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 ioualarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefloorfolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwallwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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OA-CNN-L_ScanNet2000.333 20.558 10.269 20.124 40.448 60.080 40.272 30.000 10.000 10.000 10.342 50.515 20.524 20.713 80.789 20.158 40.384 30.000 30.806 20.125 20.000 40.496 30.332 20.498 70.227 40.024 20.474 10.000 10.003 20.071 40.487 10.000 30.000 10.110 20.000 20.876 10.013 80.703 10.000 30.076 40.473 40.355 40.906 20.000 10.000 10.476 40.706 10.000 70.672 60.835 50.748 20.015 70.223 20.860 30.000 10.000 40.572 20.000 50.509 30.313 20.662 10.398 50.396 10.411 60.276 10.527 10.711 10.000 20.076 50.946 10.166 20.000 10.022 20.160 10.183 40.493 40.699 30.637 20.403 20.330 50.406 40.526 20.024 10.000 10.392 40.000 50.016 80.000 30.196 20.915 20.112 30.557 30.197 10.352 40.877 20.000 30.000 10.592 60.103 60.000 80.067 10.000 10.089 10.735 30.625 30.130 50.568 20.836 20.271 10.534 30.043 60.799 20.001 20.445 10.000 10.000 20.024 10.661 20.000 10.262 10.000 10.591 30.517 70.373 30.788 30.021 20.000 10.455 10.517 40.320 30.823 40.200 80.001 80.150 30.100 40.000 10.736 20.668 20.103 60.052 30.662 10.720 10.000 10.602 40.112 30.002 30.000 10.637 40.000 20.000 10.621 30.569 10.398 20.412 30.234 30.949 10.363 10.492 70.495 30.251 30.665 30.000 10.001 50.805 20.833 20.794 40.000 10.821 10.314 20.843 50.000 10.560 20.245 20.262 20.713 10.370 5
PPT-SpUNet-F.T.0.332 30.556 20.270 10.123 50.519 10.091 20.349 20.000 10.000 10.000 10.339 60.383 50.498 50.833 20.807 10.241 10.584 20.000 30.755 30.124 30.000 40.608 20.330 30.530 40.314 10.000 40.374 30.000 10.000 30.197 10.459 30.000 30.000 10.117 10.000 20.876 10.095 10.682 20.000 30.086 30.518 20.433 10.930 10.000 10.000 10.563 30.542 50.077 40.715 20.858 30.756 10.008 80.171 40.874 20.000 10.039 10.550 30.000 50.545 20.256 30.657 30.453 10.351 30.449 50.213 20.392 30.611 40.000 20.037 60.946 10.138 50.000 10.000 40.063 30.308 10.537 20.796 10.673 10.323 50.392 30.400 50.509 30.000 20.000 10.649 10.000 50.023 50.000 30.000 30.914 30.002 70.506 70.163 50.359 30.872 30.000 30.000 10.623 20.112 30.001 70.000 40.000 10.021 20.753 10.565 70.150 10.579 10.806 40.267 20.616 10.042 70.783 40.000 30.374 50.000 10.000 20.000 20.620 40.000 10.000 40.000 10.572 60.634 10.350 40.792 20.000 30.000 10.376 40.535 20.378 20.855 10.672 10.074 50.000 40.185 30.000 10.727 30.660 30.076 80.000 60.432 40.646 30.000 10.594 50.006 70.000 40.000 10.658 20.000 20.000 10.661 10.549 30.300 50.291 50.045 50.942 40.304 20.600 30.572 20.135 70.695 10.000 10.008 30.793 30.942 10.899 20.000 10.816 20.181 40.897 10.000 10.679 10.223 30.264 10.691 20.345 6
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.
OctFormer ScanNet200permissive0.326 40.539 40.265 30.131 30.499 20.110 10.522 10.000 10.000 10.000 10.318 80.427 40.455 60.743 60.765 40.175 30.842 10.000 30.828 10.204 10.033 20.429 40.335 10.601 10.312 20.000 40.357 40.000 10.000 30.047 50.423 40.000 30.000 10.105 30.000 20.873 30.079 60.670 40.000 30.117 10.471 50.432 20.829 50.000 10.000 10.584 20.417 80.089 30.684 50.837 40.705 70.021 60.178 30.892 10.000 10.028 20.505 50.000 50.457 40.200 60.662 10.412 30.244 60.496 30.000 80.451 20.626 30.000 20.102 40.943 30.138 50.000 10.000 40.149 20.291 20.534 30.722 20.632 30.331 40.253 70.453 30.487 40.000 20.000 10.479 20.000 50.022 60.000 30.000 30.900 40.128 20.684 10.164 40.413 10.854 50.000 30.000 10.512 80.074 80.003 60.000 40.000 10.000 30.469 60.613 40.132 40.529 30.871 10.227 70.582 20.026 80.787 30.000 30.339 60.000 10.000 20.000 20.626 30.000 10.029 30.000 10.587 40.612 30.411 20.724 50.000 30.000 10.407 30.552 10.513 10.849 20.655 20.408 10.000 40.296 10.000 10.686 60.645 50.145 30.022 40.414 50.633 40.000 10.637 10.224 10.000 40.000 10.650 30.000 20.000 10.622 20.535 40.343 30.483 20.230 40.943 30.289 30.618 20.596 10.140 60.679 20.000 10.022 10.783 40.620 60.906 10.000 10.806 30.137 60.865 20.000 10.378 40.000 70.168 80.680 30.227 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo0.340 10.551 30.247 40.181 10.475 40.057 80.142 70.000 10.000 10.000 10.387 30.463 30.499 40.924 10.774 30.213 20.257 40.000 30.546 80.100 50.006 30.615 10.177 80.534 20.246 30.000 40.400 20.000 10.338 10.006 70.484 20.609 10.000 10.083 40.000 20.873 30.089 40.661 50.000 30.048 80.560 10.408 30.892 30.000 10.000 10.586 10.616 30.000 70.692 40.900 10.721 30.162 10.228 10.860 30.000 10.000 40.575 10.083 20.550 10.347 10.624 40.410 40.360 20.740 10.109 50.321 60.660 20.000 20.121 20.939 40.143 30.000 10.400 10.003 50.190 30.564 10.652 40.615 40.421 10.304 60.579 10.547 10.000 20.000 10.296 50.000 50.030 40.096 10.000 30.916 10.037 40.551 40.171 30.376 20.865 40.286 10.000 10.633 10.102 70.027 40.011 30.000 10.000 30.474 50.742 10.133 30.311 40.824 30.242 40.503 50.068 40.828 10.000 30.429 20.000 10.063 10.000 20.781 10.000 10.000 40.000 10.665 10.633 20.450 10.818 10.000 30.000 10.429 20.532 30.226 40.825 30.510 60.377 20.709 10.079 60.000 10.753 10.683 10.102 70.063 20.401 70.620 60.000 10.619 20.000 80.000 40.000 10.595 60.000 20.000 10.345 50.564 20.411 10.603 10.384 20.945 20.266 40.643 10.367 50.304 10.663 40.000 10.010 20.726 60.767 30.898 30.000 10.784 40.435 10.861 40.000 10.447 30.000 70.257 30.656 40.377 4
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
AWCS0.305 50.508 50.225 50.142 20.463 50.063 60.195 50.000 10.000 10.000 10.467 20.551 10.504 30.773 30.764 50.142 50.029 80.000 30.626 60.100 50.000 40.360 50.179 60.507 60.137 60.006 30.300 50.000 10.000 30.172 30.364 60.512 20.000 10.056 50.000 20.865 50.093 30.634 80.000 30.071 60.396 60.296 70.876 40.000 10.000 10.373 50.436 70.063 60.749 10.877 20.721 30.131 20.124 50.804 60.000 10.000 40.515 40.010 40.452 50.252 40.578 50.417 20.179 80.484 40.171 30.337 50.606 50.000 20.115 30.937 50.142 40.000 10.008 30.000 70.157 70.484 50.402 80.501 60.339 30.553 10.529 20.478 50.000 20.000 10.404 30.001 40.022 60.077 20.000 30.894 60.219 10.628 20.093 60.305 50.886 10.233 20.000 10.603 30.112 30.023 50.000 40.000 10.000 30.741 20.664 20.097 60.253 50.782 50.264 30.523 40.154 10.707 70.000 30.411 30.000 10.000 20.000 20.332 70.000 10.000 40.000 10.602 20.595 40.185 70.656 70.159 10.000 10.355 50.424 60.154 60.729 60.516 50.220 40.620 20.084 50.000 10.707 50.651 40.173 10.014 50.381 80.582 70.000 10.619 20.049 60.000 40.000 10.702 10.000 20.000 10.302 70.489 60.317 40.334 40.392 10.922 50.254 50.533 60.394 40.129 80.613 60.000 10.000 60.820 10.649 50.749 50.000 10.782 50.282 30.863 30.000 10.288 70.006 50.220 50.633 50.542 1
LGroundpermissive0.272 60.485 60.184 60.106 60.476 30.077 50.218 40.000 10.000 10.000 10.547 10.295 60.540 10.746 50.745 60.058 70.112 70.005 10.658 50.077 80.000 40.322 60.178 70.512 50.190 50.199 10.277 60.000 10.000 30.173 20.399 50.000 30.000 10.039 70.000 20.858 60.085 50.676 30.002 10.103 20.498 30.323 50.703 60.000 10.000 10.296 60.549 40.216 10.702 30.768 60.718 50.028 40.092 70.786 70.000 10.000 40.453 70.022 30.251 80.252 40.572 60.348 60.321 40.514 20.063 60.279 70.552 60.000 20.019 70.932 60.132 70.000 10.000 40.000 70.156 80.457 60.623 50.518 50.265 70.358 40.381 60.395 60.000 20.000 10.127 80.012 30.051 10.000 30.000 30.886 70.014 50.437 80.179 20.244 60.826 60.000 30.000 10.599 40.136 10.085 20.000 40.000 10.000 30.565 40.612 50.143 20.207 60.566 60.232 60.446 60.127 20.708 60.000 30.384 40.000 10.000 20.000 20.402 50.000 10.059 20.000 10.525 80.566 50.229 60.659 60.000 30.000 10.265 60.446 50.147 70.720 80.597 40.066 60.000 40.187 20.000 10.726 40.467 80.134 50.000 60.413 60.629 50.000 10.363 70.055 50.022 20.000 10.626 50.000 20.000 10.323 60.479 80.154 70.117 60.028 70.901 60.243 60.415 80.295 80.143 50.610 70.000 10.000 60.777 50.397 80.324 70.000 10.778 60.179 50.702 70.000 10.274 80.404 10.233 40.622 60.398 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 80.455 80.171 70.079 80.418 70.059 70.186 60.000 10.000 10.000 10.335 70.250 70.316 70.766 40.697 80.142 50.170 50.003 20.553 70.112 40.097 10.201 80.186 50.476 80.081 70.000 40.216 80.000 10.000 30.001 80.314 80.000 30.000 10.055 60.000 20.832 80.094 20.659 60.002 10.076 40.310 80.293 80.664 80.000 10.000 10.175 80.634 20.130 20.552 80.686 80.700 80.076 30.110 60.770 80.000 10.000 40.430 80.000 50.319 60.166 70.542 80.327 70.205 70.332 70.052 70.375 40.444 80.000 20.012 80.930 80.203 10.000 10.000 40.046 40.175 50.413 70.592 60.471 70.299 60.152 80.340 70.247 80.000 20.000 10.225 60.058 20.037 20.000 30.207 10.862 80.014 50.548 50.033 70.233 70.816 70.000 30.000 10.542 70.123 20.121 10.019 20.000 10.000 30.463 70.454 80.045 80.128 80.557 70.235 50.441 70.063 50.484 80.000 30.308 80.000 10.000 20.000 20.318 80.000 10.000 40.000 10.545 70.543 60.164 80.734 40.000 30.000 10.215 80.371 70.198 50.743 50.205 70.062 70.000 40.079 60.000 10.683 70.547 70.142 40.000 60.441 30.579 80.000 10.464 60.098 40.041 10.000 10.590 70.000 20.000 10.373 40.494 50.174 60.105 70.001 80.895 70.222 70.537 50.307 70.180 40.625 50.000 10.000 60.591 80.609 70.398 60.000 10.766 80.014 80.638 80.000 10.377 50.004 60.206 70.609 80.465 2
Ji Hou, Benjamin Graham, Matthias Nie├čner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 70.463 70.154 80.102 70.381 80.084 30.134 80.000 10.000 10.000 10.386 40.141 80.279 80.737 70.703 70.014 80.164 60.000 30.663 40.092 70.000 40.224 70.291 40.531 30.056 80.000 40.242 70.000 10.000 30.013 60.331 70.000 30.000 10.035 80.001 10.858 60.059 70.650 70.000 30.056 70.353 70.299 60.670 70.000 10.000 10.284 70.484 60.071 50.594 70.720 70.710 60.027 50.068 80.813 50.000 10.005 30.492 60.164 10.274 70.111 80.571 70.307 80.293 50.307 80.150 40.163 80.531 70.002 10.545 10.932 60.093 80.000 10.000 40.002 60.159 60.368 80.581 70.440 80.228 80.406 20.282 80.294 70.000 20.000 10.189 70.060 10.036 30.000 30.000 30.897 50.000 80.525 60.025 80.205 80.771 80.000 30.000 10.593 50.108 50.044 30.000 40.000 10.000 30.282 80.589 60.094 70.169 70.466 80.227 70.419 80.125 30.757 50.002 10.334 70.000 10.000 20.000 20.357 60.000 10.000 40.000 10.582 50.513 80.337 50.612 80.000 30.000 10.250 70.352 80.136 80.724 70.655 20.280 30.000 40.046 80.000 10.606 80.559 60.159 20.102 10.445 20.655 20.000 10.310 80.117 20.000 40.000 10.581 80.026 10.000 10.265 80.483 70.084 80.097 80.044 60.865 80.142 80.588 40.351 60.272 20.596 80.000 10.003 40.622 70.720 40.096 80.000 10.771 70.016 70.772 60.000 10.302 60.194 40.214 60.621 70.197 8
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019