[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-187728-en":3,"doc-seo-187728-105":30,"detail-sidebar-cat-1-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":11,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},187728,8796096645457,"Arica Lee","https://ap-avatar.wpscdn.com/avatar/800003749518d68ffe3?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345340919836971",1,158,"General","NeurIPS-2024 - Template-free NeRF for Semantic 3D Reconstruction of Dynamic Scene","Template-free NeRF is evaluated for semantic 3D reconstruction in dynamic scenes, focusing on multi-entity reconstruction with semantic outputs. Experimental comparisons cover prior dynamic NeRF methods and variants with or without pretraining features, reporting geometric accuracy using distance/compensation metrics and overlap-based precision, recall, and F-score. Results also analyze hand, object, and scene reconstruction, including ablations on initialization and head-sharing, alongside evaluation under ground-truth versus predicted masks and pose, demonstrating improved reconstruction quality for the proposed method.","| Methods | Template\u003Cbr>free | No pre\u003Cbr>trained\u003Cbr>features | Reconstructs |\n| --- | --- | --- | --- |\n| Vid2Avatar [39] | ✗ | ✓ | single entity |\n| AnimatableNeRF [7] | ✗ | ✓ | single entity |\n| SDF-PDF [8] | ✗ | ✓ | single entity |\n| HumanNeRF [40] | ✗ | ✓ | single entity |\n| HOSNeRF [13] | ✗ | ✓ | multiple entities |\n| NDR [15] | ✓ | ✓ | single entity |\n| HyperNeRF [27] | ✓ | ✓ | single entity |\n| D-NeRF [14] | ✓ | ✓ | single entity |\n| BANMO [16] | ✓ | ✗ | single entity |\n| RAC [17] | ✓ | ✗ | single entity |\n| TAVA [28] | ✓ | ✓ | single entity |\n| Ours | ✓ | ✓ | multiple entities\u003Cbr>with semantic |\n\n| ❳❳❳ Metric ❳\u003Cbr>❳\u003Cbr>Method ❳❳❳❳ | Trained with\u003Cbr>ResField [45] | Dist. Acc. ↓\u003Cbr>(cm) | Comp. ↓\u003Cbr>(cm) | Prec. ↑\u003Cbr>(%) | Recal. ↑\u003Cbr>(%) | F-score ↑\u003Cbr>(%) | Chamfer ↓\u003Cbr>(cm) |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Tensor4D [25] | ✗ | 4.152 | 2.441 | 69.413 | 91.642 | 78.993 | 3.297 |\n| NDR [15] | ✗ | 4.203 | 3.527 | 73.048 | 78.846 | 75.599 | 3.865 |\n| HyperNeRF [27] | ✗ | 4.125 | 3.362 | 73.510 | 80.683 | 76.661 | 3.742 |\n| D-NeRF [14] | ✗ | 7.074 | 7.301 | 48.324 | 45.781 | 46.301 | 7.188 |\n| NDR [15] | ✓ | 3.591 | 3.193 | 78.564 | 82.472 | 80.385 | 3.399 |\n| HyperNeRF [27] | ✓ | 3.879 | 3.313 | 76.099 | 81.578 | 78.619 | 3.596 |\n| D-NeRF [14] | ✓ | 3.927 | 3.364 | 75.774 | 81.453 | 78.398 | 3.646 |\n| Ours | ✗ | 2.721 | 2.142 | 89.120 | 93.853 | 91.343 | 2.431 |\n\n\n| ❳❳❳ Metric ❳\u003Cbr>❳\u003Cbr>Method ❳❳❳❳ | Trained with\u003Cbr>ResField [45] | Dist. Acc. ↓\u003Cbr>(cm) | Comp. ↓\u003Cbr>(cm) | Prec. ↑\u003Cbr>(%) | Recal. ↑\u003Cbr>(%) | F-score ↑\u003Cbr>(%) | Chamfer ↓\u003Cbr>(cm) |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Tensor4D [25] | ✗ | 4.409 | 2.419 | 69.402 | 91.680 | 79.000 | 4.414 |\n| NDR [15] | ✗ | 4.865 | 3.546 | 69.653 | 76.994 | 72.728 | 4.205 |\n| HyperNeRF [27] | ✗ | 4.794 | 3.363 | 70.276 | 79.531 | 74.202 | 4.078 |\n| D-NeRF [14] | ✗ | 6.515 | 6.132 | 53.013 | 52.745 | 52.071 | 6.324 |\n| NDR [15] | ✓ | 4.681 | 3.186 | 72.934 | 81.025 | 76.553 | 3.931 |\n| HyperNeRF [27] | ✓ | 4.265 | 3.394 | 73.864 | 79.642 | 76.331 | 3.831 |\n| D-NeRF [14] | ✓ | 4.729 | 3.403 | 71.211 | 79.244 | 74.706 | 4.066 |\n| Ours | ✗ | 1.761 | 1.863 | 97.225 | 93.624 | 95.343 | 1.812 |\n| Tensor4D [25] ✗ 4.390 |  |  | 2.523 | 56.953 | 91.683 | 70.260 | 3.956 |\n| NDR [15] | ✗ | 3.747 | 3.607 | 76.526 | 75.534 | 75.675 | 3.677 |\n| HyperNeRF [27] | ✗ | 3.647 | 3.508 | 78.171 | 76.892 | 77.144 | 3.586 |\n| D-NeRF [14] | ✗ | 4.675 | 5.529 | 64.88 | 54.095 | 57.748 | 5.102 |\n| NDR [15] | ✓ | 3.442 | 3.531 | 81.114 | 76.612 | 78.641 | 3.485 |\n| HyperNeRF [27] | ✓ | 3.451 | 3.282 | 80.551 | 80.720 | 80.174 | 3.379 |\n| D-NeRF [14] | ✓ | 3.565 | 3.362 | 79.379 | 79.155 | 78.875 | 3.464 |\n| Ours  ✗  3.571 |  |  | 2.121 | 82.762 | 92.410 | 86.991 | 2.741 |\n\n\n|  |  | Hand reconstruction |  |  | Object reconstruction |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| ❳❳❳ Metric ❳\u003Cbr>❳ Method ❳❳❳\u003Cbr>❳\u003Cbr>\u003Cbr>Dist. Acc. ↓(cm) |  |  | F-score ↑ | Chamfer ↓ Dist. Acc. ↓ |  | F-score ↑ | Chamfer ↓ |\n|  |  |  | % | (cm) | (cm) | % | (cm) |\n| NDR [15] | ✓ | 1.419 | 94.051 | 1.217 | 1.154 | 93.782 | 1.279 |\n| HyperNeRF [27] | ✓ | 1.435 | 93.491 | 1.198 | 1.159 | 97.988 | 1.042 |\n| Ours | ✗ | 1.373 | 95.396 | 1.294 | 0.530 | 99.980 | 0.463 |\n\n\n| ❳❳❳ Metric ❳\u003Cbr>❳\u003Cbr>Method ❳❳❳❳ | Dist. ↓\u003Cbr>Acc.(cm) | Comp. ↓\u003Cbr>(cm) | Prec. ↑\u003Cbr>(%) | Recal. ↑\u003Cbr>(%) | F-score ↑\u003Cbr>(%) | Chamfer ↓\u003Cbr>(cm) |\n| --- | --- | --- | --- | --- | --- | --- |\n| TAVA [28] 2.79 |  | 2.14 | 90.40 | 95.67 | 92.95 | 2.47 |\n| AnimatableNeRF [7] | 3.63 | 2.39 | 81.57 | 93.32 | 88.30 | 2.81 |\n| Ours | 2.47 | 2.01 | 92.15 | 95.99 | 93.97 | 2.24 |\n| TAVA [28] | 0.92 | 0.53 | 99.65 | 100.00 | 99.83 | 0.73 |\n| Ours | 0.81 | 0.61 | 99.99 | 100.00 | 99.99 | 0.71 |\n\n| ❤❤Me❤th❤od❤❤❤❤❤❤M❤et❤i | Dist. ↓ F-score ↑ Chamfer ↓\u003Cbr>Acc. (cm) (%) (%) |  |  |\n| --- | --- | --- | --- |\n| W/o xinitc (Section 3, B)\u003Cbr>Same geometry heads (Section 3, A,B,C) | 3.92\u003Cbr>7.83 | 83.50\u003Cbr>41.18 | 3.51\u003Cbr>8.42 |\n\n| |  |","cbCailcMpQBu7duS","https://ap.wps.com/l/cbCailcMpQBu7duS","pdf",6068690,20,"English","en",105,"# Methods and Training Settings\n## Template-free vs Pretrained Features\n# Evaluation Metrics and Comparative Results\n## Distance/Compensation, Precision/Recall, F-score, Chamfer\n# Reconstruction Tasks and Ablation Studies\n## Hand, Object, and Scene Reconstruction\n## Ground-Truth vs Predicted Mask/Pose","[{\"question\":\"What does the document propose for dynamic-scene semantic reconstruction?\",\"answer\":\"It presents a template-free NeRF approach aimed at semantic 3D reconstruction in dynamic scenes, including multi-entity reconstruction with semantic information.\"},{\"question\":\"How are the reconstruction results evaluated?\",\"answer\":\"Evaluation reports geometric distance/compensation errors and overlap-based precision, recall, F-score, along with Chamfer distance to quantify reconstruction quality.\"},{\"question\":\"What ablations or evaluation conditions are included?\",\"answer\":\"The document compares variants such as training with or without pretrained features, initialization/head-sharing choices, and evaluation using ground-truth versus predicted mask and pose inputs.\"}]","NeurIPS-2024 - Template-free NeRF for Semantic 3D Reconstruction of Dynamic Scene | PDF",1788385047,7,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":14,"keywords":34,"description":15,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"neurips-2024-template-free-nerf-for-semantic-3d-reconstruction-of-dynamic-scene","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":11},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/template/","Template",2,{"item":49,"name":13,"@type":43,"position":50},"https://docshare.wps.com/template/general/",3,{"item":52,"name":14,"@type":43,"position":53},"https://docshare.wps.com/template/neurips-2024-template-free-nerf-for-semantic-3d-reconstruction-of-dynamic-scene/187728/",4,{"url":52,"name":14,"@type":55,"author":56,"headline":14,"publisher":58,"fileFormat":61,"inLanguage":23,"description":15,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-06","2026-09-02",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the document propose for dynamic-scene semantic reconstruction?","Question",{"text":76,"@type":77},"It presents a template-free NeRF approach aimed at semantic 3D reconstruction in dynamic scenes, including multi-entity reconstruction with semantic information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the reconstruction results evaluated?",{"text":81,"@type":77},"Evaluation reports geometric distance/compensation errors and overlap-based precision, recall, F-score, along with Chamfer distance to quantify reconstruction quality.",{"name":83,"@type":74,"acceptedAnswer":84},"What ablations or evaluation conditions are included?",{"text":85,"@type":77},"The document compares variants such as training with or without pretrained features, initialization/head-sharing choices, and evaluation using ground-truth versus predicted mask and pose inputs.","https://schema.org",{"og:url":52,"og:type":88,"og:title":14,"og:site_name":59,"og:description":15},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,99,104,109,114,119,124,129,134],{"id":95,"doc_module":11,"doc_module_name":46,"category_name":96,"show_sort_weight":97,"slug":98},11,"Presentations",90,"presentations",{"id":100,"doc_module":11,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},12,"Resumes",80,"resumes",{"id":105,"doc_module":11,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},14,"Invoices",70,"invoices",{"id":110,"doc_module":11,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},15,"Posters",60,"posters",{"id":115,"doc_module":11,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},16,"Social Media",50,"social-media",{"id":120,"doc_module":11,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},17,"Forms",40,"forms",{"id":125,"doc_module":11,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},18,"Letters",30,"letters",{"id":130,"doc_module":11,"doc_module_name":46,"category_name":131,"show_sort_weight":132,"slug":133},21,"Paper Templates",5,"papers-templates",{"id":12,"doc_module":11,"doc_module_name":46,"category_name":13,"show_sort_weight":4,"slug":135},"general-158"]