</h2><a id="user-content-3d-benchmarks" class="anchor" aria-label="Permalink: 3D Benchmarks" href="#3d-benchmarks"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<markdown-accessiblity-table><table>
<thead>
<tr>
<th align="center">Date</th>
<th align="center">keywords</th>
<th align="center">Institute</th>
<th align="left">Paper</th>
<th align="center">Publication</th>
<th align="center">Others</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">2026-01-22</td>
<td align="center">Real-3DQA</td>
<td align="center">VGG, Oxford</td>
<td align="left"><a href="https://openreview.net/forum?id=3vlMiJwo8b" rel="nofollow">Do 3D Large Language Models Really Understand 3D Spatial Relationships?</a></td>
<td align="center">ICLR 2026</td>
<td align="center"><a href="https://real-3dqa.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-12-15</td>
<td align="center">RoboTracer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2512.13660" rel="nofollow">RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboTracer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">:-----:</td>
<td align="center">:------------------:</td>
<td align="center">:--------------:</td>
<td align="left">:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</td>
<td align="center">:---------:</td>
<td align="center">:---------:</td>
</tr>
<tr>
<td align="center">2025-11-28</td>
<td align="center">ORS3D</td>
<td align="center">HUST</td>
<td align="left"><a href="https://arxiv.org/abs/2511.19430" rel="nofollow">Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution</a></td>
<td align="center">AAAI ‘26</td>
<td align="center"><a href="https://h-embodvis.github.io/GRANT/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-04</td>
<td align="center">RoboRefer</td>
<td align="center">BUAA</td>
<td align="left"><a href="https://arxiv.org/abs/2506.04308" rel="nofollow">RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://zhoues.github.io/RoboRefer/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-06-09</td>
<td align="center">SpaCE-10</td>
<td align="center">SJTU</td>
<td align="left"><a href="https://arxiv.org/abs/2506.07966" rel="nofollow">SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/VisionXLab/SpaCE-10">project</a></td>
</tr>
<tr>
<td align="center">2025-05-01</td>
<td align="center">Hypo3D</td>
<td align="center">Imperial</td>
<td align="left"><a href="https://arxiv.org/abs/2502.00954" rel="nofollow">Hypo3D: Exploring Hypothetical Reasoning in 3D</a></td>
<td align="center">ICML'25</td>
<td align="center"><a href="https://github.com/MatchLab-Imperial/Hypo3D">project</a></td>
</tr>
<tr>
<td align="center">2025-06-04</td>
<td align="center">Anywhere3D</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2506.04897" rel="nofollow">From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes</a></td>
<td align="center">NeurIPS '25</td>
<td align="center"><a href="https://anywhere-3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-05-01</td>
<td align="center">SpatialVQA</td>
<td align="center">JHU</td>
<td align="left"><a href="https://arxiv.org/abs/2505.00788" rel="nofollow">SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models</a></td>
<td align="center">CVPR'25</td>
<td align="center"><a href="/jkhu29/Awesome-LLM-3D/blob/avl-branch">project</a></td>
</tr>
<tr>
<td align="center">2025-04-03</td>
<td align="center">SPAR</td>
<td align="center">Fudan University</td>
<td align="left"><a href="https://arxiv.org/abs/2503.22976" rel="nofollow">From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://fudan-zvg.github.io/spar" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-03-28</td>
<td align="center">Beacon3D</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2503.22420" rel="nofollow">Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis</a></td>
<td align="center">CVPR '25</td>
<td align="center"><a href="https://beacon-3d.github.io" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2025-03-08</td>
<td align="center">3D-CoT</td>
<td align="center">PolyU, EIT</td>
<td align="left"><a href="https://arxiv.org/abs/2503.06232" rel="nofollow">Integrating Chain-of-Thought for Multimodal Alignment: A Study on 3D Vision-Language Learning</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://huggingface.co/datasets/Battam/3D-CoT" rel="nofollow">dataset</a></td>
</tr>
<tr>
<td align="center">2024-09-08</td>
<td align="center">MSQA / MSNN</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/abs/2409.02389" rel="nofollow">Multi-modal Situated Reasoning in 3D Scenes</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://msr3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-08-29</td>
<td align="center">Space3D-Bench</td>
<td align="center">ETHz</td>
<td align="left"><a href="https://arxiv.org/abs/2408.16662" rel="nofollow">Space3D-Bench: Spatial 3D Question Answering Benchmark</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://space3d-bench.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-07-24</td>
<td align="center">City-3DQA</td>
<td align="center">HKUST</td>
<td align="left"><a href="https://arxiv.org/pdf/2407.17398" rel="nofollow">3D Question Answering for City Scene Understanding</a></td>
<td align="center">ACM MM'24</td>
<td align="center"><a href="https://sites.google.com/view/city3dqa/?pli=1" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-06-13</td>
<td align="center">MMScan</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2406.09401" rel="nofollow">MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/OpenRobotLab/EmbodiedScan">github</a></td>
</tr>
<tr>
<td align="center">2024-06-10</td>
<td align="center">3D-GRAND / 3D-POPE</td>
<td align="center">UMich</td>
<td align="left"><a href="https://arxiv.org/pdf/2406.05132.pdf" rel="nofollow">3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://3d-grand.github.io" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-06-03</td>
<td align="center">SpatialRGPT-Bench</td>
<td align="center">UCSD</td>
<td align="left"><a href="https://arxiv.org/abs/2406.01584" rel="nofollow">SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models</a></td>
<td align="center">NeurIPS '24</td>
<td align="center"><a href="https://github.com/AnjieCheng/SpatialRGPT">github</a></td>
</tr>
<tr>
<td align="center">2024-05-27</td>
<td align="center">Reason3D</td>
<td align="center">UC Merced</td>
<td align="left"><a href="https://arxiv.org/abs/2405.17427" rel="nofollow">Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model</a></td>
<td align="center">3DV'25</td>
<td align="center"><a href="https://reason3d.github.io/" rel="nofollow">project</a></td>
</tr>
<tr>
<td align="center">2024-1-18</td>
<td align="center">SceneVerse</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/pdf/2401.09340.pdf" rel="nofollow">SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding</a></td>
<td align="center">ECCV '24</td>
<td align="center"><a href="https://github.com/scene-verse/sceneverse">github</a></td>
</tr>
<tr>
<td align="center">2023-12-26</td>
<td align="center">EmbodiedScan</td>
<td align="center">Shanghai AI Lab</td>
<td align="left"><a href="https://arxiv.org/pdf/2312.16170.pdf" rel="nofollow">EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/OpenRobotLab/EmbodiedScan">github</a></td>
</tr>
<tr>
<td align="center">2023-12-17</td>
<td align="center">M3DBench</td>
<td align="center">Fudan University</td>
<td align="left"><a href="https://arxiv.org/abs/2312.10763" rel="nofollow">M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts</a></td>
<td align="center">Arxiv</td>
<td align="center"><a href="https://github.com/OpenM3D/M3DBench">github</a></td>
</tr>
<tr>
<td align="center">2023-11-29</td>
<td align="center">-</td>
<td align="center">DeepMind</td>
<td align="left"><a href="https://arxiv.org/pdf/2311.17851.pdf" rel="nofollow">Leveraging VLM-Based Pipelines to Annotate 3D Objects</a></td>
<td align="center">ICML '24</td>
<td align="center"><a href="https://github.com/google-deepmind/objaverse_annotations">github</a></td>
</tr>
<tr>
<td align="center">2023-09-14</td>
<td align="center">CrossCoherence</td>
<td align="center">UniBO</td>
<td align="left"><a href="https://arxiv.org/pdf/2309.07917" rel="nofollow">Looking at words and points with attention: a benchmark for text-to-shape coherence</a></td>
<td align="center">ICCV '23</td>
<td align="center"><a href="https://github.com/AndreAmaduzzi/CrossCoherence">github</a></td>
</tr>
<tr>
<td align="center">2022-10-14</td>
<td align="center">SQA3D</td>
<td align="center">BIGAI</td>
<td align="left"><a href="https://arxiv.org/pdf/2210.07474.pdf" rel="nofollow">SQA3D: Situated Question Answering in 3D Scenes</a></td>
<td align="center">ICLR '23</td>
<td align="center"><a href="https://github.com/SilongYong/SQA3D">github</a></td>
</tr>
<tr>
<td align="center">2022-09-24</td>
<td align="center">FE-3DGQA</td>
<td align="center">Beihang University</td>
<td align="left"><a href="https://arxiv.org/pdf/2209.12028" rel="nofollow">Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline</a></td>
<td align="center">TCSVT</td>
<td align="center"><a href="https://github.com/zlccccc/3DVL_Codebase">github</a></td>
</tr>
<tr>
<td align="center">2021-12-20</td>
<td align="center">ScanQA</td>
<td align="center">RIKEN AIP</td>
<td align="left"><a href="https://arxiv.org/pdf/2112.10482.pdf" rel="nofollow">ScanQA: 3D Question Answering for Spatial Scene Understanding</a></td>
<td align="center">CVPR '23</td>
<td align="center"><a href="https://github.com/ATR-DBI/ScanQA">github</a></td>
</tr>
<tr>
<td align="center">2020-12-3</td>
<td align="center">Scan2Cap</td>
<td align="center">TUM</td>
<td align="left"><a href="https://arxiv.org/pdf/2012.02206.pdf" rel="nofollow">Scan2Cap: Context-aware Dense Captioning in RGB-D Scans</a></td>
<td align="center">CVPR '21</td>
<td align="center"><a href="https://github.com/daveredrum/Scan2Cap">github</a></td>
</tr>
<tr>
<td align="center">2020-8-23</td>
<td align="center">ReferIt3D</td>
<td align="center">Stanford</td>
<td align="left"><a href="https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460409.pdf" rel="nofollow">ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes</a></td>
<td align="center">ECCV '20</td>
<td align="center"><a href="https://github.com/referit3d/referit3d">github</a></td>
</tr>
<tr>
<td align="center">2019-12-18</td>
<td align="center">ScanRefer</td>
<td align="center">TUM</td>
<td align="left"><a href="https://arxiv.org/pdf/2112.10482.pdf" rel="nofollow">ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language</a></td>
<td align="center">ECCV '20</td>
<td align="center"><a href="https://daveredrum.github.io/ScanRefer/" rel="nofollow">github</a></td>
</tr>
</tbody>
</table></markdown-accessiblity-table>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing</h2><a id="user-content-contributing" class="anchor" aria-label="Permalink: Contributing" href="#contributing"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<p dir="auto">Your contributions are always welcome!</p>
<p dir="auto">I will keep some pull requests open if I'm not sure if they are awesome for 3D LLMs, you could vote for them by adding 👍 to them.</p>
<hr>
<p dir="auto">If you have any questions about this opinionated list, please get in touch at <a href="mailto:xianzheng@robots.ox.ac.uk">xianzheng@robots.ox.ac.uk</a> or Wechat ID: mxz1997112.</p>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Star History</h2><a id="user-content-star-history" class="anchor" aria-label="Permalink: Star History" href="#star-history"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<p dir="auto"><a href="https://star-history.com/#ActiveVisionLab/Awesome-LLM-3D&amp;Date" rel="nofollow"><img src="https://camo.githubusercontent.com/ff0ecc99ec768fe095656f1d397538212d46fa8ab2e5419fb0f086d1fcac0dc6/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d416374697665566973696f6e4c61622f417765736f6d652d4c4c4d2d334426747970653d44617465" alt="Star History Chart" data-canonical-src="https://api.star-history.com/svg?repos=ActiveVisionLab/Awesome-LLM-3D&amp;type=Date" style="max-width: 100%;"></a></p>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<p dir="auto">If you find this repository useful, please consider citing this paper:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{ma2024llmsstep3dworld,
      title={When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models}, 
      author={Xianzheng Ma and Yash Bhalgat and Brandon Smart and Shuai Chen and Xinghui Li and Jian Ding and Jindong Gu and Dave Zhenyu Chen and Songyou Peng and Jia-Wang Bian and Philip H Torr and Marc Pollefeys and Matthias Nießner and Ian D Reid and Angel X. Chang and Iro Laina and Victor Adrian Prisacariu},
      year={2024},
      journal={arXiv preprint arXiv:2405.10255},
}"><pre class="notranslate"><code>@misc{ma2024llmsstep3dworld,
      title={When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models}, 
      author={Xianzheng Ma and Yash Bhalgat and Brandon Smart and Shuai Chen and Xinghui Li and Jian Ding and Jindong Gu and Dave Zhenyu Chen and Songyou Peng and Jia-Wang Bian and Philip H Torr and Marc Pollefeys and Matthias Nießner and Ian D Reid and Angel X. Chang and Iro Laina and Victor Adrian Prisacariu},
      year={2024},
      journal={arXiv preprint arXiv:2405.10255},
}
</code></pre></div>
<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgement</h2><a id="user-content-acknowledgement" class="anchor" aria-label="Permalink: Acknowledgement" href="#acknowledgement"><svg data-component="Octicon" class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div>
<p dir="auto">This repo is inspired by <a href="https://github.com/Hannibal046/Awesome-LLM?tab=readme-ov-file#other-awesome-lists">Awesome-LLM</a></p>
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        class="Link--secondary underline-on-hover border-0 p-0 color-bg-transparent text-left"
        data-action="click:cookie-consent-link#showConsentManagement"
        data-analytics-event="{&quot;location&quot;:&quot;footer&quot;,&quot;action&quot;:&quot;dont_share_info&quot;,&quot;context&quot;:&quot;subfooter&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;dont_share_info_link_subfooter_footer&quot;}"
      >
        Do not share my personal information
      </button>
    </cookie-consent-link>
  </li>

      </ul>
    </nav>
  </div>
</footer>



    <ghcc-consent id="ghcc" class="position-fixed bottom-0 left-0" style="z-index: 999999"
      data-locale="en"
      data-initial-cookie-consent-allowed=""
      data-cookie-consent-required="true"
    ></ghcc-consent>




  <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden>
    <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert">
    <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path>
</svg>
    <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error">
      <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x">
    <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path>
</svg>
    </button>
    You can’t perform that action at this time.
  </div>

    <template id="site-details-dialog">
  <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open>
    <summary role="button" aria-label="Close dialog"></summary>
    <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal">
      <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog>
        <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x">
    <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path>
</svg>
      </button>
      <div class="octocat-spinner tmp-my-6 js-details-dialog-spinner"></div>
    </details-dialog>
  </details>
</template>

    <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;">
  <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;">
  </div>
</div>

    <template id="snippet-clipboard-copy-button">
  <div class="zeroclipboard-container position-absolute right-0 top-0">
    <clipboard-copy aria-label="Copy code to clipboard" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w">
      <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2 tmp-m-2">
    <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path>
</svg>
      <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2 tmp-m-2">
    <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path>
</svg>
    </clipboard-copy>
  </div>
</template>
<template id="snippet-clipboard-copy-button-unpositioned">
  <div class="zeroclipboard-container">
    <clipboard-copy aria-label="Copy code to clipboard" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w">
      <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon">
    <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path>
</svg>
      <svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none">
    <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path>
</svg>
    </clipboard-copy>
  </div>
</template>




    </div>
    <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div>
    <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div>
  </body>
</html>

