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
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 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 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 by
IMFSegNet0.334 90.532 130.251 110.179 70.486 90.041 160.139 130.003 10.283 40.000 10.274 150.191 150.457 140.704 140.795 70.197 90.830 60.000 30.710 90.055 160.064 40.518 60.305 100.458 170.216 120.027 50.284 130.000 10.000 30.044 120.406 100.561 70.000 10.080 120.000 30.873 90.021 150.683 80.000 70.076 90.494 100.363 90.648 160.000 10.000 20.425 90.649 40.000 100.668 120.908 70.740 110.010 140.206 80.862 100.000 10.000 110.560 90.000 70.359 130.237 110.631 120.408 110.411 40.322 150.246 40.439 100.599 130.047 40.213 70.940 100.139 110.000 10.369 50.124 100.188 120.495 110.624 110.626 80.320 140.595 40.495 80.496 100.000 40.000 10.340 120.014 60.032 70.135 50.000 40.903 80.277 60.612 80.196 70.344 120.848 130.260 60.000 10.574 130.073 160.062 40.000 40.000 10.091 60.839 30.776 30.123 120.392 90.756 120.274 50.518 120.029 160.842 40.000 60.357 130.000 10.035 70.000 30.444 120.793 20.245 50.000 10.512 160.512 150.159 150.713 130.000 100.000 10.336 130.484 120.569 20.852 90.615 60.120 120.068 100.228 80.000 10.733 100.773 20.190 40.000 100.608 60.792 40.000 10.597 70.000 140.025 20.000 10.573 170.000 20.000 10.508 110.555 80.363 100.139 120.610 20.947 80.305 70.594 90.527 90.009 170.633 130.000 10.060 30.820 50.604 150.799 90.000 10.799 110.034 140.784 130.000 10.618 60.424 20.134 160.646 130.214 14
OA-CNN-L_ScanNet2000.333 110.558 50.269 90.124 130.448 140.080 90.272 50.000 30.000 70.000 10.342 80.515 40.524 70.713 130.789 90.158 120.384 120.000 30.806 60.125 70.000 90.496 80.332 70.498 140.227 80.024 60.474 30.000 10.003 20.071 90.487 30.000 110.000 10.110 80.000 30.876 70.013 170.703 30.000 70.076 90.473 120.355 110.906 60.000 10.000 20.476 60.706 10.000 100.672 100.835 130.748 90.015 130.223 70.860 110.000 10.000 110.572 70.000 70.509 70.313 70.662 40.398 130.396 80.411 130.276 20.527 40.711 50.000 70.076 130.946 60.166 60.000 10.022 100.160 70.183 130.493 130.699 90.637 60.403 60.330 120.406 130.526 60.024 20.000 10.392 110.000 110.016 160.000 120.196 30.915 50.112 120.557 100.197 60.352 100.877 30.000 120.000 10.592 120.103 110.000 140.067 10.000 10.089 70.735 70.625 110.130 90.568 60.836 70.271 80.534 90.043 130.799 110.001 50.445 50.000 10.000 80.024 20.661 40.000 50.262 30.000 10.591 80.517 130.373 80.788 70.021 80.000 10.455 40.517 90.320 80.823 120.200 160.001 170.150 50.100 120.000 10.736 90.668 100.103 140.052 60.662 40.720 80.000 10.602 60.112 70.002 60.000 10.637 90.000 20.000 10.621 100.569 50.398 90.412 50.234 120.949 60.363 50.492 140.495 110.251 40.665 90.000 10.001 110.805 70.833 60.794 110.000 10.821 50.314 50.843 110.000 10.560 100.245 70.262 60.713 40.370 11
CSC-Pretrainpermissive0.249 170.455 170.171 160.079 170.418 150.059 140.186 100.000 30.000 70.000 10.335 100.250 130.316 160.766 70.697 170.142 130.170 140.003 20.553 140.112 90.097 10.201 160.186 140.476 150.081 160.000 90.216 170.000 10.000 30.001 170.314 170.000 110.000 10.055 150.000 30.832 160.094 30.659 150.002 50.076 90.310 160.293 170.664 140.000 10.000 20.175 170.634 60.130 20.552 170.686 170.700 170.076 70.110 150.770 170.000 10.000 110.430 170.000 70.319 150.166 150.542 170.327 160.205 160.332 140.052 160.375 130.444 170.000 70.012 170.930 170.203 30.000 10.000 120.046 120.175 140.413 160.592 140.471 160.299 150.152 160.340 160.247 170.000 40.000 10.225 150.058 30.037 40.000 120.207 20.862 150.014 140.548 130.033 160.233 160.816 160.000 120.000 10.542 150.123 50.121 10.019 20.000 10.000 110.463 160.454 170.045 170.128 170.557 150.235 140.441 160.063 110.484 170.000 60.308 170.000 10.000 80.000 30.318 170.000 50.000 90.000 10.545 140.543 120.164 140.734 90.000 100.000 10.215 170.371 160.198 140.743 140.205 150.062 150.000 110.079 140.000 10.683 160.547 160.142 90.000 100.441 110.579 150.000 10.464 140.098 90.041 10.000 10.590 140.000 20.000 10.373 130.494 140.174 150.105 160.001 170.895 160.222 160.537 120.307 160.180 50.625 140.000 10.000 120.591 170.609 140.398 150.000 10.766 170.014 160.638 170.000 10.377 130.004 130.206 130.609 170.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGroundpermissive0.272 150.485 150.184 150.106 150.476 110.077 100.218 80.000 30.000 70.000 10.547 20.295 110.540 50.746 100.745 150.058 160.112 160.005 10.658 110.077 150.000 90.322 140.178 160.512 110.190 130.199 20.277 150.000 10.000 30.173 70.399 120.000 110.000 10.039 160.000 30.858 140.085 70.676 110.002 50.103 60.498 80.323 140.703 120.000 10.000 20.296 150.549 120.216 10.702 60.768 140.718 140.028 100.092 160.786 160.000 10.000 110.453 160.022 50.251 170.252 90.572 150.348 140.321 110.514 70.063 150.279 160.552 150.000 70.019 160.932 150.132 150.000 10.000 120.000 150.156 170.457 150.623 120.518 140.265 160.358 110.381 150.395 140.000 40.000 10.127 170.012 80.051 10.000 120.000 40.886 130.014 140.437 170.179 80.244 150.826 150.000 120.000 10.599 100.136 10.085 30.000 40.000 10.000 110.565 130.612 130.143 50.207 150.566 140.232 150.446 150.127 40.708 150.000 60.384 90.000 10.000 80.000 30.402 140.000 50.059 70.000 10.525 150.566 110.229 120.659 150.000 100.000 10.265 150.446 140.147 160.720 170.597 80.066 140.000 110.187 90.000 10.726 130.467 170.134 120.000 100.413 150.629 120.000 10.363 160.055 100.022 30.000 10.626 110.000 20.000 10.323 150.479 170.154 160.117 150.028 160.901 150.243 150.415 160.295 170.143 60.610 160.000 10.000 120.777 120.397 170.324 160.000 10.778 150.179 80.702 160.000 10.274 160.404 40.233 100.622 150.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
AWCS0.305 140.508 140.225 140.142 110.463 130.063 130.195 90.000 30.000 70.000 10.467 30.551 30.504 80.773 60.764 140.142 130.029 170.000 30.626 130.100 110.000 90.360 130.179 150.507 130.137 150.006 80.300 120.000 10.000 30.172 80.364 150.512 90.000 10.056 140.000 30.865 130.093 40.634 170.000 70.071 130.396 140.296 160.876 90.000 10.000 20.373 130.436 160.063 90.749 20.877 100.721 120.131 30.124 140.804 150.000 10.000 110.515 120.010 60.452 100.252 90.578 140.417 80.179 170.484 100.171 70.337 140.606 120.000 70.115 100.937 140.142 90.000 10.008 110.000 150.157 160.484 140.402 170.501 150.339 90.553 70.529 30.478 120.000 40.000 10.404 100.001 100.022 130.077 90.000 40.894 120.219 70.628 70.093 150.305 140.886 10.233 90.000 10.603 90.112 60.023 90.000 40.000 10.000 110.741 60.664 80.097 150.253 140.782 100.264 110.523 110.154 20.707 160.000 60.411 80.000 10.000 80.000 30.332 160.000 50.000 90.000 10.602 70.595 100.185 130.656 160.159 60.000 10.355 110.424 150.154 150.729 150.516 100.220 100.620 30.084 130.000 10.707 140.651 130.173 50.014 90.381 170.582 140.000 10.619 30.049 120.000 70.000 10.702 40.000 20.000 10.302 160.489 150.317 130.334 70.392 70.922 140.254 130.533 130.394 130.129 140.613 150.000 10.000 120.820 50.649 110.749 130.000 10.782 140.282 60.863 60.000 10.288 150.006 120.220 110.633 140.542 3
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024
CeCo0.340 70.551 90.247 130.181 60.475 120.057 150.142 120.000 30.000 70.000 10.387 60.463 60.499 90.924 20.774 110.213 60.257 130.000 30.546 150.100 110.006 80.615 20.177 170.534 70.246 60.000 90.400 50.000 10.338 10.006 160.484 50.609 50.000 10.083 110.000 30.873 90.089 50.661 140.000 70.048 150.560 40.408 60.892 80.000 10.000 20.586 10.616 80.000 100.692 80.900 80.721 120.162 10.228 60.860 110.000 10.000 110.575 50.083 30.550 40.347 40.624 130.410 100.360 90.740 30.109 130.321 150.660 80.000 70.121 90.939 130.143 80.000 10.400 20.003 130.190 110.564 60.652 100.615 110.421 50.304 130.579 10.547 50.000 40.000 10.296 140.000 110.030 90.096 70.000 40.916 40.037 130.551 120.171 90.376 70.865 70.286 50.000 10.633 50.102 120.027 80.011 30.000 10.000 110.474 140.742 50.133 70.311 130.824 80.242 130.503 140.068 90.828 90.000 60.429 70.000 10.063 50.000 30.781 20.000 50.000 90.000 10.665 20.633 60.450 60.818 20.000 100.000 10.429 50.532 70.226 130.825 110.510 110.377 50.709 20.079 140.000 10.753 50.683 80.102 150.063 50.401 160.620 130.000 10.619 30.000 140.000 70.000 10.595 130.000 20.000 10.345 140.564 60.411 80.603 10.384 80.945 90.266 110.643 50.367 140.304 10.663 100.000 10.010 70.726 150.767 70.898 30.000 10.784 130.435 10.861 70.000 10.447 110.000 150.257 70.656 110.377 10
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
OctFormer ScanNet200permissive0.326 130.539 100.265 100.131 120.499 60.110 40.522 30.000 30.000 70.000 10.318 110.427 70.455 150.743 110.765 130.175 110.842 40.000 30.828 50.204 40.033 60.429 110.335 60.601 20.312 30.000 90.357 100.000 10.000 30.047 110.423 90.000 110.000 10.105 90.000 30.873 90.079 90.670 120.000 70.117 50.471 130.432 30.829 110.000 10.000 20.584 20.417 170.089 60.684 90.837 120.705 160.021 120.178 110.892 60.000 10.028 80.505 130.000 70.457 90.200 140.662 40.412 90.244 150.496 80.000 170.451 80.626 90.000 70.102 110.943 90.138 130.000 10.000 120.149 80.291 30.534 90.722 70.632 70.331 100.253 140.453 110.487 110.000 40.000 10.479 60.000 110.022 130.000 120.000 40.900 100.128 110.684 30.164 100.413 40.854 100.000 120.000 10.512 160.074 150.003 110.000 40.000 10.000 110.469 150.613 120.132 80.529 70.871 30.227 160.582 70.026 170.787 120.000 60.339 150.000 10.000 80.000 30.626 70.000 50.029 80.000 10.587 90.612 80.411 70.724 100.000 100.000 10.407 60.552 50.513 30.849 100.655 40.408 40.000 110.296 20.000 10.686 150.645 140.145 80.022 80.414 140.633 110.000 10.637 20.224 30.000 70.000 10.650 80.000 20.000 10.622 90.535 120.343 120.483 30.230 130.943 100.289 100.618 70.596 50.140 80.679 80.000 10.022 60.783 110.620 120.906 10.000 10.806 80.137 100.865 50.000 10.378 120.000 150.168 150.680 80.227 13
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-F.T.0.332 120.556 60.270 70.123 140.519 40.091 70.349 40.000 30.000 70.000 10.339 90.383 100.498 100.833 40.807 40.241 40.584 90.000 30.755 70.124 80.000 90.608 30.330 80.530 90.314 20.000 90.374 80.000 10.000 30.197 50.459 70.000 110.000 10.117 60.000 30.876 70.095 20.682 90.000 70.086 80.518 70.433 20.930 40.000 10.000 20.563 30.542 140.077 70.715 40.858 110.756 50.008 160.171 120.874 80.000 10.039 70.550 110.000 70.545 50.256 80.657 80.453 40.351 100.449 110.213 60.392 120.611 110.000 70.037 150.946 60.138 130.000 10.000 120.063 110.308 20.537 80.796 50.673 40.323 110.392 100.400 140.509 70.000 40.000 10.649 10.000 110.023 120.000 120.000 40.914 60.002 160.506 160.163 110.359 80.872 50.000 120.000 10.623 70.112 60.001 120.000 40.000 10.021 90.753 50.565 150.150 40.579 40.806 90.267 90.616 40.042 140.783 130.000 60.374 110.000 10.000 80.000 30.620 80.000 50.000 90.000 10.572 130.634 50.350 90.792 50.000 100.000 10.376 90.535 60.378 60.855 70.672 30.074 130.000 110.185 100.000 10.727 120.660 120.076 170.000 100.432 120.646 100.000 10.594 80.006 130.000 70.000 10.658 70.000 20.000 10.661 40.549 100.300 140.291 80.045 140.942 110.304 80.600 80.572 70.135 120.695 50.000 10.008 90.793 90.942 20.899 20.000 10.816 60.181 70.897 20.000 10.679 40.223 80.264 50.691 50.345 12
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
L3DETR-ScanNet_2000.336 80.533 110.279 60.155 100.508 50.073 110.101 170.000 30.058 60.000 10.294 140.233 140.548 40.927 10.788 100.264 20.463 110.000 30.638 120.098 130.014 70.411 120.226 130.525 100.225 90.010 70.397 60.000 10.000 30.192 60.380 140.598 60.000 10.117 60.000 30.883 60.082 80.689 40.000 70.032 170.549 60.417 40.910 50.000 10.000 20.448 80.613 90.000 100.697 70.960 30.759 40.158 20.293 30.883 70.000 10.312 30.583 40.079 40.422 110.068 170.660 70.418 70.298 120.430 120.114 110.526 50.776 30.051 30.679 30.946 60.152 70.000 10.183 80.000 150.211 80.511 100.409 160.565 120.355 80.448 80.512 50.557 30.000 40.000 10.420 90.000 110.007 170.104 60.000 40.125 170.330 30.514 150.146 120.321 130.860 80.174 110.000 10.629 60.075 140.000 140.000 40.000 10.002 100.671 80.712 70.141 60.339 120.856 40.261 120.529 100.067 100.835 60.000 60.369 120.000 10.259 20.000 30.629 60.000 50.487 10.000 10.579 110.646 40.107 170.720 110.122 70.000 10.333 140.505 100.303 90.908 30.503 130.565 20.074 80.324 10.000 10.740 80.661 110.109 130.000 100.427 130.563 170.000 10.579 110.108 80.000 70.000 10.664 60.000 20.000 10.641 70.539 110.416 70.515 20.256 110.940 120.312 60.209 170.620 30.138 110.636 110.000 10.000 120.775 130.861 50.765 120.000 10.801 90.119 110.860 80.000 10.687 20.001 140.192 140.679 90.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
GSTran0.334 100.533 120.250 120.179 80.487 80.041 160.139 130.003 10.273 50.000 10.273 160.189 160.465 120.704 140.794 80.198 80.831 50.000 30.712 80.055 160.063 50.518 60.306 90.459 160.217 100.028 40.282 140.000 10.000 30.044 120.405 110.558 80.000 10.080 120.000 30.873 90.020 160.684 70.000 70.075 120.496 90.363 90.651 150.000 10.000 20.425 90.648 50.000 100.669 110.914 60.741 100.009 150.200 90.864 90.000 10.000 110.560 90.000 70.357 140.233 120.633 110.408 110.411 40.320 160.242 50.440 90.598 140.047 40.205 80.940 100.139 110.000 10.372 40.138 90.191 100.495 110.618 130.624 90.321 120.595 40.496 70.499 80.000 40.000 10.340 120.014 60.032 70.136 40.000 40.903 80.279 50.601 90.198 50.345 110.849 110.260 60.000 10.573 140.072 170.060 50.000 40.000 10.089 70.838 40.775 40.125 110.381 110.752 130.274 50.517 130.032 150.841 50.000 60.354 140.000 10.047 60.000 30.439 130.787 30.252 40.000 10.512 160.507 160.158 160.717 120.000 100.000 10.337 120.483 130.570 10.853 80.614 70.121 110.070 90.229 70.000 10.732 110.773 20.193 30.000 100.606 70.791 50.000 10.593 90.000 140.010 50.000 10.574 160.000 20.000 10.507 120.554 90.361 110.136 130.608 30.948 70.304 80.593 100.533 80.011 160.634 120.000 10.060 30.821 40.613 130.797 100.000 10.799 110.036 130.782 140.000 10.609 70.423 30.133 170.647 120.213 15
PTv3 ScanNet2000.393 30.592 30.330 20.216 30.520 30.109 50.108 160.000 30.337 10.000 10.310 120.394 90.494 110.753 90.848 20.256 30.717 80.000 30.842 40.192 50.065 30.449 100.346 40.546 60.190 130.000 90.384 70.000 10.000 30.218 40.505 20.791 30.000 10.136 40.000 30.903 20.073 120.687 60.000 70.168 20.551 50.387 70.941 30.000 10.000 20.397 120.654 30.000 100.714 50.759 150.752 70.118 40.264 40.926 30.000 10.048 60.575 50.000 70.597 20.366 20.755 10.469 20.474 30.798 20.140 100.617 30.692 70.000 70.592 40.971 20.188 40.000 10.133 90.593 20.349 10.650 30.717 80.699 30.455 20.790 20.523 40.636 10.301 10.000 10.622 20.000 110.017 150.259 30.000 40.921 30.337 10.733 20.210 40.514 20.860 80.407 10.000 10.688 20.109 80.000 140.000 40.000 10.151 50.671 80.782 20.115 130.641 20.903 20.349 10.616 40.088 70.832 80.000 60.480 20.000 10.428 10.000 30.497 100.000 50.000 90.000 10.662 30.690 20.612 10.828 10.575 10.000 10.404 70.644 20.325 70.887 40.728 10.009 160.134 70.026 170.000 10.761 30.731 40.172 60.077 40.528 80.727 70.000 10.603 50.220 50.022 30.000 10.740 10.000 20.000 10.661 40.586 20.566 40.436 40.531 50.978 30.457 20.708 30.583 60.141 70.748 30.000 10.026 50.822 30.871 40.879 50.000 10.851 20.405 20.914 10.000 10.682 30.000 150.281 40.738 30.463 6
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)
PonderV2 ScanNet2000.346 60.552 80.270 80.175 90.497 70.070 120.239 70.000 30.000 70.000 10.232 170.412 80.584 20.842 30.804 50.212 70.540 100.000 30.433 160.106 100.000 90.590 50.290 120.548 50.243 70.000 90.356 110.000 10.000 30.062 100.398 130.441 100.000 10.104 100.000 30.888 50.076 110.682 90.030 30.094 70.491 110.351 120.869 100.000 10.063 10.403 110.700 20.000 100.660 130.881 90.761 30.050 80.186 100.852 130.000 10.007 90.570 80.100 20.565 30.326 60.641 100.431 60.290 140.621 60.259 30.408 110.622 100.125 20.082 120.950 50.179 50.000 10.263 60.424 50.193 90.558 70.880 40.545 130.375 70.727 30.445 120.499 80.000 40.000 10.475 70.002 90.034 60.083 80.000 40.924 20.290 40.636 60.115 140.400 50.874 40.186 100.000 10.611 80.128 30.113 20.000 40.000 10.000 110.584 120.636 100.103 140.385 100.843 60.283 40.603 60.080 80.825 100.000 60.377 100.000 10.000 80.000 30.457 110.000 50.000 90.000 10.574 120.608 90.481 40.792 50.394 50.000 10.357 100.503 110.261 100.817 130.504 120.304 70.472 40.115 110.000 10.750 70.677 90.202 20.000 100.509 90.729 60.000 10.519 120.000 140.000 70.000 10.620 120.000 20.000 10.660 60.560 70.486 60.384 60.346 100.952 50.247 140.667 40.436 120.269 30.691 60.000 10.010 70.787 100.889 30.880 40.000 10.810 70.336 40.860 80.000 10.606 80.009 110.248 90.681 70.392 9
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.
ODIN - Sem200permissive0.368 40.562 40.297 40.207 40.380 170.196 10.828 20.000 30.321 20.000 10.400 50.775 10.460 130.501 170.769 120.065 150.870 30.000 30.913 10.213 30.000 90.000 170.389 20.554 40.312 30.000 90.591 10.000 10.000 30.491 10.487 30.894 20.000 10.378 20.303 10.796 170.088 60.669 130.081 10.216 10.256 170.334 130.898 70.000 10.000 20.370 140.599 100.000 100.581 160.988 20.749 80.090 60.242 50.921 40.000 10.202 50.609 20.000 70.655 10.214 130.654 90.346 150.408 70.485 90.169 80.631 20.704 60.000 70.814 10.940 100.127 160.000 10.000 120.462 40.227 60.641 40.885 30.657 50.434 30.000 170.550 20.393 150.000 40.000 10.590 40.000 110.048 20.077 90.000 40.784 160.131 100.557 100.316 20.359 80.833 140.373 20.000 10.661 40.108 90.001 120.000 40.000 10.301 30.612 110.565 150.129 100.482 80.468 160.274 50.561 80.376 10.912 20.181 10.440 60.000 10.166 40.000 30.641 50.000 50.426 20.000 10.642 50.626 70.259 110.787 80.429 40.000 10.589 10.523 80.246 110.857 60.000 170.228 90.000 110.265 40.000 10.752 60.832 10.090 160.157 10.791 10.578 160.000 10.373 150.539 10.000 70.000 10.685 50.000 20.000 10.632 80.575 30.663 10.152 110.358 90.926 130.397 30.454 150.610 40.119 150.685 70.000 10.000 120.803 80.740 90.441 140.000 10.800 100.000 170.871 30.000 10.220 170.487 10.862 10.682 60.054 17
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024
ALS-MinkowskiNetcopyleft0.414 20.610 20.322 30.271 20.542 20.153 30.159 110.000 30.000 70.000 10.404 40.503 50.532 60.672 160.804 50.285 10.888 20.000 30.900 20.226 20.087 20.598 40.342 50.671 10.217 100.087 30.449 40.000 10.000 30.253 30.477 61.000 10.000 10.118 50.000 30.905 10.071 130.710 20.076 20.047 160.665 10.376 80.981 10.000 10.000 20.466 70.632 70.113 40.769 10.956 40.795 20.031 90.314 10.936 10.000 10.390 20.601 30.000 70.458 80.366 20.719 30.440 50.564 10.699 40.314 10.464 70.784 20.200 10.283 60.973 10.142 90.000 10.250 70.285 60.220 70.718 10.752 60.723 20.460 10.248 150.475 100.463 130.000 40.000 10.446 80.021 50.025 110.285 10.000 40.972 10.149 80.769 10.230 30.535 10.879 20.252 80.000 10.693 10.129 20.000 140.000 40.000 10.447 10.958 10.662 90.159 20.598 30.780 110.344 20.646 30.106 60.893 30.135 30.455 30.000 10.194 30.259 10.726 30.475 40.000 90.000 10.741 10.865 10.571 20.817 30.445 30.000 10.506 20.630 30.230 120.916 20.728 10.635 11.000 10.252 60.000 10.804 20.697 70.137 110.043 70.717 20.807 30.000 10.510 130.245 20.000 70.000 10.709 30.000 20.000 10.703 20.572 40.646 20.223 100.531 50.984 10.397 30.813 10.798 10.135 120.800 10.000 10.097 20.832 20.752 80.842 70.000 10.852 10.149 90.846 100.000 10.666 50.359 50.252 80.777 10.690 2
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
BFANet ScanNet200permissive0.360 50.553 70.293 50.193 50.483 100.096 60.266 60.000 30.000 70.000 10.298 130.255 120.661 10.810 50.810 30.194 100.785 70.000 30.000 170.161 60.000 90.494 90.382 30.574 30.258 50.000 90.372 90.000 10.000 30.043 140.436 80.000 110.000 10.239 30.000 30.901 30.105 10.689 40.025 40.128 40.614 20.436 10.493 170.000 10.000 20.526 40.546 130.109 50.651 140.953 50.753 60.101 50.143 130.897 50.000 10.431 10.469 150.000 70.522 60.337 50.661 60.459 30.409 60.666 50.102 140.508 60.757 40.000 70.060 140.970 30.497 10.000 10.376 30.511 30.262 40.688 20.921 20.617 100.321 120.590 60.491 90.556 40.000 40.000 10.481 50.093 10.043 30.284 20.000 40.875 140.135 90.669 40.124 130.394 60.849 110.298 40.000 10.476 170.088 130.042 70.000 40.000 10.254 40.653 100.741 60.215 10.573 50.852 50.266 100.654 20.056 120.835 60.000 60.492 10.000 10.000 80.000 30.612 90.000 50.000 90.000 10.616 60.469 170.460 50.698 140.516 20.000 10.378 80.563 40.476 40.863 50.574 90.330 60.000 110.282 30.000 10.760 40.710 50.233 10.000 100.641 50.814 20.000 10.585 100.053 110.000 70.000 10.629 100.000 20.000 10.678 30.528 130.534 50.129 140.596 40.973 40.264 120.772 20.526 100.139 90.707 40.000 10.000 120.764 140.591 160.848 60.000 10.827 40.338 30.806 120.000 10.568 90.151 100.358 20.659 100.510 4
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
DITR0.449 10.629 10.392 10.289 10.650 10.168 20.862 10.000 30.313 30.000 10.580 10.568 20.564 30.766 70.867 10.238 50.949 10.000 30.866 30.300 10.000 90.664 10.482 10.508 120.317 10.420 10.551 20.000 10.000 30.486 20.519 10.662 40.000 10.385 10.000 30.901 30.079 90.727 10.000 70.160 30.606 30.417 40.967 20.000 10.000 20.498 50.596 110.130 20.728 30.998 10.805 10.000 170.314 10.934 20.000 10.278 40.636 10.000 70.403 120.367 10.741 20.484 10.500 21.000 10.113 120.828 10.815 10.000 70.733 20.969 40.374 20.000 10.579 11.000 10.230 50.617 50.983 10.729 10.423 40.855 10.508 60.622 20.018 30.000 10.591 30.034 40.028 100.066 110.869 10.904 70.334 20.651 50.716 10.514 20.871 60.315 30.000 10.664 30.128 30.014 100.000 40.000 10.392 20.851 20.817 10.153 30.823 10.991 10.318 30.680 10.134 30.913 10.157 20.448 40.000 10.000 80.000 30.826 10.978 10.091 60.000 10.660 40.647 30.571 20.804 40.001 90.000 10.480 30.700 10.421 50.947 10.433 140.411 30.148 60.262 50.000 10.849 10.709 60.138 100.150 20.714 30.889 10.000 10.698 10.222 40.000 70.000 10.720 20.000 20.000 10.805 10.600 10.642 30.268 90.904 10.982 20.477 10.632 60.718 20.139 90.776 20.000 10.178 10.886 10.962 10.839 80.000 10.851 20.043 120.869 40.000 10.710 10.315 60.348 30.753 20.397 8
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation.
Minkowski 34Dpermissive0.253 160.463 160.154 170.102 160.381 160.084 80.134 150.000 30.000 70.000 10.386 70.141 170.279 170.737 120.703 160.014 170.164 150.000 30.663 100.092 140.000 90.224 150.291 110.531 80.056 170.000 90.242 160.000 10.000 30.013 150.331 160.000 110.000 10.035 170.001 20.858 140.059 140.650 160.000 70.056 140.353 150.299 150.670 130.000 10.000 20.284 160.484 150.071 80.594 150.720 160.710 150.027 110.068 170.813 140.000 10.005 100.492 140.164 10.274 160.111 160.571 160.307 170.293 130.307 170.150 90.163 170.531 160.002 60.545 50.932 150.093 170.000 10.000 120.002 140.159 150.368 170.581 150.440 170.228 170.406 90.282 170.294 160.000 40.000 10.189 160.060 20.036 50.000 120.000 40.897 110.000 170.525 140.025 170.205 170.771 170.000 120.000 10.593 110.108 90.044 60.000 40.000 10.000 110.282 170.589 140.094 160.169 160.466 170.227 160.419 170.125 50.757 140.002 40.334 160.000 10.000 80.000 30.357 150.000 50.000 90.000 10.582 100.513 140.337 100.612 170.000 100.000 10.250 160.352 170.136 170.724 160.655 40.280 80.000 110.046 160.000 10.606 170.559 150.159 70.102 30.445 100.655 90.000 10.310 170.117 60.000 70.000 10.581 150.026 10.000 10.265 170.483 160.084 170.097 170.044 150.865 170.142 170.588 110.351 150.272 20.596 170.000 10.003 100.622 160.720 100.096 170.000 10.771 160.016 150.772 150.000 10.302 140.194 90.214 120.621 160.197 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019