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GitHub - nv-tlabs/SCube: [NeurIPS 2024] SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" 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> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <span class="text-bold">449</span> stars </a> <a class="Link--secondary no-underline mr-3" href="/nv-tlabs/SCube/forks"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-1"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <span class="text-bold">21</span> forks </a> <a class="Link--secondary no-underline mr-3 d-inline-block" href="/nv-tlabs/SCube/branches"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" 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0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <span>Tags</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/nv-tlabs/SCube/activity"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-1"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span>Activity</span> </a> </div> <div class="d-flex flex-wrap gap-2"> <div class="flex-1"> <div data-view-component="true" class="BtnGroup d-flex"> <a href="/login?return_to=%2Fnv-tlabs%2FSCube" rel="nofollow" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"star 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2.555A1.519 1.519 0 0 1 13.482 13H2.518a1.516 1.516 0 0 1-1.263-2.36l1.703-2.554A.255.255 0 0 0 3 7.947Zm5-3.5A3.5 3.5 0 0 0 4.5 5v2.947c0 .346-.102.683-.294.97l-1.703 2.556a.017.017 0 0 0-.003.01l.001.006c0 .002.002.004.004.006l.006.004.007.001h10.964l.007-.001.006-.004.004-.006.001-.007a.017.017 0 0 0-.003-.01l-1.703-2.554a1.745 1.745 0 0 1-.294-.97V5A3.5 3.5 0 0 0 8 1.5Z"></path> </svg>Notifications </a> <tool-tip id="tooltip-ed0294ae-c363-4db9-90b3-49e2076d61c7" for="files-overview-watch-button" popover="manual" data-direction="s" data-type="description" data-view-component="true" class="sr-only position-absolute">You must be signed in to change notification settings</tool-tip> </div> <span> </span> </div> </div> </div> <nav data-pjax="#js-repo-pjax-container" aria-label="Repository" data-view-component="true" class="js-repo-nav js-sidenav-container-pjax js-responsive-underlinenav overflow-hidden UnderlineNav px-3 px-md-4 px-lg-5"> <ul data-view-component="true" 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8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path> </svg> <span data-content="Code">Code</span> <span id="code-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="Not available" data-view-component="true" class="Counter"></span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="issues-tab" href="/nv-tlabs/SCube/issues" data-tab-item="i1issues-tab" data-selected-links="repo_issues repo_labels repo_milestones /nv-tlabs/SCube/issues" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g i" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Issues","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-issue-opened UnderlineNav-octicon d-none d-sm-inline"> <path d="M8 9.5a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path><path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Z"></path> </svg> <span data-content="Issues">Issues</span> <span id="issues-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="7" data-view-component="true" class="Counter">7</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="pull-requests-tab" href="/nv-tlabs/SCube/pulls" data-tab-item="i2pull-requests-tab" data-selected-links="repo_pulls checks /nv-tlabs/SCube/pulls" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g p" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Pull requests","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-pull-request UnderlineNav-octicon d-none d-sm-inline"> <path d="M1.5 3.25a2.25 2.25 0 1 1 3 2.122v5.256a2.251 2.251 0 1 1-1.5 0V5.372A2.25 2.25 0 0 1 1.5 3.25Zm5.677-.177L9.573.677A.25.25 0 0 1 10 .854V2.5h1A2.5 2.5 0 0 1 13.5 5v5.628a2.251 2.251 0 1 1-1.5 0V5a1 1 0 0 0-1-1h-1v1.646a.25.25 0 0 1-.427.177L7.177 3.427a.25.25 0 0 1 0-.354ZM3.75 2.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm0 9.5a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Zm8.25.75a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Z"></path> </svg> <span data-content="Pull requests">Pull requests</span> <span id="pull-requests-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="0" hidden="hidden" data-view-component="true" class="Counter">0</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="actions-tab" href="/nv-tlabs/SCube/actions" data-tab-item="i3actions-tab" data-selected-links="repo_actions /nv-tlabs/SCube/actions" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g a" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Actions","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-play UnderlineNav-octicon d-none d-sm-inline"> <path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"></path> </svg> <span data-content="Actions">Actions</span> <span id="actions-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="Not available" data-view-component="true" class="Counter"></span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="projects-tab" href="/nv-tlabs/SCube/projects" data-tab-item="i4projects-tab" data-selected-links="repo_projects new_repo_project repo_project /nv-tlabs/SCube/projects" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g b" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Projects","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-table UnderlineNav-octicon d-none d-sm-inline"> <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 14.25ZM6.5 6.5v8h7.75a.25.25 0 0 0 .25-.25V6.5Zm8-1.5V1.75a.25.25 0 0 0-.25-.25H6.5V5Zm-13 1.5v7.75c0 .138.112.25.25.25H5v-8ZM5 5V1.5H1.75a.25.25 0 0 0-.25.25V5Z"></path> </svg> <span data-content="Projects">Projects</span> <span id="projects-repo-tab-count" data-pjax-replace="" data-turbo-replace="" title="0" hidden="hidden" data-view-component="true" class="Counter">0</span> </a></li> <li data-view-component="true" class="d-inline-flex"> <a id="security-tab" href="/nv-tlabs/SCube/security" data-tab-item="i5security-tab" data-selected-links="security overview alerts policy token_scanning code_scanning /nv-tlabs/SCube/security" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-hotkey="g s" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Security","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item 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</a></li> <li data-view-component="true" class="d-inline-flex"> <a id="insights-tab" href="/nv-tlabs/SCube/pulse" data-tab-item="i6insights-tab" data-selected-links="repo_graphs repo_contributors dependency_graph dependabot_updates pulse people community /nv-tlabs/SCube/pulse" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" data-analytics-event="{"category":"Underline navbar","action":"Click tab","label":"Insights","target":"UNDERLINE_NAV.TAB"}" data-view-component="true" class="UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navigation-item"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-graph UnderlineNav-octicon d-none d-sm-inline"> <path d="M1.5 1.75V13.5h13.75a.75.75 0 0 1 0 1.5H.75a.75.75 0 0 1-.75-.75V1.75a.75.75 0 0 1 1.5 0Zm14.28 2.53-5.25 5.25a.75.75 0 0 1-1.06 0L7 7.06 4.28 9.78a.751.751 0 0 1-1.042-.018.751.751 0 0 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Reconstruction using VoxSplats\u003c/h1\u003e\u003ca id=\"user-content-scube-instant-large-scale-scene-reconstruction-using-voxsplats\" class=\"anchor\" aria-label=\"Permalink: SCube: Instant Large-Scale Scene Reconstruction using VoxSplats\" href=\"#scube-instant-large-scale-scene-reconstruction-using-voxsplats\"\u003e\u003csvg 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=\"/nv-tlabs/SCube/blob/main/assets/demo.gif\"\u003e\u003cimg src=\"/nv-tlabs/SCube/raw/main/assets/demo.gif\" alt=\"\" width=\"1100\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSCube: Instant Large-Scale Scene Reconstruction using VoxSplats\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://xuanchiren.com/\" rel=\"nofollow\"\u003eXuanchi Ren\u003c/a\u003e\u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9191ce1d365b7ec247b9ae721bda91a6\"\u003e$^* $\u003c/math-renderer\u003e, \u003ca href=\"https://yifanlu0227.github.io/\" rel=\"nofollow\"\u003eYifan Lu\u003c/a\u003e\u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9191ce1d365b7ec247b9ae721bda91a6\"\u003e$^* $\u003c/math-renderer\u003e, \u003ca href=\"https://scholar.google.com/citations?user=XcxDA14AAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003eHanxue Liang\u003c/a\u003e, \u003ca href=\"https://zhangjiewu.github.io/\" rel=\"nofollow\"\u003eZhangjie Wu\u003c/a\u003e, \u003ca href=\"http://www.cs.toronto.edu/~linghuan/\" rel=\"nofollow\"\u003eHuan Ling\u003c/a\u003e, \u003ca href=\"https://www.linkedin.com/in/nvidia-mikechen/\" rel=\"nofollow\"\u003eMike Chen\u003c/a\u003e, \u003ca href=\"https://www.cs.utoronto.ca/~fidler/\" rel=\"nofollow\"\u003eSanja Fidler\u003c/a\u003e, \u003ca href=\"https://www.fwilliams.info/\" rel=\"nofollow\"\u003eFrancis Williams\u003c/a\u003e, \u003ca href=\"https://huangjh-pub.github.io/\" rel=\"nofollow\"\u003eJiahui Huang\u003c/a\u003e \u003cbr\u003e\n* indicates equal contribution \u003cbr\u003e\n\u003cstrong\u003e\u003ca href=\"https://research.nvidia.com/labs/toronto-ai/scube/\" rel=\"nofollow\"\u003e[Project Page]\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNews\u003c/h2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-label=\"Permalink: News\" href=\"#news\"\u003e\u003csvg 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\u003e2024-12-11: Also check out our latest research \u003ca href=\"https://research.nvidia.com/labs/toronto-ai/infinicube/\" rel=\"nofollow\"\u003eInfiniCube\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e2024-12-11: Code released! We have also integrated \u003ca href=\"https://research.nvidia.com/labs/toronto-ai/xcube/\" rel=\"nofollow\"\u003eXCube\u003c/a\u003e into this repository for easier use. For detailed instructions, please refer to the \u003ca href=\"/nv-tlabs/SCube/blob/main/XCUBE.md\"\u003eXCube Instruction\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg 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 class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir SCube-release # wrap it in a folder\ncd SCube-release\ngit clone https://github.com/nv-tlabs/SCube.git \nmkdir wandb\nmkdir waymo_tfrecords \ncd SCube\"\u003e\u003cpre\u003emkdir SCube-release \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e wrap it in a folder\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e SCube-release\ngit clone https://github.com/nv-tlabs/SCube.git \nmkdir wandb\nmkdir waymo_tfrecords \n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e SCube\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e(Optional) Install libMamba for a huge quality of life improvement when using Conda\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda update -n base conda\nconda install -n base conda-libmamba-solver\nconda config --set solver libmamba\"\u003e\u003cpre\u003econda update -n base conda\nconda install -n base conda-libmamba-solver\nconda config --set solver libmamba\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInstall the conda environment\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda env create -f environment.yml\nconda activate scube\nmim install \u0026quot;mmcv\u0026gt;=2.0.0\u0026quot;\npip install \u0026quot;mmsegmentation\u0026gt;=1.0.0\u0026quot;\"\u003e\u003cpre\u003econda env create -f environment.yml\nconda activate scube\nmim install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emmcv\u0026gt;=2.0.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emmsegmentation\u0026gt;=1.0.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup WandB\u003c/h3\u003e\u003ca id=\"user-content-setup-wandb\" class=\"anchor\" aria-label=\"Permalink: Setup WandB\" href=\"#setup-wandb\"\u003e\u003csvg 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\"\u003eOur training script highly relies on \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWandB\u003c/a\u003e. Please register an account for \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWandB\u003c/a\u003e first and get your \u003ccode\u003eAPI_key\u003c/code\u003e. Then you can setup for your machine by running this command in the terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"wandb login # requires your API key\"\u003e\u003cpre\u003ewandb login \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e requires your API key\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData Preparation\u003c/h2\u003e\u003ca id=\"user-content-data-preparation\" class=\"anchor\" aria-label=\"Permalink: Data Preparation\" href=\"#data-preparation\"\u003e\u003csvg 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 class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData Split Downloading\u003c/h3\u003e\u003ca id=\"user-content-data-split-downloading\" class=\"anchor\" aria-label=\"Permalink: Data Split Downloading\" href=\"#data-split-downloading\"\u003e\u003csvg 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\"\u003eFirst download our splits of waymo dataset.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"gdown https://drive.google.com/drive/folders/1d8V4aoHsDf5U0-SH_VY2MOZJdYvOG8TM -O ../waymo_split --folder\"\u003e\u003cpre\u003egdown https://drive.google.com/drive/folders/1d8V4aoHsDf5U0-SH_VY2MOZJdYvOG8TM -O ../waymo_split --folder\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWaymo Dataset Downloading\u003c/h3\u003e\u003ca id=\"user-content-waymo-dataset-downloading\" class=\"anchor\" aria-label=\"Permalink: Waymo Dataset Downloading\" href=\"#waymo-dataset-downloading\"\u003e\u003csvg 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\"\u003eDownload the all the training \u0026amp; validation clips from \u003ca href=\"https://waymo.com/open/download/\" rel=\"nofollow\"\u003ewaymo perception dataset v1.4.2\u003c/a\u003e to the \u003ccode\u003eSCube-release/waymo_tfrecords\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you have \u003ccode\u003esudo\u003c/code\u003e, you can use \u003ca href=\"https://cloud.google.com/storage/docs/discover-object-storage-gcloud\" rel=\"nofollow\"\u003egcloud\u003c/a\u003e to download them from terminal.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\u003cspan\u003egcloud installation (need sudo) and downloading from terminal\u003c/span\u003e\u003c/summary\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"sudo apt-get update\nsudo apt-get install apt-transport-https ca-certificates gnupg curl\ncurl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg\necho \u0026quot;deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main\u0026quot; | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list\nsudo apt-get update \u0026amp;\u0026amp; sudo apt-get install google-cloud-cli\"\u003e\u003cpre\u003esudo apt-get update\nsudo apt-get install apt-transport-https ca-certificates gnupg curl\ncurl https://packages.cloud.google.com/apt/doc/apt-key.gpg \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edeb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list\nsudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sudo apt-get install google-cloud-cli\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen you can login your google account and download the above tfrecords via\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# or use `gcloud init --no-launch-browser` if you are in a remote terminal session\ngcloud init \nbash datagen/download_waymo.sh datagen/waymo_all.json ../waymo_tfrecords\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or use `gcloud init --no-launch-browser` if you are in a remote terminal session\u003c/span\u003e\ngcloud init \nbash datagen/download_waymo.sh datagen/waymo_all.json ../waymo_tfrecords\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003eAfter downloading tfrecord files, we expect a folder structure as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"SCube-release\n|-- SCube\n| `-- ...\n|-- waymo_split\n| `-- ...\n`-- waymo_tfrecords\n |-- segment-10247954040621004675_2180_000_2200_000_with_camera_labels.tfrecord\n |-- segment-11379226583756500423_6230_810_6250_810_with_camera_labels.tfrecord\n |-- ...\n `-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels.tfrecord\"\u003e\u003cpre\u003eSCube-release\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- SCube\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e-- ...\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- waymo_split\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e-- ...\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e-- waymo_tfrecords\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- segment-10247954040621004675_2180_000_2200_000_with_camera_labels.tfrecord\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- segment-11379226583756500423_6230_810_6250_810_with_camera_labels.tfrecord\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- ...\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels.tfrecord\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eIf you download the tfrecord files from the console, you will have prefixes like \u003ccode\u003eindividual_files_training_\u003c/code\u003e or \u003ccode\u003eindividual_files_validation_\u003c/code\u003e. Make sure these prefixes are removed before further processing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData Processing\u003c/h3\u003e\u003ca id=\"user-content-data-processing\" class=\"anchor\" aria-label=\"Permalink: Data Processing\" href=\"#data-processing\"\u003e\u003csvg 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\"\u003eWe first extract the image, LiDAR, pose, intrinsic, annotation files out of tfrecords, which will be saved in \u003ccode\u003e../waymo_ns\u003c/code\u003e. If you have multiple CPU cores, you can set \u003ccode\u003e--num_workers\u003c/code\u003e to your core number for parallelization.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python datagen/waymo2nerfstudio.py -i ../waymo_tfrecords -o ../waymo_ns [--num_workers YOUR_CPU_CORE]\"\u003e\u003cpre\u003epython datagen/waymo2nerfstudio.py -i ../waymo_tfrecords -o ../waymo_ns [--num_workers YOUR_CPU_CORE]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe then process the raw data into webdataset format required by SCube training while infering SegFormer for sky mask and Metric3Dv2 for GT depth. The results will be saved in \u003ccode\u003e../waymo_webdataset\u003c/code\u003e. Note that this step is very time-comsuming for 1000 clips x 198 frames x 5 cameras, which can take over 1 day on 8x A100 GPUs.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eYou might encounter \u003ccode\u003eAssertionError: MMCV==2.2.0 is used but incompatible. Please install mmcv\u0026gt;=2.0.0rc4.\u003c/code\u003e when using mmcv. \u003ca href=\"https://github.com/open-mmlab/mmsegmentation/issues/3729#issuecomment-2225475821\" data-hovercard-type=\"issue\" data-hovercard-url=\"/open-mmlab/mmsegmentation/issues/3729/hovercard\"\u003eSolution here\u003c/a\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eYou might encounter \u003ccode\u003eRuntimeError: Failed to find function: mono.model.backbones.vit_large_reg\u003c/code\u003e when using Metric3D v2. \u003ca href=\"https://github.com/YvanYin/Metric3D/issues/151#issuecomment-2333039827\" data-hovercard-type=\"issue\" data-hovercard-url=\"/YvanYin/Metric3D/issues/151/hovercard\"\u003eSolution here\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# single GPU\npython datagen/nerfstudio2webdataset.py -i ../waymo_ns -o ../waymo_webdataset\n\n# multiple GPU\ntorchrun --nnodes=1 --nproc-per-node=8 'datagen/nerfstudio2webdataset.py' -i ../waymo_ns -o ../waymo_webdataset\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e single GPU\u003c/span\u003e\npython datagen/nerfstudio2webdataset.py -i ../waymo_ns -o ../waymo_webdataset\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e multiple GPU\u003c/span\u003e\ntorchrun --nnodes=1 --nproc-per-node=8 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003edatagen/nerfstudio2webdataset.py\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e -i ../waymo_ns -o ../waymo_webdataset\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNow the \u003ccode\u003e../waymo_ns\u003c/code\u003e and \u003ccode\u003e../waymo_tfrecords\u003c/code\u003e will not be used anymore. We expect the folder structure like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"SCube-release\n|-- SCube\n| `-- ...\n|-- waymo_split\n| `-- ...\n`-- waymo_webdataset\n `-- ...\"\u003e\u003cpre\u003eSCube-release\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- SCube\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e-- ...\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- waymo_split\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e-- ...\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e-- waymo_webdataset\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e-- ...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGT Voxel Downloading\u003c/h3\u003e\u003ca id=\"user-content-gt-voxel-downloading\" class=\"anchor\" aria-label=\"Permalink: GT Voxel Downloading\" href=\"#gt-voxel-downloading\"\u003e\u003csvg 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\"\u003eWe provide the webdataset-format files of ground-truth voxels in \u003ca href=\"https://drive.google.com/file/d/1o5hC1SVxrItMEWwKEy4FYgVJGXpE72D4/view?usp=drive_link\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e (need request). After getting approval, download and uncompress it in \u003ccode\u003eSCube-release/waymo_webdataset\u003c/code\u003e along with other attributes.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo visualize the ground-truth voxel data, which is stored in point cloud format, you can run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python inference/visualize_gt_pc.py -p \u0026lt;GT_VOXEL_TAR_FILE\u0026gt;\"\u003e\u003cpre\u003epython inference/visualize_gt_pc.py -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eGT_VOXEL_TAR_FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Training\u003c/h2\u003e\u003ca id=\"user-content-model-training\" class=\"anchor\" aria-label=\"Permalink: Model Training\" href=\"#model-training\"\u003e\u003csvg 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 class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVAE Training\u003c/h3\u003e\u003ca id=\"user-content-vae-training\" class=\"anchor\" aria-label=\"Permalink: VAE Training\" href=\"#vae-training\"\u003e\u003csvg 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 class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Coarse stage VAE\npython train.py configs/waymo_scube/train_vae_64x64x64_dense_height_down2_residual.yaml --wname train_vae_64x64x64_dense_height_down2_residual --max_epochs 10 --gpus 8 --eval_interval 1\n\n# Fine stage VAE\npython train.py configs/waymo_scube/train_vae_256x256x128_sparse.yaml --wname train_vae_256x256x128_sparse --max_epochs 10 --gpus 8 --eval_interval 1\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Coarse stage VAE\u003c/span\u003e\npython train.py configs/waymo_scube/train_vae_64x64x64_dense_height_down2_residual.yaml --wname train_vae_64x64x64_dense_height_down2_residual --max_epochs 10 --gpus 8 --eval_interval 1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Fine stage VAE\u003c/span\u003e\npython train.py configs/waymo_scube/train_vae_256x256x128_sparse.yaml --wname train_vae_256x256x128_sparse --max_epochs 10 --gpus 8 --eval_interval 1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGeometry Reconstruction Training (Latent Voxel Diffusion Model)\u003c/h3\u003e\u003ca id=\"user-content-geometry-reconstruction-training-latent-voxel-diffusion-model\" class=\"anchor\" aria-label=\"Permalink: Geometry Reconstruction Training (Latent Voxel Diffusion Model)\" href=\"#geometry-reconstruction-training-latent-voxel-diffusion-model\"\u003e\u003csvg 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 class=\"markdown-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003eYou need to modify the \u003ccode\u003evae_checkpoint\u003c/code\u003e in \u003ccode\u003econfigs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml\u003c/code\u003e and \u003ccode\u003econfigs/waymo_scube/train_diffusion_256x256x128_sparse.yaml\u003c/code\u003e to your own experiments. In you wandb webpage, you can find a unique \"run id\" for your experiment and replace the \u003ccode\u003e[YOUR_RUN_ID]\u003c/code\u003e in the yaml file.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Image conditioned voxel diffusion\npython train.py configs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml --wname train_diffusion_64x64x64_image_cond --max_epochs 40 --gpus 8 --eval_interval 1\n\n# Semantic conditioned upsampling voxel diffusion\npython train.py configs/waymo_scube/train_diffusion_256x256x128_sparse.yaml --wname train_diffusion_256x256x128_sparse --max_epochs 20 --gpus 8 --eval_interval 1\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Image conditioned voxel diffusion\u003c/span\u003e\npython train.py configs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml --wname train_diffusion_64x64x64_image_cond --max_epochs 40 --gpus 8 --eval_interval 1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Semantic conditioned upsampling voxel diffusion\u003c/span\u003e\npython train.py configs/waymo_scube/train_diffusion_256x256x128_sparse.yaml --wname train_diffusion_256x256x128_sparse --max_epochs 20 --gpus 8 --eval_interval 1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAppearance Reconstruction Training (GSM, \u003cem\u003eG\u003c/em\u003eaussian \u003cem\u003eS\u003c/em\u003eplatting \u003cem\u003eM\u003c/em\u003eodel)\u003c/h3\u003e\u003ca id=\"user-content-appearance-reconstruction-training-gsm-gaussian-splatting-model\" class=\"anchor\" aria-label=\"Permalink: Appearance Reconstruction Training (GSM, Gaussian Splatting Model)\" href=\"#appearance-reconstruction-training-gsm-gaussian-splatting-model\"\u003e\u003csvg 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 class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python train.py configs/waymo_scube/train_gsm_unet3d_view3.yaml --wname train_gsm_unet3d_view3 --max_epochs 30 --gpus 8 --eval_interval 1\"\u003e\u003cpre\u003epython train.py configs/waymo_scube/train_gsm_unet3d_view3.yaml --wname train_gsm_unet3d_view3 --max_epochs 30 --gpus 8 --eval_interval 1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Inference\u003c/h2\u003e\u003ca id=\"user-content-model-inference\" class=\"anchor\" aria-label=\"Permalink: Model Inference\" href=\"#model-inference\"\u003e\u003csvg 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\u003cstrong\u003eInference VAE\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# replace nvidia-toronto to your own wandb account!\npython inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_64x64x64_dense_height_down2_residual\npython inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_256x256x128_sparse\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e replace nvidia-toronto to your own wandb account!\u003c/span\u003e\npython inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_64x64x64_dense_height_down2_residual\npython inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_256x256x128_sparse\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will generate a folder \u003ccode\u003e../vae_output_waymo_wds/\u003c/code\u003e, storing predicted \u0026amp; GT grid + semantics. Specify \u003ccode\u003e--ckpt_vae\u003c/code\u003e to the experiment name.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInference Diffusion\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# replace nvidia-toronto to your own wandb account!\npython inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond\npython inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e replace nvidia-toronto to your own wandb account!\u003c/span\u003e\npython inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond\npython inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will generate a folder \u003ccode\u003e../diffusion_output_waymo_wds/\u003c/code\u003e, storing predicted \u0026amp; GT voxel grid with semantics.\nSpecify \u003ccode\u003e--ckpt_dm\u003c/code\u003e to the experiment name.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInference GSM\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# replace nvidia-toronto to your own wandb account!\npython inference/gaussian_feedforward.py none --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 [--save_gs]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e replace nvidia-toronto to your own wandb account!\u003c/span\u003e\npython inference/gaussian_feedforward.py none --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 [--save_gs]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will generate a folder \u003ccode\u003e../diffusion_output_waymo_wds/\u003c/code\u003e, storing predicted \u0026amp; GT grid + semantics.\nSpecify \u003ccode\u003e--ckpt_gsm\u003c/code\u003e to the experiment name. Add \u003ccode\u003e[--save_gs]\u003c/code\u003e to save the 3D Gaussians.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInference Full Pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# replace nvidia-toronto to your own wandb account!\npython inference/diffusion_cascading_gsm.py none \\\n --use_ema --use_ddim --ddim_step 100 \\\n --ckpt_dm_c wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond \\\n --ckpt_dm_f wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse \\\n --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 \\\n --input_frame_offsets [0] \\\n --sup_frame_offsets [0,5,10] \\\n --split val \\\n --val_starting_frame 0\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e replace nvidia-toronto to your own wandb account!\u003c/span\u003e\npython inference/diffusion_cascading_gsm.py none \\\n --use_ema --use_ddim --ddim_step 100 \\\n --ckpt_dm_c wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond \\\n --ckpt_dm_f wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse \\\n --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 \\\n --input_frame_offsets [0] \\\n --sup_frame_offsets [0,5,10] \\\n --split val \\\n --val_starting_frame 0\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVisualize the Inference Result\u003c/h2\u003e\u003ca id=\"user-content-visualize-the-inference-result\" class=\"anchor\" aria-label=\"Permalink: Visualize the Inference Result\" href=\"#visualize-the-inference-result\"\u003e\u003csvg 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\u003cstrong\u003eVisualize Grids\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/nv-tlabs/SCube/blob/main/assets/vis-pair.png\"\u003e\u003cimg src=\"/nv-tlabs/SCube/raw/main/assets/vis-pair.png\" alt=\"\" width=\"900\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn \u003ccode\u003e../vae_output_waymo_wds/\u003c/code\u003e and \u003ccode\u003e../diffusion_output_waymo_wds/\u003c/code\u003e and \u003ccode\u003e../cascading_diffusion_output_waymo_wds\u003c/code\u003e, you can find some folders containing \u003ccode\u003ex.pt\u003c/code\u003e and \u003ccode\u003ex_gt.pt\u003c/code\u003e, where \u003ccode\u003ex\u003c/code\u003e is the sample index for your inference. Run the following command to launch a interactive visualizer (even on a remote-ssh machine) in a web browser.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python inference/visualize_grid_compare.py -p \u0026lt;PT_FILES_FOLDER\u0026gt; [-t voxel]\"\u003e\u003cpre\u003epython inference/visualize_grid_compare.py -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePT_FILES_FOLDER\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [-t voxel]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ewhere \u003ccode\u003e-t\u003c/code\u003e is available for \u003ccode\u003evoxel\u003c/code\u003e or \u003ccode\u003epc\u003c/code\u003e. Do not use \u003ccode\u003evoxel\u003c/code\u003e for the fine stage, which is very heavy for rendering. It would be easy to use it on a remote machine with VSCode; VSCode will forward the port automatically for you.\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eVisualize 3DGS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/nv-tlabs/SCube/blob/main/assets/vis-gs.png\"\u003e\u003cimg src=\"/nv-tlabs/SCube/raw/main/assets/vis-gs.png\" alt=\"\" width=\"900\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn \u003ccode\u003e../splat_output_waymo_wds\u003c/code\u003e and \u003ccode\u003ecascading_diffusion_output_waymo_wds\u003c/code\u003e, you can find some folder containing \u003ccode\u003ex_splat.pkl\u003c/code\u003e, where \u003ccode\u003ex\u003c/code\u003e is the sample index for your inferenece. You can visualize the 3DGS with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python inference/visualize_3dgs_pkl.py -p \u0026lt;3DGS_PKL_FILE\u0026gt;\"\u003e\u003cpre\u003epython inference/visualize_3dgs_pkl.py -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e3DGS_PKL_FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eShortcut\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou could install this project to create short cut for visualization.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"poetry install \"\u003e\u003cpre\u003epoetry install \u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen you will have command \u003ccode\u003evis-pair\u003c/code\u003e equivalent to \u003ccode\u003epython inference/visualize_grid_compare.py\u003c/code\u003e, \u003ccode\u003evis-gs\u003c/code\u003e equivalent to \u003ccode\u003epython inference/visualize_3dgs_pkl.py\u003c/code\u003e and \u003ccode\u003evis-gt-pc\u003c/code\u003e equivalent to \u003ccode\u003einference/visualize_gt_pc.py\u003c/code\u003e. You can run visualization as\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"vis-pair -p \u0026lt;PT_FILES_FOLDER\u0026gt;\nvis-gs -p \u0026lt;3DGS_PKL_FILE\u0026gt;\nvis-gt-pc -p \u0026lt;GT_VOXEL_TAR_FILE\u0026gt;\"\u003e\u003cpre\u003evis-pair -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePT_FILES_FOLDER\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nvis-gs -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e3DGS_PKL_FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nvis-gt-pc -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eGT_VOXEL_TAR_FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg 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\"\u003eCopyright © 2024, NVIDIA Corporation \u0026amp; affiliates. All rights reserved.\nThis work is made available under the \u003ca href=\"/nv-tlabs/SCube/blob/main/LICENSE.txt\"\u003eNvidia Source Code License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRelated Works\u003c/h2\u003e\u003ca id=\"user-content-related-works\" class=\"anchor\" aria-label=\"Permalink: Related Works\" href=\"#related-works\"\u003e\u003csvg 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\u003eLu et al. 2024. \u003ca href=\"https://research.nvidia.com/labs/toronto-ai/infinicube/\" rel=\"nofollow\"\u003eInfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eRen et al. 2024. \u003ca href=\"https://research.nvidia.com/labs/toronto-ai/xcube\" rel=\"nofollow\"\u003eXCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eWilliams et al. 2024. \u003ca href=\"https://arxiv.org/abs/2407.01781\" rel=\"nofollow\"\u003e𝑓VDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\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 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 based on \u003ca href=\"https://github.com/nv-tlabs/XCube\"\u003ehttps://github.com/nv-tlabs/XCube\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 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 class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{\n ren2024scube,\n title={SCube: Instant Large-Scale Scene Reconstruction using VoxSplats},\n author={Ren, Xuanchi and Lu, Yifan and Liang, Hanxue and Wu, Jay Zhangjie and Ling, Huan and Chen, Mike and Fidler, Sanja annd Williams, Francis and Huang, Jiahui},\n booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},\n year={2024},\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{\n ren2024scube,\n title={SCube: Instant Large-Scale Scene Reconstruction using VoxSplats},\n author={Ren, Xuanchi and Lu, Yifan and Liang, Hanxue and Wu, Jay Zhangjie and Ling, Huan and Chen, Mike and Fidler, Sanja annd Williams, Francis and Huang, Jiahui},\n booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},\n year={2024},\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"SCube: Instant Large-Scale Scene Reconstruction using VoxSplats","anchor":"scube-instant-large-scale-scene-reconstruction-using-voxsplats","htmlText":"SCube: Instant Large-Scale Scene Reconstruction using VoxSplats"},{"level":2,"text":"News","anchor":"news","htmlText":"News"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Setup WandB","anchor":"setup-wandb","htmlText":"Setup WandB"},{"level":2,"text":"Data Preparation","anchor":"data-preparation","htmlText":"Data Preparation"},{"level":3,"text":"Data Split Downloading","anchor":"data-split-downloading","htmlText":"Data Split Downloading"},{"level":3,"text":"Waymo Dataset Downloading","anchor":"waymo-dataset-downloading","htmlText":"Waymo Dataset Downloading"},{"level":3,"text":"Data Processing","anchor":"data-processing","htmlText":"Data Processing"},{"level":3,"text":"GT Voxel Downloading","anchor":"gt-voxel-downloading","htmlText":"GT Voxel Downloading"},{"level":2,"text":"Model Training","anchor":"model-training","htmlText":"Model Training"},{"level":3,"text":"VAE Training","anchor":"vae-training","htmlText":"VAE Training"},{"level":3,"text":"Geometry Reconstruction Training (Latent Voxel Diffusion Model)","anchor":"geometry-reconstruction-training-latent-voxel-diffusion-model","htmlText":"Geometry Reconstruction Training (Latent Voxel Diffusion Model)"},{"level":3,"text":"Appearance Reconstruction Training (GSM, Gaussian Splatting Model)","anchor":"appearance-reconstruction-training-gsm-gaussian-splatting-model","htmlText":"Appearance Reconstruction Training (GSM, Gaussian Splatting Model)"},{"level":2,"text":"Model Inference","anchor":"model-inference","htmlText":"Model Inference"},{"level":2,"text":"Visualize the Inference Result","anchor":"visualize-the-inference-result","htmlText":"Visualize the Inference Result"},{"level":2,"text":"License","anchor":"license","htmlText":"License"},{"level":2,"text":"Related Works","anchor":"related-works","htmlText":"Related Works"},{"level":2,"text":"Acknowledgement","anchor":"acknowledgement","htmlText":"Acknowledgement"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fnv-tlabs%2FSCube"}},{"displayName":"LICENSE.txt","repoName":"SCube","refName":"main","path":"LICENSE.txt","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fnv-tlabs%2FSCube"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> 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d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">SCube: Instant Large-Scale Scene Reconstruction using VoxSplats</h1><a id="user-content-scube-instant-large-scale-scene-reconstruction-using-voxsplats" class="anchor" aria-label="Permalink: SCube: Instant Large-Scale Scene Reconstruction using VoxSplats" href="#scube-instant-large-scale-scene-reconstruction-using-voxsplats"><svg 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> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/nv-tlabs/SCube/blob/main/assets/demo.gif"><img src="/nv-tlabs/SCube/raw/main/assets/demo.gif" alt="" width="1100" data-animated-image="" style="max-width: 100%;"></a> </div> <p dir="auto"><strong>SCube: Instant Large-Scale Scene Reconstruction using VoxSplats</strong><br> <a href="https://xuanchiren.com/" rel="nofollow">Xuanchi Ren</a><math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9191ce1d365b7ec247b9ae721bda91a6">$^* $</math-renderer>, <a href="https://yifanlu0227.github.io/" rel="nofollow">Yifan Lu</a><math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9191ce1d365b7ec247b9ae721bda91a6">$^* $</math-renderer>, <a href="https://scholar.google.com/citations?user=XcxDA14AAAAJ&hl=en" rel="nofollow">Hanxue Liang</a>, <a href="https://zhangjiewu.github.io/" rel="nofollow">Zhangjie Wu</a>, <a href="http://www.cs.toronto.edu/~linghuan/" rel="nofollow">Huan Ling</a>, <a href="https://www.linkedin.com/in/nvidia-mikechen/" rel="nofollow">Mike Chen</a>, <a href="https://www.cs.utoronto.ca/~fidler/" rel="nofollow">Sanja Fidler</a>, <a href="https://www.fwilliams.info/" rel="nofollow">Francis Williams</a>, <a href="https://huangjh-pub.github.io/" rel="nofollow">Jiahui Huang</a> <br> * indicates equal contribution <br> <strong><a href="https://research.nvidia.com/labs/toronto-ai/scube/" rel="nofollow">[Project Page]</a></strong></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 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>2024-12-11: Also check out our latest research <a href="https://research.nvidia.com/labs/toronto-ai/infinicube/" rel="nofollow">InfiniCube</a>.</li> <li>2024-12-11: Code released! We have also integrated <a href="https://research.nvidia.com/labs/toronto-ai/xcube/" rel="nofollow">XCube</a> into this repository for easier use. For detailed instructions, please refer to the <a href="/nv-tlabs/SCube/blob/main/XCUBE.md">XCube Instruction</a>.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg 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> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir SCube-release # wrap it in a folder cd SCube-release git clone https://github.com/nv-tlabs/SCube.git mkdir wandb mkdir waymo_tfrecords cd SCube"><pre>mkdir SCube-release <span class="pl-c"><span class="pl-c">#</span> wrap it in a folder</span> <span class="pl-c1">cd</span> SCube-release git clone https://github.com/nv-tlabs/SCube.git mkdir wandb mkdir waymo_tfrecords <span class="pl-c1">cd</span> SCube</pre></div> <p dir="auto"><strong>(Optional) Install libMamba for a huge quality of life improvement when using Conda</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda update -n base conda conda install -n base conda-libmamba-solver conda config --set solver libmamba"><pre>conda update -n base conda conda install -n base conda-libmamba-solver conda config --set solver libmamba</pre></div> <p dir="auto"><strong>Install the conda environment</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda env create -f environment.yml conda activate scube mim install "mmcv>=2.0.0" pip install "mmsegmentation>=1.0.0""><pre>conda env create -f environment.yml conda activate scube mim install <span class="pl-s"><span class="pl-pds">"</span>mmcv>=2.0.0<span class="pl-pds">"</span></span> pip install <span class="pl-s"><span class="pl-pds">"</span>mmsegmentation>=1.0.0<span class="pl-pds">"</span></span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Setup WandB</h3><a id="user-content-setup-wandb" class="anchor" aria-label="Permalink: Setup WandB" href="#setup-wandb"><svg 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">Our training script highly relies on <a href="https://wandb.ai/" rel="nofollow">WandB</a>. Please register an account for <a href="https://wandb.ai/" rel="nofollow">WandB</a> first and get your <code>API_key</code>. Then you can setup for your machine by running this command in the terminal:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="wandb login # requires your API key"><pre>wandb login <span class="pl-c"><span class="pl-c">#</span> requires your API key</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Data Preparation</h2><a id="user-content-data-preparation" class="anchor" aria-label="Permalink: Data Preparation" href="#data-preparation"><svg 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> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Data Split Downloading</h3><a id="user-content-data-split-downloading" class="anchor" aria-label="Permalink: Data Split Downloading" href="#data-split-downloading"><svg 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">First download our splits of waymo dataset.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="gdown https://drive.google.com/drive/folders/1d8V4aoHsDf5U0-SH_VY2MOZJdYvOG8TM -O ../waymo_split --folder"><pre>gdown https://drive.google.com/drive/folders/1d8V4aoHsDf5U0-SH_VY2MOZJdYvOG8TM -O ../waymo_split --folder</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Waymo Dataset Downloading</h3><a id="user-content-waymo-dataset-downloading" class="anchor" aria-label="Permalink: Waymo Dataset Downloading" href="#waymo-dataset-downloading"><svg 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">Download the all the training & validation clips from <a href="https://waymo.com/open/download/" rel="nofollow">waymo perception dataset v1.4.2</a> to the <code>SCube-release/waymo_tfrecords</code>.</p> <p dir="auto">If you have <code>sudo</code>, you can use <a href="https://cloud.google.com/storage/docs/discover-object-storage-gcloud" rel="nofollow">gcloud</a> to download them from terminal.</p> <details> <summary><span>gcloud installation (need sudo) and downloading from terminal</span></summary> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="sudo apt-get update sudo apt-get install apt-transport-https ca-certificates gnupg curl curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list sudo apt-get update && sudo apt-get install google-cloud-cli"><pre>sudo apt-get update sudo apt-get install apt-transport-https ca-certificates gnupg curl curl https://packages.cloud.google.com/apt/doc/apt-key.gpg <span class="pl-k">|</span> sudo gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg <span class="pl-c1">echo</span> <span class="pl-s"><span class="pl-pds">"</span>deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main<span class="pl-pds">"</span></span> <span class="pl-k">|</span> sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list sudo apt-get update <span class="pl-k">&&</span> sudo apt-get install google-cloud-cli</pre></div> <p dir="auto">Then you can login your google account and download the above tfrecords via</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# or use `gcloud init --no-launch-browser` if you are in a remote terminal session gcloud init bash datagen/download_waymo.sh datagen/waymo_all.json ../waymo_tfrecords"><pre><span class="pl-c"><span class="pl-c">#</span> or use `gcloud init --no-launch-browser` if you are in a remote terminal session</span> gcloud init bash datagen/download_waymo.sh datagen/waymo_all.json ../waymo_tfrecords</pre></div> </details> <p dir="auto">After downloading tfrecord files, we expect a folder structure as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="SCube-release |-- SCube | `-- ... |-- waymo_split | `-- ... `-- waymo_tfrecords |-- segment-10247954040621004675_2180_000_2200_000_with_camera_labels.tfrecord |-- segment-11379226583756500423_6230_810_6250_810_with_camera_labels.tfrecord |-- ... `-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels.tfrecord"><pre>SCube-release <span class="pl-k">|</span>-- SCube <span class="pl-k">|</span> <span class="pl-s"><span class="pl-pds">`</span>-- ...</span> <span class="pl-s"><span class="pl-k">|</span>-- waymo_split</span> <span class="pl-s"><span class="pl-k">|</span> <span class="pl-pds">`</span></span>-- ... <span class="pl-s"><span class="pl-pds">`</span>-- waymo_tfrecords</span> <span class="pl-s"> <span class="pl-k">|</span>-- segment-10247954040621004675_2180_000_2200_000_with_camera_labels.tfrecord</span> <span class="pl-s"> <span class="pl-k">|</span>-- segment-11379226583756500423_6230_810_6250_810_with_camera_labels.tfrecord</span> <span class="pl-s"> <span class="pl-k">|</span>-- ...</span> <span class="pl-s"> <span class="pl-pds">`</span></span>-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels.tfrecord</pre></div> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">If you download the tfrecord files from the console, you will have prefixes like <code>individual_files_training_</code> or <code>individual_files_validation_</code>. Make sure these prefixes are removed before further processing.</p> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Data Processing</h3><a id="user-content-data-processing" class="anchor" aria-label="Permalink: Data Processing" href="#data-processing"><svg 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">We first extract the image, LiDAR, pose, intrinsic, annotation files out of tfrecords, which will be saved in <code>../waymo_ns</code>. If you have multiple CPU cores, you can set <code>--num_workers</code> to your core number for parallelization.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python datagen/waymo2nerfstudio.py -i ../waymo_tfrecords -o ../waymo_ns [--num_workers YOUR_CPU_CORE]"><pre>python datagen/waymo2nerfstudio.py -i ../waymo_tfrecords -o ../waymo_ns [--num_workers YOUR_CPU_CORE]</pre></div> <p dir="auto">We then process the raw data into webdataset format required by SCube training while infering SegFormer for sky mask and Metric3Dv2 for GT depth. The results will be saved in <code>../waymo_webdataset</code>. Note that this step is very time-comsuming for 1000 clips x 198 frames x 5 cameras, which can take over 1 day on 8x A100 GPUs.</p> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">You might encounter <code>AssertionError: MMCV==2.2.0 is used but incompatible. Please install mmcv>=2.0.0rc4.</code> when using mmcv. <a href="https://github.com/open-mmlab/mmsegmentation/issues/3729#issuecomment-2225475821" data-hovercard-type="issue" data-hovercard-url="/open-mmlab/mmsegmentation/issues/3729/hovercard">Solution here</a>.</p> </div> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">You might encounter <code>RuntimeError: Failed to find function: mono.model.backbones.vit_large_reg</code> when using Metric3D v2. <a href="https://github.com/YvanYin/Metric3D/issues/151#issuecomment-2333039827" data-hovercard-type="issue" data-hovercard-url="/YvanYin/Metric3D/issues/151/hovercard">Solution here</a></p> </div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# single GPU python datagen/nerfstudio2webdataset.py -i ../waymo_ns -o ../waymo_webdataset # multiple GPU torchrun --nnodes=1 --nproc-per-node=8 'datagen/nerfstudio2webdataset.py' -i ../waymo_ns -o ../waymo_webdataset"><pre><span class="pl-c"><span class="pl-c">#</span> single GPU</span> python datagen/nerfstudio2webdataset.py -i ../waymo_ns -o ../waymo_webdataset <span class="pl-c"><span class="pl-c">#</span> multiple GPU</span> torchrun --nnodes=1 --nproc-per-node=8 <span class="pl-s"><span class="pl-pds">'</span>datagen/nerfstudio2webdataset.py<span class="pl-pds">'</span></span> -i ../waymo_ns -o ../waymo_webdataset</pre></div> <p dir="auto">Now the <code>../waymo_ns</code> and <code>../waymo_tfrecords</code> will not be used anymore. We expect the folder structure like:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="SCube-release |-- SCube | `-- ... |-- waymo_split | `-- ... `-- waymo_webdataset `-- ..."><pre>SCube-release <span class="pl-k">|</span>-- SCube <span class="pl-k">|</span> <span class="pl-s"><span class="pl-pds">`</span>-- ...</span> <span class="pl-s"><span class="pl-k">|</span>-- waymo_split</span> <span class="pl-s"><span class="pl-k">|</span> <span class="pl-pds">`</span></span>-- ... <span class="pl-s"><span class="pl-pds">`</span>-- waymo_webdataset</span> <span class="pl-s"> <span class="pl-pds">`</span></span>-- ...</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">GT Voxel Downloading</h3><a id="user-content-gt-voxel-downloading" class="anchor" aria-label="Permalink: GT Voxel Downloading" href="#gt-voxel-downloading"><svg 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">We provide the webdataset-format files of ground-truth voxels in <a href="https://drive.google.com/file/d/1o5hC1SVxrItMEWwKEy4FYgVJGXpE72D4/view?usp=drive_link" rel="nofollow">Google Drive</a> (need request). After getting approval, download and uncompress it in <code>SCube-release/waymo_webdataset</code> along with other attributes.</p> <p dir="auto">To visualize the ground-truth voxel data, which is stored in point cloud format, you can run the following command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python inference/visualize_gt_pc.py -p <GT_VOXEL_TAR_FILE>"><pre>python inference/visualize_gt_pc.py -p <span class="pl-k"><</span>GT_VOXEL_TAR_FILE<span class="pl-k">></span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Training</h2><a id="user-content-model-training" class="anchor" aria-label="Permalink: Model Training" href="#model-training"><svg 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> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">VAE Training</h3><a id="user-content-vae-training" class="anchor" aria-label="Permalink: VAE Training" href="#vae-training"><svg 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> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Coarse stage VAE python train.py configs/waymo_scube/train_vae_64x64x64_dense_height_down2_residual.yaml --wname train_vae_64x64x64_dense_height_down2_residual --max_epochs 10 --gpus 8 --eval_interval 1 # Fine stage VAE python train.py configs/waymo_scube/train_vae_256x256x128_sparse.yaml --wname train_vae_256x256x128_sparse --max_epochs 10 --gpus 8 --eval_interval 1"><pre><span class="pl-c"><span class="pl-c">#</span> Coarse stage VAE</span> python train.py configs/waymo_scube/train_vae_64x64x64_dense_height_down2_residual.yaml --wname train_vae_64x64x64_dense_height_down2_residual --max_epochs 10 --gpus 8 --eval_interval 1 <span class="pl-c"><span class="pl-c">#</span> Fine stage VAE</span> python train.py configs/waymo_scube/train_vae_256x256x128_sparse.yaml --wname train_vae_256x256x128_sparse --max_epochs 10 --gpus 8 --eval_interval 1</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Geometry Reconstruction Training (Latent Voxel Diffusion Model)</h3><a id="user-content-geometry-reconstruction-training-latent-voxel-diffusion-model" class="anchor" aria-label="Permalink: Geometry Reconstruction Training (Latent Voxel Diffusion Model)" href="#geometry-reconstruction-training-latent-voxel-diffusion-model"><svg 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> <div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">You need to modify the <code>vae_checkpoint</code> in <code>configs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml</code> and <code>configs/waymo_scube/train_diffusion_256x256x128_sparse.yaml</code> to your own experiments. In you wandb webpage, you can find a unique "run id" for your experiment and replace the <code>[YOUR_RUN_ID]</code> in the yaml file.</p> </div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Image conditioned voxel diffusion python train.py configs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml --wname train_diffusion_64x64x64_image_cond --max_epochs 40 --gpus 8 --eval_interval 1 # Semantic conditioned upsampling voxel diffusion python train.py configs/waymo_scube/train_diffusion_256x256x128_sparse.yaml --wname train_diffusion_256x256x128_sparse --max_epochs 20 --gpus 8 --eval_interval 1"><pre><span class="pl-c"><span class="pl-c">#</span> Image conditioned voxel diffusion</span> python train.py configs/waymo_scube/train_diffusion_64x64x64_image_cond.yaml --wname train_diffusion_64x64x64_image_cond --max_epochs 40 --gpus 8 --eval_interval 1 <span class="pl-c"><span class="pl-c">#</span> Semantic conditioned upsampling voxel diffusion</span> python train.py configs/waymo_scube/train_diffusion_256x256x128_sparse.yaml --wname train_diffusion_256x256x128_sparse --max_epochs 20 --gpus 8 --eval_interval 1</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Appearance Reconstruction Training (GSM, <em>G</em>aussian <em>S</em>platting <em>M</em>odel)</h3><a id="user-content-appearance-reconstruction-training-gsm-gaussian-splatting-model" class="anchor" aria-label="Permalink: Appearance Reconstruction Training (GSM, Gaussian Splatting Model)" href="#appearance-reconstruction-training-gsm-gaussian-splatting-model"><svg 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> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python train.py configs/waymo_scube/train_gsm_unet3d_view3.yaml --wname train_gsm_unet3d_view3 --max_epochs 30 --gpus 8 --eval_interval 1"><pre>python train.py configs/waymo_scube/train_gsm_unet3d_view3.yaml --wname train_gsm_unet3d_view3 --max_epochs 30 --gpus 8 --eval_interval 1</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Inference</h2><a id="user-content-model-inference" class="anchor" aria-label="Permalink: Model Inference" href="#model-inference"><svg 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"><strong>Inference VAE</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# replace nvidia-toronto to your own wandb account! python inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_64x64x64_dense_height_down2_residual python inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_256x256x128_sparse"><pre><span class="pl-c"><span class="pl-c">#</span> replace nvidia-toronto to your own wandb account!</span> python inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_64x64x64_dense_height_down2_residual python inference/vae.py none --ckpt_vae wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_vae_256x256x128_sparse</pre></div> <p dir="auto">This will generate a folder <code>../vae_output_waymo_wds/</code>, storing predicted & GT grid + semantics. Specify <code>--ckpt_vae</code> to the experiment name.</p> <p dir="auto"><strong>Inference Diffusion</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# replace nvidia-toronto to your own wandb account! python inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond python inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse"><pre><span class="pl-c"><span class="pl-c">#</span> replace nvidia-toronto to your own wandb account!</span> python inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond python inference/diffusion.py none --use_ema --use_ddim --ddim_step 100 --ckpt_dm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse</pre></div> <p dir="auto">This will generate a folder <code>../diffusion_output_waymo_wds/</code>, storing predicted & GT voxel grid with semantics. Specify <code>--ckpt_dm</code> to the experiment name.</p> <p dir="auto"><strong>Inference GSM</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# replace nvidia-toronto to your own wandb account! python inference/gaussian_feedforward.py none --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 [--save_gs]"><pre><span class="pl-c"><span class="pl-c">#</span> replace nvidia-toronto to your own wandb account!</span> python inference/gaussian_feedforward.py none --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 [--save_gs]</pre></div> <p dir="auto">This will generate a folder <code>../diffusion_output_waymo_wds/</code>, storing predicted & GT grid + semantics. Specify <code>--ckpt_gsm</code> to the experiment name. Add <code>[--save_gs]</code> to save the 3D Gaussians.</p> <p dir="auto"><strong>Inference Full Pipeline</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# replace nvidia-toronto to your own wandb account! python inference/diffusion_cascading_gsm.py none \ --use_ema --use_ddim --ddim_step 100 \ --ckpt_dm_c wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond \ --ckpt_dm_f wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse \ --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 \ --input_frame_offsets [0] \ --sup_frame_offsets [0,5,10] \ --split val \ --val_starting_frame 0"><pre><span class="pl-c"><span class="pl-c">#</span> replace nvidia-toronto to your own wandb account!</span> python inference/diffusion_cascading_gsm.py none \ --use_ema --use_ddim --ddim_step 100 \ --ckpt_dm_c wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_64x64x64_image_cond \ --ckpt_dm_f wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_diffusion_256x256x128_sparse \ --ckpt_gsm wdb:nvidia-toronto/scube-scene-recon/waymo_wds/train_gsm_unet3d_view3 \ --input_frame_offsets [0] \ --sup_frame_offsets [0,5,10] \ --split val \ --val_starting_frame 0</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Visualize the Inference Result</h2><a id="user-content-visualize-the-inference-result" class="anchor" aria-label="Permalink: Visualize the Inference Result" href="#visualize-the-inference-result"><svg 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"><strong>Visualize Grids</strong></p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/nv-tlabs/SCube/blob/main/assets/vis-pair.png"><img src="/nv-tlabs/SCube/raw/main/assets/vis-pair.png" alt="" width="900" style="max-width: 100%;"></a> </div> <p dir="auto">In <code>../vae_output_waymo_wds/</code> and <code>../diffusion_output_waymo_wds/</code> and <code>../cascading_diffusion_output_waymo_wds</code>, you can find some folders containing <code>x.pt</code> and <code>x_gt.pt</code>, where <code>x</code> is the sample index for your inference. Run the following command to launch a interactive visualizer (even on a remote-ssh machine) in a web browser.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python inference/visualize_grid_compare.py -p <PT_FILES_FOLDER> [-t voxel]"><pre>python inference/visualize_grid_compare.py -p <span class="pl-k"><</span>PT_FILES_FOLDER<span class="pl-k">></span> [-t voxel]</pre></div> <p dir="auto">where <code>-t</code> is available for <code>voxel</code> or <code>pc</code>. Do not use <code>voxel</code> for the fine stage, which is very heavy for rendering. It would be easy to use it on a remote machine with VSCode; VSCode will forward the port automatically for you.</p> <hr> <p dir="auto"><strong>Visualize 3DGS</strong></p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/nv-tlabs/SCube/blob/main/assets/vis-gs.png"><img src="/nv-tlabs/SCube/raw/main/assets/vis-gs.png" alt="" width="900" style="max-width: 100%;"></a> </div> <p dir="auto">In <code>../splat_output_waymo_wds</code> and <code>cascading_diffusion_output_waymo_wds</code>, you can find some folder containing <code>x_splat.pkl</code>, where <code>x</code> is the sample index for your inferenece. You can visualize the 3DGS with</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python inference/visualize_3dgs_pkl.py -p <3DGS_PKL_FILE>"><pre>python inference/visualize_3dgs_pkl.py -p <span class="pl-k"><</span>3DGS_PKL_FILE<span class="pl-k">></span></pre></div> <hr> <p dir="auto"><strong>Shortcut</strong></p> <p dir="auto">You could install this project to create short cut for visualization.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="poetry install "><pre>poetry install </pre></div> <p dir="auto">Then you will have command <code>vis-pair</code> equivalent to <code>python inference/visualize_grid_compare.py</code>, <code>vis-gs</code> equivalent to <code>python inference/visualize_3dgs_pkl.py</code> and <code>vis-gt-pc</code> equivalent to <code>inference/visualize_gt_pc.py</code>. You can run visualization as</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="vis-pair -p <PT_FILES_FOLDER> vis-gs -p <3DGS_PKL_FILE> vis-gt-pc -p <GT_VOXEL_TAR_FILE>"><pre>vis-pair -p <span class="pl-k"><</span>PT_FILES_FOLDER<span class="pl-k">></span> vis-gs -p <span class="pl-k"><</span>3DGS_PKL_FILE<span class="pl-k">></span> vis-gt-pc -p <span class="pl-k"><</span>GT_VOXEL_TAR_FILE<span class="pl-k">></span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">License</h2><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg 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">Copyright © 2024, NVIDIA Corporation & affiliates. All rights reserved. This work is made available under the <a href="/nv-tlabs/SCube/blob/main/LICENSE.txt">Nvidia Source Code License</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Related Works</h2><a id="user-content-related-works" class="anchor" aria-label="Permalink: Related Works" href="#related-works"><svg 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>Lu et al. 2024. <a href="https://research.nvidia.com/labs/toronto-ai/infinicube/" rel="nofollow">InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models</a>.</li> <li>Ren et al. 2024. <a href="https://research.nvidia.com/labs/toronto-ai/xcube" rel="nofollow">XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies</a>.</li> <li>Williams et al. 2024. <a href="https://arxiv.org/abs/2407.01781" rel="nofollow">𝑓VDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence</a>.</li> </ul> <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 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 based on <a href="https://github.com/nv-tlabs/XCube">https://github.com/nv-tlabs/XCube</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 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> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{ ren2024scube, title={SCube: Instant Large-Scale Scene Reconstruction using VoxSplats}, author={Ren, Xuanchi and Lu, Yifan and Liang, Hanxue and Wu, Jay Zhangjie and Ling, Huan and Chen, Mike and Fidler, Sanja annd Williams, Francis and Huang, Jiahui}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}, year={2024}, }"><pre class="notranslate"><code>@inproceedings{ ren2024scube, title={SCube: Instant Large-Scale Scene Reconstruction using VoxSplats}, author={Ren, Xuanchi and Lu, Yifan and Liang, Hanxue and Wu, Jay Zhangjie and Ling, Huan and Chen, Mike and Fidler, Sanja annd Williams, Francis and Huang, Jiahui}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}, year={2024}, } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="M0KPaF705VGsKD+WyjPDsQ7ri0mu8z9qVIJwgQhUGWHZ2urjuoMvlJIFqoC9UJzJpon+ywAtuNS1nWkUmwXw0A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 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