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 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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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
AWCS0.305 140.508 140.225 140.142 110.782 140.634 170.937 140.489 150.578 140.721 120.364 150.355 110.515 120.023 90.764 140.523 110.707 140.264 110.633 140.922 140.507 130.886 10.804 150.179 150.436 160.300 120.656 160.529 30.501 150.394 130.296 160.820 50.603 90.131 30.179 170.619 30.000 10.707 160.865 130.773 60.171 70.010 60.484 140.063 130.463 130.254 130.332 160.649 110.220 110.100 110.729 150.613 150.071 130.582 140.628 70.702 40.424 150.749 20.137 150.000 30.142 130.360 130.863 60.305 140.877 100.000 70.173 50.606 120.337 140.478 120.154 150.000 10.253 140.664 80.000 40.000 90.000 10.000 60.626 130.782 100.302 160.602 70.185 130.282 60.651 130.317 130.000 30.000 70.000 10.022 130.000 40.154 20.876 90.000 10.014 90.063 90.029 170.553 70.467 30.084 130.124 140.157 160.049 120.373 130.252 90.097 150.000 40.219 70.542 30.000 30.392 70.172 80.000 150.339 90.417 80.533 130.093 150.115 100.195 90.000 10.516 100.288 150.741 60.000 10.001 100.233 90.056 140.000 70.159 60.334 70.077 90.000 80.000 110.000 10.000 50.749 130.000 10.411 80.000 10.008 110.452 100.000 10.595 100.000 10.000 10.220 100.006 120.894 120.006 80.000 110.000 10.000 90.000 10.112 60.504 80.404 100.551 30.093 40.129 140.484 100.381 170.000 120.000 10.396 140.000 20.000 20.620 30.402 170.000 30.000 10.000 10.142 90.000 70.000 30.512 90.000 1
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024
ALS-MinkowskiNetcopyleft0.414 20.610 20.322 30.271 20.852 10.710 20.973 10.572 40.719 30.795 20.477 60.506 20.601 30.000 140.804 50.646 30.804 20.344 20.777 10.984 10.671 10.879 20.936 10.342 50.632 70.449 40.817 30.475 100.723 20.798 10.376 80.832 20.693 10.031 90.564 10.510 130.000 10.893 30.905 10.672 160.314 10.000 70.718 10.153 30.542 20.397 30.726 30.752 80.252 80.226 20.916 20.800 10.047 160.807 30.769 10.709 30.630 30.769 10.217 100.000 30.285 10.598 40.846 100.535 10.956 40.000 70.137 110.784 20.464 70.463 130.230 120.000 10.598 30.662 90.000 40.087 20.000 10.135 30.900 20.780 110.703 20.741 10.571 20.149 90.697 70.646 20.000 30.076 20.000 10.025 110.000 40.106 60.981 10.000 10.043 70.113 40.888 20.248 150.404 40.252 60.314 10.220 70.245 20.466 70.366 20.159 20.000 40.149 80.690 20.000 30.531 50.253 30.285 60.460 10.440 50.813 10.230 30.283 60.159 110.000 10.728 10.666 50.958 10.000 10.021 50.252 80.118 50.000 70.445 30.223 100.285 10.194 30.390 20.000 10.475 40.842 70.000 10.455 30.000 10.250 70.458 80.000 10.865 10.000 10.000 10.635 10.359 50.972 10.087 30.447 10.000 10.000 90.000 10.129 20.532 60.446 80.503 50.071 130.135 120.699 40.717 20.097 20.000 10.665 10.000 20.000 21.000 10.752 60.000 30.000 10.000 10.142 90.200 10.259 11.000 10.000 1
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv
OA-CNN-L_ScanNet2000.333 110.558 50.269 90.124 130.821 50.703 30.946 60.569 50.662 40.748 90.487 30.455 40.572 70.000 140.789 90.534 90.736 90.271 80.713 40.949 60.498 140.877 30.860 110.332 70.706 10.474 30.788 70.406 130.637 60.495 110.355 110.805 70.592 120.015 130.396 80.602 60.000 10.799 110.876 70.713 130.276 20.000 70.493 130.080 90.448 140.363 50.661 40.833 60.262 60.125 70.823 120.665 90.076 90.720 80.557 100.637 90.517 90.672 100.227 80.000 30.158 120.496 80.843 110.352 100.835 130.000 70.103 140.711 50.527 40.526 60.320 80.000 10.568 60.625 110.067 10.000 90.000 10.001 50.806 60.836 70.621 100.591 80.373 80.314 50.668 100.398 90.003 20.000 70.000 10.016 160.024 20.043 130.906 60.000 10.052 60.000 100.384 120.330 120.342 80.100 120.223 70.183 130.112 70.476 60.313 70.130 90.196 30.112 120.370 110.000 30.234 120.071 90.160 70.403 60.398 130.492 140.197 60.076 130.272 50.000 10.200 160.560 100.735 70.000 10.000 110.000 120.110 80.002 60.021 80.412 50.000 120.000 80.000 110.000 10.000 50.794 110.000 10.445 50.000 10.022 100.509 70.000 10.517 130.000 10.000 10.001 170.245 70.915 50.024 60.089 70.000 10.262 30.000 10.103 110.524 70.392 110.515 40.013 170.251 40.411 130.662 40.001 110.000 10.473 120.000 20.000 20.150 50.699 90.000 30.000 10.000 10.166 60.000 70.024 20.000 110.000 1
PonderV2 ScanNet2000.346 60.552 80.270 80.175 90.810 70.682 90.950 50.560 70.641 100.761 30.398 130.357 100.570 80.113 20.804 50.603 60.750 70.283 40.681 70.952 50.548 50.874 40.852 130.290 120.700 20.356 110.792 50.445 120.545 130.436 120.351 120.787 100.611 80.050 80.290 140.519 120.000 10.825 100.888 50.842 30.259 30.100 20.558 70.070 120.497 70.247 140.457 110.889 30.248 90.106 100.817 130.691 60.094 70.729 60.636 60.620 120.503 110.660 130.243 70.000 30.212 70.590 50.860 80.400 50.881 90.000 70.202 20.622 100.408 110.499 80.261 100.000 10.385 100.636 100.000 40.000 90.000 10.000 60.433 160.843 60.660 60.574 120.481 40.336 40.677 90.486 60.000 30.030 30.000 10.034 60.000 40.080 80.869 100.000 10.000 100.000 100.540 100.727 30.232 170.115 110.186 100.193 90.000 140.403 110.326 60.103 140.000 40.290 40.392 90.000 30.346 100.062 100.424 50.375 70.431 60.667 40.115 140.082 120.239 70.000 10.504 120.606 80.584 120.000 10.002 90.186 100.104 100.000 70.394 50.384 60.083 80.000 80.007 90.000 10.000 50.880 40.000 10.377 100.000 10.263 60.565 30.000 10.608 90.000 10.000 10.304 70.009 110.924 20.000 90.000 110.000 10.000 90.000 10.128 30.584 20.475 70.412 80.076 110.269 30.621 60.509 90.010 70.000 10.491 110.063 10.000 20.472 40.880 40.000 30.000 10.000 10.179 50.125 20.000 30.441 100.000 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.
PPT-SpUNet-F.T.0.332 120.556 60.270 70.123 140.816 60.682 90.946 60.549 100.657 80.756 50.459 70.376 90.550 110.001 120.807 40.616 40.727 120.267 90.691 50.942 110.530 90.872 50.874 80.330 80.542 140.374 80.792 50.400 140.673 40.572 70.433 20.793 90.623 70.008 160.351 100.594 80.000 10.783 130.876 70.833 40.213 60.000 70.537 80.091 70.519 40.304 80.620 80.942 20.264 50.124 80.855 70.695 50.086 80.646 100.506 160.658 70.535 60.715 40.314 20.000 30.241 40.608 30.897 20.359 80.858 110.000 70.076 170.611 110.392 120.509 70.378 60.000 10.579 40.565 150.000 40.000 90.000 10.000 60.755 70.806 90.661 40.572 130.350 90.181 70.660 120.300 140.000 30.000 70.000 10.023 120.000 40.042 140.930 40.000 10.000 100.077 70.584 90.392 100.339 90.185 100.171 120.308 20.006 130.563 30.256 80.150 40.000 40.002 160.345 120.000 30.045 140.197 50.063 110.323 110.453 40.600 80.163 110.037 150.349 40.000 10.672 30.679 40.753 50.000 10.000 110.000 120.117 60.000 70.000 100.291 80.000 120.000 80.039 70.000 10.000 50.899 20.000 10.374 110.000 10.000 120.545 50.000 10.634 50.000 10.000 10.074 130.223 80.914 60.000 90.021 90.000 10.000 90.000 10.112 60.498 100.649 10.383 100.095 20.135 120.449 110.432 120.008 90.000 10.518 70.000 20.000 20.000 110.796 50.000 30.000 10.000 10.138 130.000 70.000 30.000 110.000 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
DITR0.449 10.629 10.392 10.289 10.851 20.727 10.969 40.600 10.741 20.805 10.519 10.480 30.636 10.014 100.867 10.680 10.849 10.318 30.753 20.982 20.508 120.871 60.934 20.482 10.596 110.551 20.804 40.508 60.729 10.718 20.417 40.886 10.664 30.000 170.500 20.698 10.000 10.913 10.901 30.766 70.113 120.000 70.617 50.168 20.650 10.477 10.826 10.962 10.348 30.300 10.947 10.776 20.160 30.889 10.651 50.720 20.700 10.728 30.317 10.000 30.238 50.664 10.869 40.514 20.998 10.313 30.138 100.815 10.828 10.622 20.421 50.000 10.823 10.817 10.000 40.000 90.000 10.157 20.866 30.991 10.805 10.660 40.571 20.043 120.709 60.642 30.000 30.000 70.000 10.028 100.018 30.134 30.967 20.000 10.150 20.130 20.949 10.855 10.580 10.262 50.314 10.230 50.222 40.498 50.367 10.153 30.869 10.334 20.397 80.000 30.904 10.486 21.000 10.423 40.484 10.632 60.716 10.733 20.862 10.000 10.433 140.710 10.851 20.000 10.034 40.315 30.385 10.000 70.001 90.268 90.066 110.000 80.278 40.000 10.978 10.839 80.000 10.448 40.000 10.579 10.403 120.000 10.647 30.000 10.000 10.411 30.315 60.904 70.420 10.392 20.000 10.091 60.000 10.128 30.564 30.591 30.568 20.079 90.139 91.000 10.714 30.178 10.000 10.606 30.000 20.000 20.148 60.983 10.000 30.000 10.000 10.374 20.000 70.000 30.662 40.000 1
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.
CeCo0.340 70.551 90.247 130.181 60.784 130.661 140.939 130.564 60.624 130.721 120.484 50.429 50.575 50.027 80.774 110.503 140.753 50.242 130.656 110.945 90.534 70.865 70.860 110.177 170.616 80.400 50.818 20.579 10.615 110.367 140.408 60.726 150.633 50.162 10.360 90.619 30.000 10.828 90.873 90.924 20.109 130.083 30.564 60.057 150.475 120.266 110.781 20.767 70.257 70.100 110.825 110.663 100.048 150.620 130.551 120.595 130.532 70.692 80.246 60.000 30.213 60.615 20.861 70.376 70.900 80.000 70.102 150.660 80.321 150.547 50.226 130.000 10.311 130.742 50.011 30.006 80.000 10.000 60.546 150.824 80.345 140.665 20.450 60.435 10.683 80.411 80.338 10.000 70.000 10.030 90.000 40.068 90.892 80.000 10.063 50.000 100.257 130.304 130.387 60.079 140.228 60.190 110.000 140.586 10.347 40.133 70.000 40.037 130.377 100.000 30.384 80.006 160.003 130.421 50.410 100.643 50.171 90.121 90.142 120.000 10.510 110.447 110.474 140.000 10.000 110.286 50.083 110.000 70.000 100.603 10.096 70.063 50.000 110.000 10.000 50.898 30.000 10.429 70.000 10.400 20.550 40.000 10.633 60.000 10.000 10.377 50.000 150.916 40.000 90.000 110.000 10.000 90.000 10.102 120.499 90.296 140.463 60.089 50.304 10.740 30.401 160.010 70.000 10.560 40.000 20.000 20.709 20.652 100.000 30.000 10.000 10.143 80.000 70.000 30.609 50.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
L3DETR-ScanNet_2000.336 80.533 110.279 60.155 100.801 90.689 40.946 60.539 110.660 70.759 40.380 140.333 140.583 40.000 140.788 100.529 100.740 80.261 120.679 90.940 120.525 100.860 80.883 70.226 130.613 90.397 60.720 110.512 50.565 120.620 30.417 40.775 130.629 60.158 20.298 120.579 110.000 10.835 60.883 60.927 10.114 110.079 40.511 100.073 110.508 50.312 60.629 60.861 50.192 140.098 130.908 30.636 110.032 170.563 170.514 150.664 60.505 100.697 70.225 90.000 30.264 20.411 120.860 80.321 130.960 30.058 60.109 130.776 30.526 50.557 30.303 90.000 10.339 120.712 70.000 40.014 70.000 10.000 60.638 120.856 40.641 70.579 110.107 170.119 110.661 110.416 70.000 30.000 70.000 10.007 170.000 40.067 100.910 50.000 10.000 100.000 100.463 110.448 80.294 140.324 10.293 30.211 80.108 80.448 80.068 170.141 60.000 40.330 30.699 10.000 30.256 110.192 60.000 150.355 80.418 70.209 170.146 120.679 30.101 170.000 10.503 130.687 20.671 80.000 10.000 110.174 110.117 60.000 70.122 70.515 20.104 60.259 20.312 30.000 10.000 50.765 120.000 10.369 120.000 10.183 80.422 110.000 10.646 40.000 10.000 10.565 20.001 140.125 170.010 70.002 100.000 10.487 10.000 10.075 140.548 40.420 90.233 140.082 80.138 110.430 120.427 130.000 120.000 10.549 60.000 20.000 20.074 80.409 160.000 30.000 10.000 10.152 70.051 30.000 30.598 60.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
PTv3 ScanNet2000.393 30.592 30.330 20.216 30.851 20.687 60.971 20.586 20.755 10.752 70.505 20.404 70.575 50.000 140.848 20.616 40.761 30.349 10.738 30.978 30.546 60.860 80.926 30.346 40.654 30.384 70.828 10.523 40.699 30.583 60.387 70.822 30.688 20.118 40.474 30.603 50.000 10.832 80.903 20.753 90.140 100.000 70.650 30.109 50.520 30.457 20.497 100.871 40.281 40.192 50.887 40.748 30.168 20.727 70.733 20.740 10.644 20.714 50.190 130.000 30.256 30.449 100.914 10.514 20.759 150.337 10.172 60.692 70.617 30.636 10.325 70.000 10.641 20.782 20.000 40.065 30.000 10.000 60.842 40.903 20.661 40.662 30.612 10.405 20.731 40.566 40.000 30.000 70.000 10.017 150.301 10.088 70.941 30.000 10.077 40.000 100.717 80.790 20.310 120.026 170.264 40.349 10.220 50.397 120.366 20.115 130.000 40.337 10.463 60.000 30.531 50.218 40.593 20.455 20.469 20.708 30.210 40.592 40.108 160.000 10.728 10.682 30.671 80.000 10.000 110.407 10.136 40.022 30.575 10.436 40.259 30.428 10.048 60.000 10.000 50.879 50.000 10.480 20.000 10.133 90.597 20.000 10.690 20.000 10.000 10.009 160.000 150.921 30.000 90.151 50.000 10.000 90.000 10.109 80.494 110.622 20.394 90.073 120.141 70.798 20.528 80.026 50.000 10.551 50.000 20.000 20.134 70.717 80.000 30.000 10.000 10.188 40.000 70.000 30.791 30.000 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)
OctFormer ScanNet200permissive0.326 130.539 100.265 100.131 120.806 80.670 120.943 90.535 120.662 40.705 160.423 90.407 60.505 130.003 110.765 130.582 70.686 150.227 160.680 80.943 100.601 20.854 100.892 60.335 60.417 170.357 100.724 100.453 110.632 70.596 50.432 30.783 110.512 160.021 120.244 150.637 20.000 10.787 120.873 90.743 110.000 170.000 70.534 90.110 40.499 60.289 100.626 70.620 120.168 150.204 40.849 100.679 80.117 50.633 110.684 30.650 80.552 50.684 90.312 30.000 30.175 110.429 110.865 50.413 40.837 120.000 70.145 80.626 90.451 80.487 110.513 30.000 10.529 70.613 120.000 40.033 60.000 10.000 60.828 50.871 30.622 90.587 90.411 70.137 100.645 140.343 120.000 30.000 70.000 10.022 130.000 40.026 170.829 110.000 10.022 80.089 60.842 40.253 140.318 110.296 20.178 110.291 30.224 30.584 20.200 140.132 80.000 40.128 110.227 130.000 30.230 130.047 110.149 80.331 100.412 90.618 70.164 100.102 110.522 30.000 10.655 40.378 120.469 150.000 10.000 110.000 120.105 90.000 70.000 100.483 30.000 120.000 80.028 80.000 10.000 50.906 10.000 10.339 150.000 10.000 120.457 90.000 10.612 80.000 10.000 10.408 40.000 150.900 100.000 90.000 110.000 10.029 80.000 10.074 150.455 150.479 60.427 70.079 90.140 80.496 80.414 140.022 60.000 10.471 130.000 20.000 20.000 110.722 70.000 30.000 10.000 10.138 130.000 70.000 30.000 110.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
BFANet ScanNet200permissive0.360 50.553 70.293 50.193 50.827 40.689 40.970 30.528 130.661 60.753 60.436 80.378 80.469 150.042 70.810 30.654 20.760 40.266 100.659 100.973 40.574 30.849 110.897 50.382 30.546 130.372 90.698 140.491 90.617 100.526 100.436 10.764 140.476 170.101 50.409 60.585 100.000 10.835 60.901 30.810 50.102 140.000 70.688 20.096 60.483 100.264 120.612 90.591 160.358 20.161 60.863 50.707 40.128 40.814 20.669 40.629 100.563 40.651 140.258 50.000 30.194 100.494 90.806 120.394 60.953 50.000 70.233 10.757 40.508 60.556 40.476 40.000 10.573 50.741 60.000 40.000 90.000 10.000 60.000 170.852 50.678 30.616 60.460 50.338 30.710 50.534 50.000 30.025 40.000 10.043 30.000 40.056 120.493 170.000 10.000 100.109 50.785 70.590 60.298 130.282 30.143 130.262 40.053 110.526 40.337 50.215 10.000 40.135 90.510 40.000 30.596 40.043 140.511 30.321 120.459 30.772 20.124 130.060 140.266 60.000 10.574 90.568 90.653 100.000 10.093 10.298 40.239 30.000 70.516 20.129 140.284 20.000 80.431 10.000 10.000 50.848 60.000 10.492 10.000 10.376 30.522 60.000 10.469 170.000 10.000 10.330 60.151 100.875 140.000 90.254 40.000 10.000 90.000 10.088 130.661 10.481 50.255 120.105 10.139 90.666 50.641 50.000 120.000 10.614 20.000 20.000 20.000 110.921 20.000 30.000 10.000 10.497 10.000 70.000 30.000 110.000 1
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025
GSTran0.334 100.533 120.250 120.179 80.799 110.684 70.940 100.554 90.633 110.741 100.405 110.337 120.560 90.060 50.794 80.517 130.732 110.274 50.647 120.948 70.459 160.849 110.864 90.306 90.648 50.282 140.717 120.496 70.624 90.533 80.363 90.821 40.573 140.009 150.411 40.593 90.000 10.841 50.873 90.704 140.242 50.000 70.495 110.041 160.487 80.304 80.439 130.613 130.133 170.055 160.853 80.634 120.075 120.791 50.601 90.574 160.483 130.669 110.217 100.000 30.198 80.518 60.782 140.345 110.914 60.273 50.193 30.598 140.440 90.499 80.570 10.000 10.381 110.775 40.000 40.063 50.000 10.000 60.712 80.752 130.507 120.512 160.158 160.036 130.773 20.361 110.000 30.000 70.000 10.032 70.000 40.032 150.651 150.000 10.000 100.000 100.831 50.595 40.273 160.229 70.200 90.191 100.000 140.425 90.233 120.125 110.000 40.279 50.213 150.003 10.608 30.044 120.138 90.321 120.408 110.593 100.198 50.205 80.139 130.000 10.614 70.609 70.838 40.000 10.014 60.260 60.080 120.010 50.000 100.136 130.136 40.047 60.000 110.000 10.787 30.797 100.000 10.354 140.000 10.372 40.357 140.000 10.507 160.000 10.000 10.121 110.423 30.903 80.028 40.089 70.000 10.252 40.000 10.072 170.465 120.340 120.189 160.020 160.011 160.320 160.606 70.060 30.000 10.496 90.000 20.000 20.070 90.618 130.000 30.000 10.000 10.139 110.047 40.000 30.558 80.000 1
IMFSegNet0.334 90.532 130.251 110.179 70.799 110.683 80.940 100.555 80.631 120.740 110.406 100.336 130.560 90.062 40.795 70.518 120.733 100.274 50.646 130.947 80.458 170.848 130.862 100.305 100.649 40.284 130.713 130.495 80.626 80.527 90.363 90.820 50.574 130.010 140.411 40.597 70.000 10.842 40.873 90.704 140.246 40.000 70.495 110.041 160.486 90.305 70.444 120.604 150.134 160.055 160.852 90.633 130.076 90.792 40.612 80.573 170.484 120.668 120.216 120.000 30.197 90.518 60.784 130.344 120.908 70.283 40.190 40.599 130.439 100.496 100.569 20.000 10.392 90.776 30.000 40.064 40.000 10.000 60.710 90.756 120.508 110.512 160.159 150.034 140.773 20.363 100.000 30.000 70.000 10.032 70.000 40.029 160.648 160.000 10.000 100.000 100.830 60.595 40.274 150.228 80.206 80.188 120.000 140.425 90.237 110.123 120.000 40.277 60.214 140.003 10.610 20.044 120.124 100.320 140.408 110.594 90.196 70.213 70.139 130.000 10.615 60.618 60.839 30.000 10.014 60.260 60.080 120.025 20.000 100.139 120.135 50.035 70.000 110.000 10.793 20.799 90.000 10.357 130.000 10.369 50.359 130.000 10.512 150.000 10.000 10.120 120.424 20.903 80.027 50.091 60.000 10.245 50.000 10.073 160.457 140.340 120.191 150.021 150.009 170.322 150.608 60.060 30.000 10.494 100.000 20.000 20.068 100.624 110.000 30.000 10.000 10.139 110.047 40.000 30.561 70.000 1
ODIN - Sem200permissive0.368 40.562 40.297 40.207 40.800 100.669 130.940 100.575 30.654 90.749 80.487 30.589 10.609 20.001 120.769 120.561 80.752 60.274 50.682 60.926 130.554 40.833 140.921 40.389 20.599 100.591 10.787 80.550 20.657 50.610 40.334 130.803 80.661 40.090 60.408 70.373 150.000 10.912 20.796 170.501 170.169 80.000 70.641 40.196 10.380 170.397 30.641 50.740 90.862 10.213 30.857 60.685 70.216 10.578 160.557 100.685 50.523 80.581 160.312 30.000 30.065 150.000 170.871 30.359 80.988 20.321 20.090 160.704 60.631 20.393 150.246 110.000 10.482 80.565 150.000 40.000 90.000 10.181 10.913 10.468 160.632 80.642 50.259 110.000 170.832 10.663 10.000 30.081 10.000 10.048 20.000 40.376 10.898 70.000 10.157 10.000 100.870 30.000 170.400 50.265 40.242 50.227 60.539 10.370 140.214 130.129 100.000 40.131 100.054 170.000 30.358 90.491 10.462 40.434 30.346 150.454 150.316 20.814 10.828 20.000 10.000 170.220 170.612 110.000 10.000 110.373 20.378 20.000 70.429 40.152 110.077 90.166 40.202 50.000 10.000 50.441 140.000 10.440 60.000 10.000 120.655 10.000 10.626 70.000 10.000 10.228 90.487 10.784 160.000 90.301 30.000 10.426 20.000 10.108 90.460 130.590 40.775 10.088 60.119 150.485 90.791 10.000 120.000 10.256 170.000 20.000 20.000 110.885 30.303 10.000 10.000 10.127 160.000 70.000 30.894 20.000 1
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
LGroundpermissive0.272 150.485 150.184 150.106 150.778 150.676 110.932 150.479 170.572 150.718 140.399 120.265 150.453 160.085 30.745 150.446 150.726 130.232 150.622 150.901 150.512 110.826 150.786 160.178 160.549 120.277 150.659 150.381 150.518 140.295 170.323 140.777 120.599 100.028 100.321 110.363 160.000 10.708 150.858 140.746 100.063 150.022 50.457 150.077 100.476 110.243 150.402 140.397 170.233 100.077 150.720 170.610 160.103 60.629 120.437 170.626 110.446 140.702 60.190 130.005 10.058 160.322 140.702 160.244 150.768 140.000 70.134 120.552 150.279 160.395 140.147 160.000 10.207 150.612 130.000 40.000 90.000 10.000 60.658 110.566 140.323 150.525 150.229 120.179 80.467 170.154 160.000 30.002 50.000 10.051 10.000 40.127 40.703 120.000 10.000 100.216 10.112 160.358 110.547 20.187 90.092 160.156 170.055 100.296 150.252 90.143 50.000 40.014 140.398 70.000 30.028 160.173 70.000 150.265 160.348 140.415 160.179 80.019 160.218 80.000 10.597 80.274 160.565 130.000 10.012 80.000 120.039 160.022 30.000 100.117 150.000 120.000 80.000 110.000 10.000 50.324 160.000 10.384 90.000 10.000 120.251 170.000 10.566 110.000 10.000 10.066 140.404 40.886 130.199 20.000 110.000 10.059 70.000 10.136 10.540 50.127 170.295 110.085 70.143 60.514 70.413 150.000 120.000 10.498 80.000 20.000 20.000 110.623 120.000 30.000 10.000 10.132 150.000 70.000 30.000 110.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 170.455 170.171 160.079 170.766 170.659 150.930 170.494 140.542 170.700 170.314 170.215 170.430 170.121 10.697 170.441 160.683 160.235 140.609 170.895 160.476 150.816 160.770 170.186 140.634 60.216 170.734 90.340 160.471 160.307 160.293 170.591 170.542 150.076 70.205 160.464 140.000 10.484 170.832 160.766 70.052 160.000 70.413 160.059 140.418 150.222 160.318 170.609 140.206 130.112 90.743 140.625 140.076 90.579 150.548 130.590 140.371 160.552 170.081 160.003 20.142 130.201 160.638 170.233 160.686 170.000 70.142 90.444 170.375 130.247 170.198 140.000 10.128 170.454 170.019 20.097 10.000 10.000 60.553 140.557 150.373 130.545 140.164 140.014 160.547 160.174 150.000 30.002 50.000 10.037 40.000 40.063 110.664 140.000 10.000 100.130 20.170 140.152 160.335 100.079 140.110 150.175 140.098 90.175 170.166 150.045 170.207 20.014 140.465 50.000 30.001 170.001 170.046 120.299 150.327 160.537 120.033 160.012 170.186 100.000 10.205 150.377 130.463 160.000 10.058 30.000 120.055 150.041 10.000 100.105 160.000 120.000 80.000 110.000 10.000 50.398 150.000 10.308 170.000 10.000 120.319 150.000 10.543 120.000 10.000 10.062 150.004 130.862 150.000 90.000 110.000 10.000 90.000 10.123 50.316 160.225 150.250 130.094 30.180 50.332 140.441 110.000 120.000 10.310 160.000 20.000 20.000 110.592 140.000 30.000 10.000 10.203 30.000 70.000 30.000 110.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 160.463 160.154 170.102 160.771 160.650 160.932 150.483 160.571 160.710 150.331 160.250 160.492 140.044 60.703 160.419 170.606 170.227 160.621 160.865 170.531 80.771 170.813 140.291 110.484 150.242 160.612 170.282 170.440 170.351 150.299 150.622 160.593 110.027 110.293 130.310 170.000 10.757 140.858 140.737 120.150 90.164 10.368 170.084 80.381 160.142 170.357 150.720 100.214 120.092 140.724 160.596 170.056 140.655 90.525 140.581 150.352 170.594 150.056 170.000 30.014 170.224 150.772 150.205 170.720 160.000 70.159 70.531 160.163 170.294 160.136 170.000 10.169 160.589 140.000 40.000 90.000 10.002 40.663 100.466 170.265 170.582 100.337 100.016 150.559 150.084 170.000 30.000 70.000 10.036 50.000 40.125 50.670 130.000 10.102 30.071 80.164 150.406 90.386 70.046 160.068 170.159 150.117 60.284 160.111 160.094 160.000 40.000 170.197 160.000 30.044 150.013 150.002 140.228 170.307 170.588 110.025 170.545 50.134 150.000 10.655 40.302 140.282 170.000 10.060 20.000 120.035 170.000 70.000 100.097 170.000 120.000 80.005 100.000 10.000 50.096 170.000 10.334 160.000 10.000 120.274 160.000 10.513 140.000 10.000 10.280 80.194 90.897 110.000 90.000 110.000 10.000 90.000 10.108 90.279 170.189 160.141 170.059 140.272 20.307 170.445 100.003 100.000 10.353 150.000 20.026 10.000 110.581 150.001 20.000 10.000 10.093 170.002 60.000 30.000 110.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019