The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).



This table lists the benchmark results for the ScanNet200 3D semantic label scenario.




Method Infoavg iouhead ioucommon ioutail iouwallchairfloortabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LGroundpermissive0.272 100.485 100.184 100.106 100.778 100.676 70.932 100.479 120.572 100.718 90.399 70.265 100.453 110.085 30.745 100.446 100.726 80.232 100.622 100.901 100.512 90.826 100.786 110.178 110.549 70.277 100.659 100.381 100.518 90.295 120.323 90.777 70.599 70.028 80.321 60.363 110.000 10.708 100.858 100.746 90.063 100.022 50.457 100.077 70.476 70.243 100.402 90.397 120.233 70.077 120.720 120.610 110.103 40.629 80.437 120.626 80.446 90.702 40.190 80.005 10.058 110.322 100.702 110.244 100.768 90.000 30.134 80.552 100.279 110.395 100.147 110.000 10.207 100.612 90.000 40.000 60.000 10.000 30.658 60.566 100.323 100.525 120.229 90.179 80.467 120.154 110.000 30.002 30.000 10.051 10.000 30.127 20.703 90.000 10.000 70.216 10.112 110.358 80.547 10.187 40.092 110.156 120.055 70.296 100.252 70.143 30.000 30.014 90.398 60.000 10.028 110.173 40.000 100.265 110.348 100.415 110.179 30.019 110.218 60.000 10.597 50.274 120.565 80.000 10.012 40.000 70.039 110.022 20.000 70.117 100.000 70.000 40.000 80.000 10.000 10.324 110.000 10.384 60.000 10.000 80.251 120.000 10.566 80.000 10.000 10.066 90.404 10.886 90.199 10.000 60.000 10.059 30.000 10.136 10.540 40.127 120.295 80.085 60.143 60.514 50.413 100.000 80.000 10.498 60.000 20.000 20.000 70.623 80.000 20.000 10.000 10.132 110.000 40.000 20.000 60.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
PonderV2 ScanNet2000.346 30.552 50.270 50.175 40.810 50.682 50.950 30.560 40.641 70.761 10.398 80.357 70.570 50.113 20.804 40.603 40.750 40.283 20.681 40.952 30.548 30.874 30.852 80.290 70.700 20.356 80.792 30.445 70.545 80.436 70.351 80.787 50.611 50.050 70.290 90.519 90.000 10.825 50.888 30.842 30.259 20.100 20.558 40.070 90.497 50.247 90.457 80.889 20.248 60.106 70.817 80.691 40.094 50.729 20.636 40.620 90.503 80.660 90.243 50.000 30.212 50.590 30.860 60.400 30.881 40.000 30.202 20.622 70.408 60.499 70.261 70.000 10.385 60.636 60.000 40.000 60.000 10.000 30.433 110.843 50.660 40.574 90.481 20.336 40.677 40.486 30.000 30.030 10.000 10.034 50.000 30.080 50.869 70.000 10.000 70.000 80.540 50.727 20.232 120.115 60.186 50.193 60.000 110.403 70.326 40.103 90.000 30.290 30.392 70.000 10.346 50.062 70.424 30.375 40.431 40.667 30.115 90.082 70.239 50.000 10.504 90.606 40.584 70.000 10.002 50.186 50.104 70.000 50.394 30.384 60.083 50.000 40.007 60.000 10.000 10.880 40.000 10.377 70.000 10.263 30.565 20.000 10.608 60.000 10.000 10.304 50.009 60.924 10.000 50.000 60.000 10.000 50.000 10.128 20.584 20.475 50.412 50.076 90.269 30.621 40.509 40.010 30.000 10.491 70.063 10.000 20.472 30.880 20.000 20.000 10.000 10.179 40.125 10.000 20.441 50.000 1
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
CSC-Pretrainpermissive0.249 120.455 120.171 110.079 120.766 120.659 100.930 120.494 90.542 120.700 120.314 120.215 120.430 120.121 10.697 120.441 110.683 110.235 90.609 120.895 110.476 120.816 110.770 120.186 90.634 40.216 120.734 60.340 110.471 110.307 110.293 120.591 120.542 100.076 60.205 110.464 100.000 10.484 120.832 120.766 70.052 110.000 70.413 110.059 110.418 110.222 110.318 120.609 100.206 100.112 60.743 90.625 90.076 70.579 110.548 80.590 110.371 110.552 120.081 110.003 20.142 90.201 120.638 120.233 110.686 120.000 30.142 70.444 120.375 80.247 120.198 90.000 10.128 120.454 120.019 20.097 10.000 10.000 30.553 90.557 110.373 80.545 110.164 110.014 120.547 110.174 100.000 30.002 30.000 10.037 30.000 30.063 80.664 110.000 10.000 70.130 20.170 90.152 120.335 70.079 90.110 100.175 90.098 60.175 120.166 100.045 120.207 10.014 90.465 40.000 10.001 120.001 120.046 70.299 100.327 110.537 80.033 110.012 120.186 80.000 10.205 110.377 90.463 110.000 10.058 30.000 70.055 100.041 10.000 70.105 110.000 70.000 40.000 80.000 10.000 10.398 100.000 10.308 120.000 10.000 80.319 100.000 10.543 90.000 10.000 10.062 100.004 80.862 110.000 50.000 60.000 10.000 50.000 10.123 30.316 110.225 100.250 100.094 30.180 50.332 110.441 60.000 80.000 10.310 120.000 20.000 20.000 70.592 90.000 20.000 10.000 10.203 20.000 40.000 20.000 60.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
PPT-SpUNet-F.T.0.332 70.556 30.270 40.123 90.816 40.682 50.946 40.549 50.657 60.756 30.459 40.376 60.550 60.001 90.807 30.616 20.727 70.267 40.691 30.942 70.530 70.872 40.874 50.330 50.542 90.374 50.792 30.400 90.673 20.572 40.433 20.793 40.623 40.008 120.351 50.594 60.000 10.783 80.876 50.833 40.213 30.000 70.537 50.091 40.519 20.304 40.620 50.942 10.264 30.124 50.855 40.695 30.086 60.646 60.506 110.658 40.535 40.715 20.314 10.000 30.241 30.608 20.897 20.359 60.858 60.000 30.076 120.611 80.392 70.509 60.378 30.000 10.579 20.565 110.000 40.000 60.000 10.000 30.755 40.806 80.661 20.572 100.350 70.181 70.660 70.300 90.000 30.000 50.000 10.023 70.000 30.042 110.930 20.000 10.000 70.077 50.584 40.392 70.339 60.185 50.171 70.308 20.006 100.563 30.256 60.150 20.000 30.002 110.345 100.000 10.045 90.197 20.063 60.323 80.453 30.600 60.163 60.037 100.349 20.000 10.672 20.679 30.753 10.000 10.000 70.000 70.117 30.000 50.000 70.291 80.000 70.000 40.039 40.000 10.000 10.899 20.000 10.374 80.000 10.000 80.545 40.000 10.634 30.000 10.000 10.074 80.223 30.914 50.000 50.021 40.000 10.000 50.000 10.112 40.498 80.649 10.383 70.095 20.135 110.449 80.432 70.008 50.000 10.518 50.000 20.000 20.000 70.796 30.000 20.000 10.000 10.138 90.000 40.000 20.000 60.000 1
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
AWCS0.305 90.508 90.225 90.142 60.782 90.634 120.937 90.489 100.578 90.721 70.364 100.355 80.515 70.023 70.764 90.523 80.707 90.264 60.633 90.922 90.507 100.886 10.804 100.179 100.436 110.300 90.656 110.529 20.501 100.394 80.296 110.820 20.603 60.131 30.179 120.619 20.000 10.707 110.865 90.773 60.171 40.010 60.484 90.063 100.463 90.254 80.332 110.649 80.220 80.100 80.729 100.613 100.071 90.582 100.628 50.702 20.424 100.749 10.137 100.000 30.142 90.360 90.863 40.305 90.877 50.000 30.173 30.606 90.337 90.478 90.154 100.000 10.253 90.664 50.000 40.000 60.000 10.000 30.626 80.782 90.302 110.602 40.185 100.282 60.651 80.317 80.000 30.000 50.000 10.022 80.000 30.154 10.876 60.000 10.014 60.063 70.029 120.553 40.467 20.084 80.124 90.157 110.049 90.373 90.252 70.097 100.000 30.219 40.542 20.000 10.392 30.172 50.000 100.339 60.417 60.533 90.093 100.115 50.195 70.000 10.516 70.288 110.741 20.000 10.001 60.233 40.056 90.000 50.159 40.334 70.077 60.000 40.000 80.000 10.000 10.749 90.000 10.411 50.000 10.008 70.452 80.000 10.595 70.000 10.000 10.220 70.006 70.894 80.006 40.000 60.000 10.000 50.000 10.112 40.504 60.404 70.551 10.093 40.129 120.484 70.381 120.000 80.000 10.396 100.000 20.000 20.620 20.402 120.000 20.000 10.000 10.142 80.000 40.000 20.512 40.000 1
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.851 10.687 40.971 10.586 10.755 10.752 50.505 10.404 40.575 20.000 100.848 10.616 20.761 10.349 10.738 10.978 10.546 40.860 60.926 10.346 20.654 30.384 40.828 10.523 30.699 10.583 30.387 60.822 10.688 10.118 40.474 10.603 40.000 10.832 30.903 10.753 80.140 60.000 70.650 20.109 20.520 10.457 10.497 70.871 30.281 20.192 20.887 20.748 10.168 10.727 30.733 10.740 10.644 10.714 30.190 80.000 30.256 20.449 60.914 10.514 10.759 100.337 10.172 40.692 40.617 10.636 10.325 40.000 10.641 10.782 10.000 40.065 20.000 10.000 30.842 10.903 10.661 20.662 20.612 10.405 20.731 10.566 10.000 30.000 50.000 10.017 100.301 10.088 40.941 10.000 10.077 20.000 80.717 30.790 10.310 90.026 120.264 20.349 10.220 20.397 80.366 10.115 80.000 30.337 10.463 50.000 10.531 20.218 10.593 10.455 10.469 10.708 20.210 10.592 20.108 110.000 10.728 10.682 20.671 40.000 10.000 70.407 10.136 20.022 20.575 10.436 40.259 20.428 10.048 30.000 10.000 10.879 50.000 10.480 20.000 10.133 50.597 10.000 10.690 10.000 10.000 10.009 110.000 100.921 20.000 50.151 20.000 10.000 50.000 10.109 60.494 90.622 20.394 60.073 100.141 70.798 10.528 30.026 10.000 10.551 30.000 20.000 20.134 50.717 50.000 20.000 10.000 10.188 30.000 40.000 20.791 10.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
Minkowski 34Dpermissive0.253 110.463 110.154 120.102 110.771 110.650 110.932 100.483 110.571 110.710 100.331 110.250 110.492 90.044 40.703 110.419 120.606 120.227 110.621 110.865 120.531 60.771 120.813 90.291 60.484 100.242 110.612 120.282 120.440 120.351 100.299 100.622 110.593 80.027 90.293 80.310 120.000 10.757 90.858 100.737 110.150 50.164 10.368 120.084 50.381 120.142 120.357 100.720 70.214 90.092 110.724 110.596 120.056 100.655 50.525 90.581 120.352 120.594 110.056 120.000 30.014 120.224 110.772 100.205 120.720 110.000 30.159 50.531 110.163 120.294 110.136 120.000 10.169 110.589 100.000 40.000 60.000 10.002 10.663 50.466 120.265 120.582 70.337 80.016 110.559 100.084 120.000 30.000 50.000 10.036 40.000 30.125 30.670 100.000 10.102 10.071 60.164 100.406 60.386 40.046 110.068 120.159 100.117 30.284 110.111 110.094 110.000 30.000 120.197 120.000 10.044 100.013 100.002 90.228 120.307 120.588 70.025 120.545 30.134 100.000 10.655 30.302 100.282 120.000 10.060 20.000 70.035 120.000 50.000 70.097 120.000 70.000 40.005 70.000 10.000 10.096 120.000 10.334 110.000 10.000 80.274 110.000 10.513 110.000 10.000 10.280 60.194 40.897 70.000 50.000 60.000 10.000 50.000 10.108 70.279 120.189 110.141 120.059 110.272 20.307 120.445 50.003 60.000 10.353 110.000 20.026 10.000 70.581 100.001 10.000 10.000 10.093 120.002 30.000 20.000 60.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
OA-CNN-L_ScanNet2000.333 60.558 20.269 60.124 80.821 30.703 10.946 40.569 20.662 20.748 60.487 20.455 10.572 40.000 100.789 50.534 60.736 60.271 30.713 20.949 40.498 110.877 20.860 60.332 40.706 10.474 10.788 50.406 80.637 30.495 60.355 70.805 30.592 90.015 110.396 30.602 50.000 10.799 60.876 50.713 120.276 10.000 70.493 80.080 60.448 100.363 20.661 20.833 50.262 40.125 40.823 70.665 60.076 70.720 40.557 60.637 60.517 60.672 80.227 60.000 30.158 80.496 40.843 80.352 70.835 80.000 30.103 100.711 30.527 20.526 50.320 50.000 10.568 40.625 70.067 10.000 60.000 10.001 20.806 30.836 60.621 70.591 50.373 60.314 50.668 50.398 60.003 20.000 50.000 10.016 110.024 20.043 100.906 40.000 10.052 40.000 80.384 70.330 90.342 50.100 70.223 40.183 80.112 40.476 50.313 50.130 70.196 20.112 70.370 90.000 10.234 70.071 60.160 40.403 30.398 90.492 100.197 20.076 80.272 30.000 10.200 120.560 60.735 30.000 10.000 70.000 70.110 50.002 40.021 60.412 50.000 70.000 40.000 80.000 10.000 10.794 70.000 10.445 30.000 10.022 60.509 60.000 10.517 100.000 10.000 10.001 120.245 20.915 40.024 20.089 30.000 10.262 20.000 10.103 80.524 50.392 80.515 20.013 120.251 40.411 100.662 10.001 70.000 10.473 80.000 20.000 20.150 40.699 60.000 20.000 10.000 10.166 50.000 40.024 10.000 60.000 1
CeCo0.340 40.551 60.247 80.181 30.784 80.661 90.939 80.564 30.624 80.721 70.484 30.429 20.575 20.027 60.774 70.503 90.753 30.242 80.656 80.945 50.534 50.865 50.860 60.177 120.616 50.400 20.818 20.579 10.615 60.367 90.408 50.726 100.633 20.162 10.360 40.619 20.000 10.828 40.873 70.924 20.109 80.083 30.564 30.057 120.475 80.266 60.781 10.767 60.257 50.100 80.825 60.663 70.048 110.620 90.551 70.595 100.532 50.692 60.246 40.000 30.213 40.615 10.861 50.376 50.900 30.000 30.102 110.660 50.321 100.547 40.226 80.000 10.311 80.742 20.011 30.006 50.000 10.000 30.546 100.824 70.345 90.665 10.450 40.435 10.683 30.411 50.338 10.000 50.000 10.030 60.000 30.068 60.892 50.000 10.063 30.000 80.257 80.304 100.387 30.079 90.228 30.190 70.000 110.586 10.347 20.133 50.000 30.037 80.377 80.000 10.384 40.006 110.003 80.421 20.410 80.643 40.171 40.121 40.142 90.000 10.510 80.447 70.474 90.000 10.000 70.286 30.083 80.000 50.000 70.603 10.096 40.063 30.000 80.000 10.000 10.898 30.000 10.429 40.000 10.400 10.550 30.000 10.633 40.000 10.000 10.377 30.000 100.916 30.000 50.000 60.000 10.000 50.000 10.102 90.499 70.296 90.463 30.089 50.304 10.740 20.401 110.010 30.000 10.560 20.000 20.000 20.709 10.652 70.000 20.000 10.000 10.143 70.000 40.000 20.609 20.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
BFANet ScanNet200permissive0.360 20.553 40.293 20.193 20.827 20.689 20.970 20.528 80.661 40.753 40.436 50.378 50.469 100.042 50.810 20.654 10.760 20.266 50.659 70.973 20.574 20.849 90.897 20.382 10.546 80.372 60.698 90.491 50.617 50.526 50.436 10.764 90.476 120.101 50.409 20.585 70.000 10.835 10.901 20.810 50.102 90.000 70.688 10.096 30.483 60.264 70.612 60.591 110.358 10.161 30.863 30.707 20.128 20.814 10.669 30.629 70.563 20.651 100.258 30.000 30.194 60.494 50.806 90.394 40.953 20.000 30.233 10.757 20.508 40.556 30.476 20.000 10.573 30.741 30.000 40.000 60.000 10.000 30.000 120.852 40.678 10.616 30.460 30.338 30.710 20.534 20.000 30.025 20.000 10.043 20.000 30.056 90.493 120.000 10.000 70.109 30.785 20.590 30.298 100.282 30.143 80.262 40.053 80.526 40.337 30.215 10.000 30.135 50.510 30.000 10.596 10.043 90.511 20.321 90.459 20.772 10.124 80.060 90.266 40.000 10.574 60.568 50.653 60.000 10.093 10.298 20.239 10.000 50.516 20.129 90.284 10.000 40.431 10.000 10.000 10.848 60.000 10.492 10.000 10.376 20.522 50.000 10.469 120.000 10.000 10.330 40.151 50.875 100.000 50.254 10.000 10.000 50.000 10.088 100.661 10.481 30.255 90.105 10.139 90.666 30.641 20.000 80.000 10.614 10.000 20.000 20.000 70.921 10.000 20.000 10.000 10.497 10.000 40.000 20.000 60.000 1
L3DETR-ScanNet_2000.336 50.533 80.279 30.155 50.801 70.689 20.946 40.539 60.660 50.759 20.380 90.333 90.583 10.000 100.788 60.529 70.740 50.261 70.679 60.940 80.525 80.860 60.883 40.226 80.613 60.397 30.720 80.512 40.565 70.620 10.417 40.775 80.629 30.158 20.298 70.579 80.000 10.835 10.883 40.927 10.114 70.079 40.511 70.073 80.508 30.312 30.629 30.861 40.192 110.098 100.908 10.636 80.032 120.563 120.514 100.664 30.505 70.697 50.225 70.000 30.264 10.411 80.860 60.321 80.960 10.058 20.109 90.776 10.526 30.557 20.303 60.000 10.339 70.712 40.000 40.014 40.000 10.000 30.638 70.856 30.641 50.579 80.107 120.119 100.661 60.416 40.000 30.000 50.000 10.007 120.000 30.067 70.910 30.000 10.000 70.000 80.463 60.448 50.294 110.324 10.293 10.211 50.108 50.448 60.068 120.141 40.000 30.330 20.699 10.000 10.256 60.192 30.000 100.355 50.418 50.209 120.146 70.679 10.101 120.000 10.503 100.687 10.671 40.000 10.000 70.174 60.117 30.000 50.122 50.515 20.104 30.259 20.312 20.000 10.000 10.765 80.000 10.369 90.000 10.183 40.422 90.000 10.646 20.000 10.000 10.565 10.001 90.125 120.010 30.002 50.000 10.487 10.000 10.075 110.548 30.420 60.233 110.082 70.138 100.430 90.427 80.000 80.000 10.549 40.000 20.000 20.074 60.409 110.000 20.000 10.000 10.152 60.051 20.000 20.598 30.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OctFormer ScanNet200permissive0.326 80.539 70.265 70.131 70.806 60.670 80.943 70.535 70.662 20.705 110.423 60.407 30.505 80.003 80.765 80.582 50.686 100.227 110.680 50.943 60.601 10.854 80.892 30.335 30.417 120.357 70.724 70.453 60.632 40.596 20.432 30.783 60.512 110.021 100.244 100.637 10.000 10.787 70.873 70.743 100.000 120.000 70.534 60.110 10.499 40.289 50.626 40.620 90.168 120.204 10.849 50.679 50.117 30.633 70.684 20.650 50.552 30.684 70.312 20.000 30.175 70.429 70.865 30.413 20.837 70.000 30.145 60.626 60.451 50.487 80.513 10.000 10.529 50.613 80.000 40.033 30.000 10.000 30.828 20.871 20.622 60.587 60.411 50.137 90.645 90.343 70.000 30.000 50.000 10.022 80.000 30.026 120.829 80.000 10.022 50.089 40.842 10.253 110.318 80.296 20.178 60.291 30.224 10.584 20.200 90.132 60.000 30.128 60.227 110.000 10.230 80.047 80.149 50.331 70.412 70.618 50.164 50.102 60.522 10.000 10.655 30.378 80.469 100.000 10.000 70.000 70.105 60.000 50.000 70.483 30.000 70.000 40.028 50.000 10.000 10.906 10.000 10.339 100.000 10.000 80.457 70.000 10.612 50.000 10.000 10.408 20.000 100.900 60.000 50.000 60.000 10.029 40.000 10.074 120.455 100.479 40.427 40.079 80.140 80.496 60.414 90.022 20.000 10.471 90.000 20.000 20.000 70.722 40.000 20.000 10.000 10.138 90.000 40.000 20.000 60.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023