Presenting the ScanNet200 Benchmark

We present the ScanNet200 benchmark, which studies an order of magnitude more class categories than previous version of ScanNet. The scene geometry is shared within the two tasks, but the parsing of surface annotation allows for a larger vocabulary and more realistic setting for in the wild 3D understanding methods.

The ScanNet200 benchmark includes both finer-grained categories as well as a large number of previously unaddressed classes. This induces a much more challenging setting regarding the diversity of naturally observed semantic classes seen in the raw ScanNet RGB-D observations, where the data also reflects naturally encountered class imbalances. The difference in category frequencies between ScanNet and ScanNet200 can be seen in the Figure above.

ScanNet200 Benchmark

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 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 by
BFANet ScanNet200permissive0.360 30.553 50.293 30.193 30.483 70.096 40.266 40.000 10.000 30.000 10.298 110.255 100.661 10.810 50.810 20.194 70.785 30.000 30.000 130.161 40.000 70.494 60.382 10.574 30.258 30.000 60.372 70.000 10.000 30.043 100.436 60.000 70.000 10.239 10.000 20.901 30.105 10.689 30.025 30.128 20.614 20.436 10.493 130.000 10.000 20.526 40.546 90.109 40.651 110.953 30.753 50.101 50.143 90.897 30.000 10.431 10.469 110.000 70.522 50.337 40.661 50.459 20.409 30.666 40.102 100.508 40.757 30.000 50.060 100.970 30.497 10.000 10.376 20.511 20.262 40.688 20.921 10.617 60.321 100.590 30.491 50.556 30.000 30.000 10.481 30.093 10.043 20.284 20.000 30.875 110.135 60.669 40.124 90.394 50.849 100.298 20.000 10.476 130.088 110.042 50.000 40.000 10.254 20.653 70.741 30.215 10.573 40.852 40.266 60.654 10.056 100.835 20.000 40.492 10.000 10.000 50.000 30.612 70.000 20.000 50.000 10.616 40.469 130.460 40.698 100.516 20.000 10.378 60.563 30.476 20.863 40.574 70.330 50.000 80.282 30.000 10.760 30.710 20.233 10.000 80.641 30.814 10.000 10.585 70.053 90.000 50.000 10.629 80.000 20.000 10.678 20.528 90.534 30.129 100.596 10.973 30.264 80.772 20.526 60.139 90.707 30.000 10.000 90.764 100.591 120.848 60.000 10.827 30.338 30.806 100.000 10.568 60.151 60.358 10.659 80.510 4
PTv3 ScanNet2000.393 20.592 20.330 10.216 20.520 20.109 30.108 120.000 10.337 10.000 10.310 100.394 70.494 100.753 80.848 10.256 30.717 40.000 30.842 20.192 30.065 30.449 70.346 20.546 50.190 90.000 60.384 50.000 10.000 30.218 20.505 10.791 20.000 10.136 20.000 20.903 20.073 100.687 50.000 60.168 10.551 40.387 60.941 20.000 10.000 20.397 90.654 30.000 90.714 40.759 110.752 60.118 40.264 30.926 20.000 10.048 40.575 30.000 70.597 10.366 10.755 10.469 10.474 20.798 10.140 70.617 10.692 50.000 50.592 20.971 20.188 30.000 10.133 60.593 10.349 10.650 30.717 60.699 20.455 20.790 10.523 30.636 10.301 10.000 10.622 20.000 80.017 110.259 30.000 30.921 30.337 10.733 20.210 20.514 20.860 70.407 10.000 10.688 20.109 70.000 100.000 40.000 10.151 30.671 50.782 10.115 90.641 10.903 10.349 10.616 30.088 50.832 40.000 40.480 20.000 10.428 10.000 30.497 80.000 20.000 50.000 10.662 30.690 20.612 10.828 10.575 10.000 10.404 50.644 10.325 40.887 30.728 10.009 120.134 60.026 130.000 10.761 20.731 10.172 40.077 20.528 40.727 40.000 10.603 40.220 30.022 20.000 10.740 10.000 20.000 10.661 30.586 10.566 20.436 40.531 20.978 20.457 10.708 30.583 40.141 70.748 20.000 10.026 20.822 20.871 30.879 50.000 10.851 20.405 20.914 10.000 10.682 20.000 110.281 20.738 20.463 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)
ALS-MinkowskiNetcopyleft0.414 10.610 10.322 20.271 10.542 10.153 10.159 90.000 10.000 30.000 10.404 30.503 30.532 50.672 130.804 40.285 10.888 10.000 30.900 10.226 10.087 20.598 30.342 30.671 10.217 80.087 20.449 20.000 10.000 30.253 10.477 41.000 10.000 10.118 30.000 20.905 10.071 110.710 10.076 10.047 120.665 10.376 70.981 10.000 10.000 20.466 60.632 50.113 30.769 10.956 20.795 10.031 80.314 10.936 10.000 10.390 20.601 10.000 70.458 70.366 10.719 20.440 40.564 10.699 30.314 10.464 50.784 10.200 10.283 40.973 10.142 80.000 10.250 40.285 40.220 50.718 10.752 40.723 10.460 10.248 120.475 60.463 100.000 30.000 10.446 60.021 40.025 70.285 10.000 30.972 10.149 50.769 10.230 10.535 10.879 20.252 40.000 10.693 10.129 20.000 100.000 40.000 10.447 10.958 10.662 60.159 20.598 20.780 100.344 20.646 20.106 40.893 10.135 10.455 30.000 10.194 30.259 10.726 20.475 10.000 50.000 10.741 10.865 10.571 20.817 30.445 30.000 10.506 10.630 20.230 80.916 10.728 10.635 11.000 10.252 40.000 10.804 10.697 30.137 80.043 50.717 10.807 20.000 10.510 100.245 10.000 50.000 10.709 20.000 20.000 10.703 10.572 20.646 10.223 90.531 20.984 10.397 20.813 10.798 10.135 110.800 10.000 10.097 10.832 10.752 70.842 70.000 10.852 10.149 90.846 80.000 10.666 40.359 20.252 60.777 10.690 2
OA-CNN-L_ScanNet2000.333 70.558 30.269 70.124 90.448 110.080 70.272 30.000 10.000 30.000 10.342 60.515 20.524 60.713 120.789 60.158 90.384 80.000 30.806 40.125 50.000 70.496 50.332 50.498 120.227 60.024 30.474 10.000 10.003 20.071 70.487 20.000 70.000 10.110 60.000 20.876 60.013 130.703 20.000 60.076 70.473 90.355 80.906 50.000 10.000 20.476 50.706 10.000 90.672 90.835 90.748 70.015 120.223 50.860 70.000 10.000 90.572 50.000 70.509 60.313 60.662 30.398 100.396 40.411 110.276 20.527 20.711 40.000 50.076 90.946 50.166 50.000 10.022 70.160 50.183 90.493 90.699 70.637 40.403 40.330 90.406 90.526 50.024 20.000 10.392 90.000 80.016 120.000 80.196 20.915 50.112 80.557 70.197 30.352 80.877 30.000 80.000 10.592 100.103 90.000 100.067 10.000 10.089 40.735 40.625 80.130 80.568 50.836 60.271 40.534 70.043 110.799 70.001 30.445 40.000 10.000 50.024 20.661 30.000 20.262 20.000 10.591 60.517 110.373 70.788 60.021 70.000 10.455 20.517 70.320 50.823 80.200 130.001 130.150 50.100 80.000 10.736 70.668 60.103 110.052 40.662 20.720 50.000 10.602 50.112 50.002 40.000 10.637 70.000 20.000 10.621 80.569 30.398 70.412 50.234 80.949 50.363 30.492 110.495 70.251 40.665 70.000 10.001 80.805 40.833 50.794 80.000 10.821 40.314 50.843 90.000 10.560 70.245 30.262 40.713 30.370 10
CeCo0.340 50.551 70.247 90.181 40.475 90.057 130.142 100.000 10.000 30.000 10.387 40.463 40.499 80.924 20.774 80.213 50.257 90.000 30.546 110.100 90.006 60.615 10.177 130.534 60.246 40.000 60.400 30.000 10.338 10.006 120.484 30.609 30.000 10.083 90.000 20.873 80.089 50.661 100.000 60.048 110.560 30.408 50.892 60.000 10.000 20.586 10.616 60.000 90.692 70.900 40.721 80.162 10.228 40.860 70.000 10.000 90.575 30.083 30.550 30.347 30.624 90.410 90.360 50.740 20.109 90.321 110.660 60.000 50.121 50.939 90.143 70.000 10.400 10.003 90.190 80.564 40.652 80.615 70.421 30.304 100.579 10.547 40.000 30.000 10.296 100.000 80.030 60.096 50.000 30.916 40.037 90.551 80.171 50.376 60.865 60.286 30.000 10.633 30.102 100.027 60.011 30.000 10.000 70.474 100.742 20.133 60.311 90.824 70.242 90.503 100.068 70.828 50.000 40.429 50.000 10.063 40.000 30.781 10.000 20.000 50.000 10.665 20.633 50.450 50.818 20.000 80.000 10.429 30.532 60.226 90.825 70.510 90.377 40.709 20.079 100.000 10.753 40.683 40.102 120.063 30.401 120.620 100.000 10.619 20.000 120.000 50.000 10.595 110.000 20.000 10.345 100.564 40.411 60.603 10.384 50.945 60.266 70.643 50.367 100.304 10.663 80.000 10.010 40.726 110.767 60.898 30.000 10.784 90.435 10.861 50.000 10.447 80.000 110.257 50.656 90.377 9
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS0.305 100.508 100.225 100.142 70.463 100.063 110.195 70.000 10.000 30.000 10.467 20.551 10.504 70.773 60.764 100.142 100.029 130.000 30.626 90.100 90.000 70.360 100.179 110.507 110.137 110.006 50.300 100.000 10.000 30.172 60.364 110.512 50.000 10.056 100.000 20.865 100.093 40.634 130.000 60.071 90.396 110.296 120.876 70.000 10.000 20.373 100.436 120.063 80.749 20.877 60.721 80.131 30.124 100.804 110.000 10.000 90.515 80.010 60.452 90.252 80.578 100.417 70.179 130.484 80.171 50.337 100.606 100.000 50.115 60.937 100.142 80.000 10.008 80.000 110.157 120.484 100.402 130.501 110.339 70.553 40.529 20.478 90.000 30.000 10.404 80.001 70.022 90.077 70.000 30.894 90.219 40.628 60.093 110.305 100.886 10.233 50.000 10.603 70.112 50.023 70.000 40.000 10.000 70.741 30.664 50.097 110.253 100.782 90.264 70.523 90.154 10.707 120.000 40.411 60.000 10.000 50.000 30.332 120.000 20.000 50.000 10.602 50.595 80.185 110.656 120.159 50.000 10.355 90.424 110.154 110.729 110.516 80.220 80.620 30.084 90.000 10.707 100.651 90.173 30.014 70.381 130.582 110.000 10.619 20.049 100.000 50.000 10.702 30.000 20.000 10.302 120.489 110.317 90.334 70.392 40.922 100.254 90.533 100.394 90.129 130.613 110.000 10.000 90.820 30.649 90.749 100.000 10.782 100.282 60.863 40.000 10.288 120.006 80.220 90.633 100.542 3
LGroundpermissive0.272 110.485 110.184 110.106 110.476 80.077 80.218 60.000 10.000 30.000 10.547 10.295 90.540 40.746 90.745 110.058 120.112 120.005 10.658 70.077 130.000 70.322 110.178 120.512 100.190 90.199 10.277 110.000 10.000 30.173 50.399 80.000 70.000 10.039 120.000 20.858 110.085 60.676 80.002 40.103 40.498 70.323 100.703 100.000 10.000 20.296 110.549 80.216 10.702 50.768 100.718 100.028 90.092 120.786 120.000 10.000 90.453 120.022 50.251 130.252 80.572 110.348 110.321 70.514 60.063 110.279 120.552 110.000 50.019 120.932 110.132 120.000 10.000 90.000 110.156 130.457 110.623 90.518 100.265 120.358 80.381 110.395 110.000 30.000 10.127 130.012 50.051 10.000 80.000 30.886 100.014 100.437 130.179 40.244 110.826 110.000 80.000 10.599 80.136 10.085 30.000 40.000 10.000 70.565 90.612 100.143 40.207 110.566 110.232 110.446 110.127 20.708 110.000 40.384 70.000 10.000 50.000 30.402 100.000 20.059 30.000 10.525 130.566 90.229 100.659 110.000 80.000 10.265 110.446 100.147 120.720 130.597 60.066 100.000 80.187 50.000 10.726 90.467 130.134 90.000 80.413 110.629 90.000 10.363 120.055 80.022 20.000 10.626 90.000 20.000 10.323 110.479 130.154 120.117 110.028 120.901 110.243 110.415 120.295 130.143 60.610 120.000 10.000 90.777 80.397 130.324 120.000 10.778 110.179 80.702 120.000 10.274 130.404 10.233 80.622 110.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
PonderV2 ScanNet2000.346 40.552 60.270 60.175 50.497 60.070 100.239 50.000 10.000 30.000 10.232 130.412 60.584 20.842 30.804 40.212 60.540 60.000 30.433 120.106 80.000 70.590 40.290 80.548 40.243 50.000 60.356 90.000 10.000 30.062 80.398 90.441 60.000 10.104 80.000 20.888 40.076 90.682 60.030 20.094 50.491 80.351 90.869 80.000 10.063 10.403 80.700 20.000 90.660 100.881 50.761 20.050 70.186 60.852 90.000 10.007 70.570 60.100 20.565 20.326 50.641 80.431 50.290 100.621 50.259 30.408 70.622 80.125 20.082 80.950 40.179 40.000 10.263 30.424 30.193 70.558 50.880 20.545 90.375 50.727 20.445 80.499 70.000 30.000 10.475 50.002 60.034 50.083 60.000 30.924 20.290 30.636 50.115 100.400 40.874 40.186 60.000 10.611 60.128 30.113 20.000 40.000 10.000 70.584 80.636 70.103 100.385 70.843 50.283 30.603 50.080 60.825 60.000 40.377 80.000 10.000 50.000 30.457 90.000 20.000 50.000 10.574 100.608 70.481 30.792 40.394 40.000 10.357 80.503 90.261 70.817 90.504 100.304 60.472 40.115 70.000 10.750 50.677 50.202 20.000 80.509 50.729 30.000 10.519 90.000 120.000 50.000 10.620 100.000 20.000 10.660 50.560 50.486 40.384 60.346 60.952 40.247 100.667 40.436 80.269 30.691 50.000 10.010 40.787 60.889 20.880 40.000 10.810 60.336 40.860 60.000 10.606 50.009 70.248 70.681 50.392 8
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 80.556 40.270 50.123 100.519 30.091 50.349 20.000 10.000 30.000 10.339 70.383 80.498 90.833 40.807 30.241 40.584 50.000 30.755 50.124 60.000 70.608 20.330 60.530 80.314 10.000 60.374 60.000 10.000 30.197 30.459 50.000 70.000 10.117 40.000 20.876 60.095 20.682 60.000 60.086 60.518 60.433 20.930 30.000 10.000 20.563 30.542 100.077 60.715 30.858 70.756 40.008 130.171 80.874 60.000 10.039 50.550 70.000 70.545 40.256 70.657 70.453 30.351 60.449 90.213 40.392 80.611 90.000 50.037 110.946 50.138 100.000 10.000 90.063 70.308 20.537 60.796 30.673 30.323 90.392 70.400 100.509 60.000 30.000 10.649 10.000 80.023 80.000 80.000 30.914 60.002 120.506 120.163 70.359 70.872 50.000 80.000 10.623 50.112 50.001 90.000 40.000 10.021 50.753 20.565 120.150 30.579 30.806 80.267 50.616 30.042 120.783 90.000 40.374 90.000 10.000 50.000 30.620 60.000 20.000 50.000 10.572 110.634 40.350 80.792 40.000 80.000 10.376 70.535 50.378 30.855 50.672 30.074 90.000 80.185 60.000 10.727 80.660 80.076 130.000 80.432 80.646 70.000 10.594 60.006 110.000 50.000 10.658 50.000 20.000 10.661 30.549 60.300 100.291 80.045 100.942 80.304 50.600 70.572 50.135 110.695 40.000 10.008 60.793 50.942 10.899 20.000 10.816 50.181 70.897 20.000 10.679 30.223 40.264 30.691 40.345 11
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 60.533 90.279 40.155 60.508 40.073 90.101 130.000 10.058 20.000 10.294 120.233 120.548 30.927 10.788 70.264 20.463 70.000 30.638 80.098 110.014 50.411 90.226 90.525 90.225 70.010 40.397 40.000 10.000 30.192 40.380 100.598 40.000 10.117 40.000 20.883 50.082 70.689 30.000 60.032 130.549 50.417 40.910 40.000 10.000 20.448 70.613 70.000 90.697 60.960 10.759 30.158 20.293 20.883 50.000 10.312 30.583 20.079 40.422 100.068 130.660 60.418 60.298 80.430 100.114 80.526 30.776 20.051 30.679 10.946 50.152 60.000 10.183 50.000 110.211 60.511 80.409 120.565 80.355 60.448 50.512 40.557 20.000 30.000 10.420 70.000 80.007 130.104 40.000 30.125 130.330 20.514 110.146 80.321 90.860 70.174 70.000 10.629 40.075 120.000 100.000 40.000 10.002 60.671 50.712 40.141 50.339 80.856 30.261 80.529 80.067 80.835 20.000 40.369 100.000 10.259 20.000 30.629 40.000 20.487 10.000 10.579 90.646 30.107 130.720 90.122 60.000 10.333 100.505 80.303 60.908 20.503 110.565 20.074 70.324 10.000 10.740 60.661 70.109 100.000 80.427 90.563 130.000 10.579 80.108 60.000 50.000 10.664 40.000 20.000 10.641 60.539 70.416 50.515 20.256 70.940 90.312 40.209 130.620 20.138 100.636 90.000 10.000 90.775 90.861 40.765 90.000 10.801 80.119 110.860 60.000 10.687 10.001 100.192 120.679 70.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OctFormer ScanNet200permissive0.326 90.539 80.265 80.131 80.499 50.110 20.522 10.000 10.000 30.000 10.318 90.427 50.455 110.743 100.765 90.175 80.842 20.000 30.828 30.204 20.033 40.429 80.335 40.601 20.312 20.000 60.357 80.000 10.000 30.047 90.423 70.000 70.000 10.105 70.000 20.873 80.079 80.670 90.000 60.117 30.471 100.432 30.829 90.000 10.000 20.584 20.417 130.089 50.684 80.837 80.705 120.021 110.178 70.892 40.000 10.028 60.505 90.000 70.457 80.200 100.662 30.412 80.244 110.496 70.000 130.451 60.626 70.000 50.102 70.943 80.138 100.000 10.000 90.149 60.291 30.534 70.722 50.632 50.331 80.253 110.453 70.487 80.000 30.000 10.479 40.000 80.022 90.000 80.000 30.900 70.128 70.684 30.164 60.413 30.854 90.000 80.000 10.512 120.074 130.003 80.000 40.000 10.000 70.469 110.613 90.132 70.529 60.871 20.227 120.582 60.026 130.787 80.000 40.339 110.000 10.000 50.000 30.626 50.000 20.029 40.000 10.587 70.612 60.411 60.724 80.000 80.000 10.407 40.552 40.513 10.849 60.655 40.408 30.000 80.296 20.000 10.686 110.645 100.145 60.022 60.414 100.633 80.000 10.637 10.224 20.000 50.000 10.650 60.000 20.000 10.622 70.535 80.343 80.483 30.230 90.943 70.289 60.618 60.596 30.140 80.679 60.000 10.022 30.783 70.620 100.906 10.000 10.806 70.137 100.865 30.000 10.378 90.000 110.168 130.680 60.227 12
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
Minkowski 34Dpermissive0.253 120.463 120.154 130.102 120.381 130.084 60.134 110.000 10.000 30.000 10.386 50.141 130.279 130.737 110.703 120.014 130.164 110.000 30.663 60.092 120.000 70.224 120.291 70.531 70.056 130.000 60.242 120.000 10.000 30.013 110.331 120.000 70.000 10.035 130.001 10.858 110.059 120.650 120.000 60.056 100.353 120.299 110.670 110.000 10.000 20.284 120.484 110.071 70.594 120.720 120.710 110.027 100.068 130.813 100.000 10.005 80.492 100.164 10.274 120.111 120.571 120.307 130.293 90.307 130.150 60.163 130.531 120.002 40.545 30.932 110.093 130.000 10.000 90.002 100.159 110.368 130.581 110.440 130.228 130.406 60.282 130.294 120.000 30.000 10.189 120.060 20.036 40.000 80.000 30.897 80.000 130.525 100.025 130.205 130.771 130.000 80.000 10.593 90.108 80.044 40.000 40.000 10.000 70.282 130.589 110.094 120.169 120.466 130.227 120.419 130.125 30.757 100.002 20.334 120.000 10.000 50.000 30.357 110.000 20.000 50.000 10.582 80.513 120.337 90.612 130.000 80.000 10.250 120.352 130.136 130.724 120.655 40.280 70.000 80.046 120.000 10.606 130.559 110.159 50.102 10.445 60.655 60.000 10.310 130.117 40.000 50.000 10.581 130.026 10.000 10.265 130.483 120.084 130.097 130.044 110.865 130.142 130.588 80.351 110.272 20.596 130.000 10.003 70.622 120.720 80.096 130.000 10.771 120.016 120.772 110.000 10.302 110.194 50.214 100.621 120.197 13
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrainpermissive0.249 130.455 130.171 120.079 130.418 120.059 120.186 80.000 10.000 30.000 10.335 80.250 110.316 120.766 70.697 130.142 100.170 100.003 20.553 100.112 70.097 10.201 130.186 100.476 130.081 120.000 60.216 130.000 10.000 30.001 130.314 130.000 70.000 10.055 110.000 20.832 130.094 30.659 110.002 40.076 70.310 130.293 130.664 120.000 10.000 20.175 130.634 40.130 20.552 130.686 130.700 130.076 60.110 110.770 130.000 10.000 90.430 130.000 70.319 110.166 110.542 130.327 120.205 120.332 120.052 120.375 90.444 130.000 50.012 130.930 130.203 20.000 10.000 90.046 80.175 100.413 120.592 100.471 120.299 110.152 130.340 120.247 130.000 30.000 10.225 110.058 30.037 30.000 80.207 10.862 120.014 100.548 90.033 120.233 120.816 120.000 80.000 10.542 110.123 40.121 10.019 20.000 10.000 70.463 120.454 130.045 130.128 130.557 120.235 100.441 120.063 90.484 130.000 40.308 130.000 10.000 50.000 30.318 130.000 20.000 50.000 10.545 120.543 100.164 120.734 70.000 80.000 10.215 130.371 120.198 100.743 100.205 120.062 110.000 80.079 100.000 10.683 120.547 120.142 70.000 80.441 70.579 120.000 10.464 110.098 70.041 10.000 10.590 120.000 20.000 10.373 90.494 100.174 110.105 120.001 130.895 120.222 120.537 90.307 120.180 50.625 100.000 10.000 90.591 130.609 110.398 110.000 10.766 130.014 130.638 130.000 10.377 100.004 90.206 110.609 130.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


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




Method Infoavgalarm 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 extinguisherfireplacefolded 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 cleanerventwardrobewashing 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 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 by
Mask3D Scannet2000.388 10.542 10.357 10.237 10.610 10.091 10.125 50.000 10.000 10.000 10.065 30.668 10.451 11.000 10.955 10.640 10.500 10.039 10.125 20.063 20.409 10.311 20.291 10.609 30.266 10.000 10.163 10.000 10.008 10.044 20.496 11.000 10.000 10.018 20.000 10.756 10.573 10.808 20.000 10.010 10.042 30.130 30.552 10.042 10.000 11.000 10.725 40.750 10.883 11.000 10.832 40.024 20.107 10.614 30.226 10.250 10.628 20.792 10.677 20.400 10.741 10.278 10.511 10.077 50.111 10.313 20.715 20.302 10.017 30.200 20.000 10.188 10.000 10.178 20.736 11.000 10.615 10.514 10.409 20.380 50.600 10.000 10.000 10.400 10.013 20.254 10.381 10.000 10.123 40.400 10.839 10.258 10.463 10.926 10.265 10.000 10.857 20.099 10.021 20.500 10.027 10.028 11.000 10.502 50.016 10.076 40.500 10.612 10.578 10.005 20.597 20.194 10.497 10.000 10.500 10.000 20.323 40.000 11.000 10.000 10.748 10.708 20.050 40.890 21.000 10.008 20.151 30.301 11.000 11.000 10.792 30.945 11.000 10.511 10.004 20.753 10.776 20.287 20.020 20.003 40.974 30.033 10.412 50.000 10.000 20.000 20.667 10.000 10.000 10.491 10.676 20.352 10.335 10.060 20.822 50.527 21.000 10.517 10.606 10.853 10.000 10.004 10.806 11.000 10.727 10.000 10.042 20.739 20.000 10.399 30.391 10.504 10.591 10.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
CSC-Pretrain Inst.permissive0.209 40.361 50.157 40.085 40.506 20.007 30.500 10.000 10.000 10.000 10.000 50.093 50.221 40.667 40.524 50.400 30.000 20.000 30.000 30.004 40.000 20.000 50.109 50.589 40.000 40.000 10.059 50.000 10.000 20.000 30.322 20.000 20.000 10.000 30.000 10.405 30.055 40.700 50.000 10.000 20.028 40.091 50.083 30.000 20.000 10.667 20.768 20.000 40.807 31.000 10.776 50.000 30.000 50.340 50.000 30.000 30.103 50.000 30.750 10.200 30.634 50.053 50.246 30.677 20.006 50.198 30.432 40.000 20.000 40.050 40.000 10.000 30.000 10.111 50.356 40.500 20.188 50.000 40.220 40.448 20.050 50.000 10.000 10.000 30.000 30.032 50.000 30.000 10.396 20.000 40.573 40.000 50.228 30.747 40.000 30.000 10.573 50.021 50.000 30.000 30.000 20.000 30.500 40.573 30.000 20.000 50.125 40.592 30.364 50.000 30.450 50.000 20.364 20.000 10.000 30.000 20.340 30.000 10.000 20.000 10.610 30.833 10.221 10.702 50.000 30.000 30.135 50.094 40.125 20.571 40.500 40.143 50.000 20.125 30.000 30.618 20.667 40.115 50.000 30.125 21.000 10.000 30.500 20.000 10.000 20.000 20.502 40.000 10.000 10.312 40.248 50.050 40.000 50.000 30.997 30.420 30.500 40.149 50.451 20.748 20.000 10.000 20.636 30.667 50.600 20.000 10.000 30.278 50.000 10.333 40.000 50.294 20.381 50.110 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.246 30.413 30.170 30.130 30.455 50.003 50.500 10.000 10.000 10.000 10.017 40.333 40.111 51.000 10.681 40.400 30.000 20.000 31.000 10.003 50.000 20.167 30.190 20.637 20.067 30.000 10.081 30.000 10.000 20.000 30.264 40.000 20.000 10.000 30.000 10.387 40.031 50.754 30.000 10.000 20.151 20.135 20.056 40.000 20.000 10.582 40.589 50.500 20.815 21.000 10.903 10.000 30.097 20.588 40.000 30.000 30.234 30.000 30.500 30.400 10.682 40.156 30.159 40.750 10.046 30.125 40.660 30.000 20.200 20.000 50.000 10.000 30.000 10.164 30.402 30.500 20.373 30.025 30.143 50.426 30.317 20.000 10.000 10.000 30.000 30.063 30.000 30.000 10.000 50.000 40.575 30.250 20.241 20.772 30.000 30.000 10.653 40.034 30.000 30.000 30.000 20.000 31.000 10.561 40.000 20.100 20.500 10.541 40.452 30.000 30.581 30.000 20.364 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.568 40.511 40.167 30.857 30.000 30.000 30.164 20.112 30.000 50.530 51.000 10.286 30.000 20.125 30.000 30.464 50.706 30.208 40.000 30.125 20.744 40.000 30.500 20.000 10.000 20.000 20.511 30.000 10.000 10.344 20.541 30.068 30.333 20.000 31.000 10.196 40.533 30.318 30.000 40.748 30.000 10.000 20.690 21.000 10.400 30.000 10.000 30.667 30.000 10.333 40.333 20.270 30.399 30.083 4
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
TD3D Scannet200permissive0.320 20.501 20.264 20.164 20.506 30.062 20.500 10.000 10.000 10.000 10.208 10.431 20.252 31.000 10.733 30.587 20.000 20.008 20.000 30.106 10.000 20.356 10.123 40.686 10.101 20.000 10.152 20.000 10.000 20.226 10.280 30.000 20.000 10.250 10.000 10.619 20.061 30.841 10.000 10.000 20.167 10.194 10.333 20.000 20.000 10.667 20.820 10.250 30.790 41.000 10.879 20.077 10.094 30.708 10.217 20.049 20.634 10.792 10.331 40.033 50.716 20.159 20.396 20.331 40.099 20.415 10.842 10.000 20.458 10.542 10.000 10.101 20.000 10.218 10.513 20.500 20.458 20.104 20.516 10.456 10.268 40.000 10.000 10.400 10.022 10.233 20.143 20.000 10.677 10.400 10.504 50.095 30.083 50.890 20.061 20.000 10.906 10.076 20.231 10.125 20.000 20.003 20.792 30.881 10.000 20.098 30.125 40.498 50.459 20.063 10.715 10.000 20.241 40.000 10.396 20.063 10.605 10.000 10.000 20.000 10.448 50.629 30.202 20.967 10.250 20.038 10.192 10.185 20.083 41.000 11.000 10.857 20.000 20.470 20.012 10.565 30.798 10.621 10.111 10.500 11.000 10.017 20.509 10.000 10.008 11.000 10.525 20.000 10.000 10.332 30.679 10.264 20.333 20.267 11.000 10.549 10.299 50.387 20.328 30.744 40.000 10.000 20.435 51.000 10.283 40.000 10.196 10.817 10.000 10.472 10.222 30.123 40.560 20.156 2
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Minkowski 34D Inst.permissive0.203 50.369 40.134 50.078 50.479 40.003 40.500 10.000 10.000 10.000 10.100 20.371 30.300 20.667 40.746 20.400 30.000 20.000 30.000 30.031 30.000 20.074 40.165 30.413 50.000 40.000 10.070 40.000 10.000 20.000 30.221 50.000 20.000 10.000 30.000 10.372 50.070 20.706 40.000 10.000 20.000 50.123 40.033 50.000 20.000 10.422 50.732 30.000 40.778 51.000 10.845 30.000 30.090 40.636 20.000 30.000 30.158 40.000 30.250 50.050 40.693 30.123 40.051 50.385 30.009 40.118 50.406 50.000 20.000 40.200 20.000 10.000 30.000 10.133 40.307 50.500 20.251 40.000 40.281 30.402 40.317 20.000 10.000 10.000 30.000 30.060 40.000 30.000 10.396 20.200 30.669 20.021 40.218 40.720 50.000 30.000 10.696 30.025 40.000 30.000 30.000 20.000 30.125 50.596 20.000 20.191 10.500 10.595 20.369 40.000 30.500 40.000 20.143 50.000 10.000 30.000 20.226 50.000 10.000 20.000 10.701 20.511 40.000 50.851 40.000 30.000 30.150 40.052 50.100 30.981 30.500 40.286 30.000 20.000 50.000 30.545 40.522 50.250 30.000 30.000 50.522 50.000 30.500 20.000 10.000 20.000 20.282 50.000 10.000 10.178 50.382 40.018 50.056 40.000 30.997 30.107 50.677 20.313 40.000 40.726 50.000 10.000 20.583 40.903 40.200 50.000 10.000 30.333 40.000 10.442 20.083 40.109 50.387 40.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3-PPT-ALCcopyleft0.798 10.911 100.812 200.854 60.770 110.856 130.555 130.943 10.660 230.735 20.979 10.606 60.492 10.792 30.934 30.841 20.819 40.716 70.947 90.906 10.822 1
PTv3 ScanNet0.794 20.941 30.813 190.851 80.782 60.890 20.597 10.916 30.696 80.713 40.979 10.635 10.384 30.793 20.907 90.821 50.790 320.696 120.967 30.903 20.805 2
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)
PonderV20.785 30.978 10.800 280.833 240.788 40.853 170.545 170.910 60.713 10.705 50.979 10.596 80.390 20.769 130.832 420.821 50.792 310.730 10.975 10.897 50.785 5
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.
Mix3Dpermissive0.781 40.964 20.855 10.843 170.781 70.858 120.575 60.831 340.685 140.714 30.979 10.594 90.310 270.801 10.892 170.841 20.819 40.723 40.940 140.887 70.725 25
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 50.861 210.818 140.836 210.790 30.875 40.576 50.905 70.704 50.739 10.969 110.611 20.349 110.756 230.958 10.702 460.805 150.708 80.916 330.898 40.801 3
TTT-KD0.773 60.646 920.818 140.809 360.774 90.878 30.581 20.943 10.687 120.704 60.978 50.607 50.336 160.775 90.912 70.838 40.823 20.694 130.967 30.899 30.794 4
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS0.772 70.939 40.824 60.854 60.771 100.840 310.564 100.900 90.686 130.677 130.961 170.537 320.348 120.769 130.903 110.785 110.815 70.676 230.939 150.880 120.772 9
PPT-SpUNet-Joint0.766 80.932 50.794 340.829 260.751 230.854 150.540 210.903 80.630 350.672 160.963 150.565 220.357 90.788 40.900 130.737 260.802 160.685 180.950 70.887 70.780 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. CVPR 2024
OctFormerpermissive0.766 80.925 70.808 240.849 100.786 50.846 270.566 90.876 160.690 100.674 150.960 180.576 180.226 680.753 250.904 100.777 130.815 70.722 50.923 280.877 140.776 8
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OccuSeg+Semantic0.764 100.758 590.796 320.839 190.746 260.907 10.562 110.850 260.680 160.672 160.978 50.610 30.335 180.777 70.819 460.847 10.830 10.691 150.972 20.885 90.727 23
CU-Hybrid Net0.764 100.924 80.819 120.840 180.757 180.853 170.580 30.848 270.709 30.643 250.958 220.587 130.295 340.753 250.884 210.758 200.815 70.725 30.927 250.867 230.743 16
O-CNNpermissive0.762 120.924 80.823 70.844 160.770 110.852 190.577 40.847 290.711 20.640 290.958 220.592 100.217 740.762 180.888 180.758 200.813 110.726 20.932 230.868 220.744 15
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DTC0.757 130.843 270.820 100.847 130.791 20.862 100.511 340.870 180.707 40.652 210.954 360.604 70.279 450.760 190.942 20.734 270.766 450.701 110.884 550.874 200.736 17
OA-CNN-L_ScanNet200.756 140.783 450.826 50.858 40.776 80.837 340.548 160.896 120.649 270.675 140.962 160.586 140.335 180.771 120.802 500.770 160.787 340.691 150.936 180.880 120.761 11
ConDaFormer0.755 150.927 60.822 80.836 210.801 10.849 220.516 310.864 230.651 260.680 120.958 220.584 160.282 420.759 210.855 320.728 290.802 160.678 200.880 600.873 210.756 13
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PNE0.755 150.786 430.835 40.834 230.758 160.849 220.570 80.836 330.648 280.668 180.978 50.581 170.367 70.683 360.856 300.804 70.801 200.678 200.961 50.889 60.716 30
P. Hermosilla: Point Neighborhood Embeddings.
DMF-Net0.752 170.906 130.793 360.802 420.689 410.825 470.556 120.867 190.681 150.602 450.960 180.555 280.365 80.779 60.859 270.747 230.795 280.717 60.917 320.856 310.764 10
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointTransformerV20.752 170.742 670.809 230.872 10.758 160.860 110.552 140.891 140.610 420.687 70.960 180.559 260.304 300.766 160.926 50.767 170.797 240.644 340.942 120.876 170.722 27
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
BPNetcopyleft0.749 190.909 110.818 140.811 340.752 210.839 330.485 480.842 300.673 180.644 240.957 260.528 380.305 290.773 100.859 270.788 90.818 60.693 140.916 330.856 310.723 26
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer0.749 190.793 410.790 370.807 380.750 250.856 130.524 270.881 150.588 540.642 280.977 90.591 110.274 480.781 50.929 40.804 70.796 250.642 350.947 90.885 90.715 31
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP0.748 210.623 950.804 260.859 30.745 270.824 490.501 380.912 50.690 100.685 90.956 270.567 210.320 240.768 150.918 60.720 340.802 160.676 230.921 300.881 110.779 7
StratifiedFormerpermissive0.747 220.901 140.803 270.845 150.757 180.846 270.512 330.825 370.696 80.645 230.956 270.576 180.262 590.744 300.861 260.742 240.770 430.705 90.899 450.860 280.734 18
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 230.771 530.819 120.848 120.702 380.865 90.397 860.899 100.699 60.664 190.948 560.588 120.330 200.746 290.851 360.764 180.796 250.704 100.935 190.866 240.728 21
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 230.870 190.838 20.858 40.729 320.850 210.501 380.874 170.587 550.658 200.956 270.564 230.299 320.765 170.900 130.716 370.812 120.631 400.939 150.858 290.709 32
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
DiffSeg3D20.745 250.725 760.814 180.837 200.751 230.831 410.514 320.896 120.674 170.684 100.960 180.564 230.303 310.773 100.820 450.713 400.798 230.690 170.923 280.875 180.757 12
Retro-FPN0.744 260.842 280.800 280.767 560.740 280.836 360.541 190.914 40.672 190.626 330.958 220.552 290.272 500.777 70.886 200.696 470.801 200.674 260.941 130.858 290.717 28
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net0.743 270.620 960.799 310.849 100.730 310.822 510.493 450.897 110.664 200.681 110.955 300.562 250.378 40.760 190.903 110.738 250.801 200.673 270.907 370.877 140.745 14
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 280.860 220.765 500.819 290.769 130.848 240.533 230.829 350.663 210.631 320.955 300.586 140.274 480.753 250.896 150.729 280.760 510.666 290.921 300.855 330.733 19
LRPNet0.742 280.816 360.806 250.807 380.752 210.828 450.575 60.839 320.699 60.637 300.954 360.520 410.320 240.755 240.834 400.760 190.772 400.676 230.915 350.862 260.717 28
LargeKernel3D0.739 300.909 110.820 100.806 400.740 280.852 190.545 170.826 360.594 530.643 250.955 300.541 310.263 580.723 340.858 290.775 150.767 440.678 200.933 210.848 380.694 37
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 310.776 490.790 370.851 80.754 200.854 150.491 470.866 210.596 520.686 80.955 300.536 330.342 140.624 510.869 230.787 100.802 160.628 410.927 250.875 180.704 34
MinkowskiNetpermissive0.736 310.859 230.818 140.832 250.709 360.840 310.521 290.853 250.660 230.643 250.951 460.544 300.286 400.731 320.893 160.675 560.772 400.683 190.874 670.852 360.727 23
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 330.890 150.837 30.864 20.726 330.873 50.530 260.824 380.489 880.647 220.978 50.609 40.336 160.624 510.733 590.758 200.776 380.570 660.949 80.877 140.728 21
PointTransformer++0.725 340.727 750.811 220.819 290.765 140.841 300.502 370.814 430.621 380.623 350.955 300.556 270.284 410.620 530.866 240.781 120.757 550.648 320.932 230.862 260.709 32
SparseConvNet0.725 340.647 910.821 90.846 140.721 340.869 60.533 230.754 590.603 480.614 370.955 300.572 200.325 220.710 350.870 220.724 320.823 20.628 410.934 200.865 250.683 40
MatchingNet0.724 360.812 380.812 200.810 350.735 300.834 380.495 440.860 240.572 620.602 450.954 360.512 430.280 440.757 220.845 380.725 310.780 360.606 510.937 170.851 370.700 36
INS-Conv-semantic0.717 370.751 620.759 530.812 330.704 370.868 70.537 220.842 300.609 440.608 410.953 400.534 350.293 350.616 540.864 250.719 360.793 290.640 360.933 210.845 420.663 46
PointMetaBase0.714 380.835 290.785 390.821 270.684 430.846 270.531 250.865 220.614 390.596 490.953 400.500 460.246 640.674 370.888 180.692 480.764 470.624 430.849 820.844 430.675 42
contrastBoundarypermissive0.705 390.769 560.775 440.809 360.687 420.820 540.439 740.812 440.661 220.591 510.945 640.515 420.171 920.633 480.856 300.720 340.796 250.668 280.889 520.847 390.689 38
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 400.774 510.800 280.793 470.760 150.847 260.471 520.802 470.463 950.634 310.968 130.491 490.271 520.726 330.910 80.706 420.815 70.551 780.878 610.833 440.570 78
RFCR0.702 410.889 160.745 640.813 320.672 460.818 580.493 450.815 420.623 360.610 390.947 580.470 580.249 630.594 570.848 370.705 430.779 370.646 330.892 500.823 500.611 61
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click0.701 420.825 330.796 320.723 630.716 350.832 400.433 760.816 400.634 330.609 400.969 110.418 840.344 130.559 690.833 410.715 380.808 140.560 720.902 420.847 390.680 41
JSENetpermissive0.699 430.881 180.762 510.821 270.667 470.800 700.522 280.792 500.613 400.607 420.935 840.492 480.205 790.576 620.853 340.691 500.758 530.652 310.872 700.828 470.649 50
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click0.693 440.743 660.794 340.655 860.684 430.822 510.497 430.719 690.622 370.617 360.977 90.447 710.339 150.750 280.664 750.703 450.790 320.596 560.946 110.855 330.647 51
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 450.732 710.772 450.786 480.677 450.866 80.517 300.848 270.509 810.626 330.952 440.536 330.225 700.545 750.704 660.689 530.810 130.564 710.903 410.854 350.729 20
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 460.884 170.754 570.795 450.647 540.818 580.422 780.802 470.612 410.604 430.945 640.462 610.189 870.563 680.853 340.726 300.765 460.632 390.904 390.821 530.606 65
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 470.704 810.741 680.754 600.656 490.829 430.501 380.741 640.609 440.548 590.950 500.522 400.371 50.633 480.756 540.715 380.771 420.623 440.861 780.814 560.658 47
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 480.866 200.748 610.819 290.645 560.794 730.450 640.802 470.587 550.604 430.945 640.464 600.201 820.554 710.840 390.723 330.732 650.602 540.907 370.822 520.603 68
VACNN++0.684 490.728 740.757 560.776 530.690 390.804 680.464 570.816 400.577 610.587 520.945 640.508 450.276 470.671 380.710 640.663 610.750 590.589 610.881 580.832 460.653 49
DGNet0.684 490.712 800.784 400.782 520.658 480.835 370.499 420.823 390.641 300.597 480.950 500.487 510.281 430.575 630.619 790.647 690.764 470.620 460.871 730.846 410.688 39
KP-FCNN0.684 490.847 260.758 550.784 500.647 540.814 610.473 510.772 530.605 460.594 500.935 840.450 690.181 900.587 580.805 490.690 510.785 350.614 470.882 570.819 540.632 57
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
Superpoint Network0.683 520.851 250.728 720.800 440.653 510.806 660.468 540.804 450.572 620.602 450.946 610.453 680.239 670.519 800.822 430.689 530.762 500.595 580.895 480.827 480.630 58
PointContrast_LA_SEM0.683 520.757 600.784 400.786 480.639 580.824 490.408 810.775 520.604 470.541 610.934 880.532 360.269 540.552 720.777 520.645 720.793 290.640 360.913 360.824 490.671 43
VI-PointConv0.676 540.770 550.754 570.783 510.621 620.814 610.552 140.758 570.571 640.557 570.954 360.529 370.268 560.530 780.682 700.675 560.719 680.603 530.888 530.833 440.665 45
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 550.789 420.748 610.763 580.635 600.814 610.407 830.747 610.581 590.573 540.950 500.484 520.271 520.607 550.754 550.649 660.774 390.596 560.883 560.823 500.606 65
SALANet0.670 560.816 360.770 480.768 550.652 520.807 650.451 610.747 610.659 250.545 600.924 940.473 570.149 1020.571 650.811 480.635 750.746 600.623 440.892 500.794 690.570 78
O3DSeg0.668 570.822 340.771 470.496 1060.651 530.833 390.541 190.761 560.555 700.611 380.966 140.489 500.370 60.388 1000.580 820.776 140.751 570.570 660.956 60.817 550.646 52
PointASNLpermissive0.666 580.703 820.781 420.751 620.655 500.830 420.471 520.769 540.474 910.537 630.951 460.475 560.279 450.635 460.698 690.675 560.751 570.553 770.816 890.806 600.703 35
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 580.781 460.759 530.699 710.644 570.822 510.475 500.779 510.564 670.504 770.953 400.428 780.203 810.586 600.754 550.661 620.753 560.588 620.902 420.813 580.642 53
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 600.746 640.708 750.722 640.638 590.820 540.451 610.566 970.599 500.541 610.950 500.510 440.313 260.648 430.819 460.616 800.682 830.590 600.869 740.810 590.656 48
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 610.778 470.702 780.806 400.619 630.813 640.468 540.693 770.494 840.524 690.941 760.449 700.298 330.510 820.821 440.675 560.727 670.568 690.826 870.803 630.637 55
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN0.658 610.558 1030.751 590.655 860.690 390.722 950.453 600.867 190.579 600.576 530.893 1060.523 390.293 350.733 310.571 840.692 480.659 900.606 510.875 640.804 620.668 44
HPGCNN0.656 630.698 840.743 660.650 880.564 800.820 540.505 360.758 570.631 340.479 810.945 640.480 540.226 680.572 640.774 530.690 510.735 630.614 470.853 810.776 840.597 71
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 640.752 610.734 700.664 840.583 750.815 600.399 850.754 590.639 310.535 650.942 740.470 580.309 280.665 390.539 860.650 650.708 730.635 380.857 800.793 710.642 53
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 650.778 470.731 710.699 710.577 760.829 430.446 660.736 650.477 900.523 710.945 640.454 650.269 540.484 900.749 580.618 780.738 610.599 550.827 860.792 740.621 60
PointConv-SFPN0.641 660.776 490.703 770.721 650.557 830.826 460.451 610.672 820.563 680.483 800.943 730.425 810.162 970.644 440.726 600.659 630.709 720.572 650.875 640.786 790.559 84
MVPNetpermissive0.641 660.831 300.715 730.671 810.590 710.781 790.394 870.679 790.642 290.553 580.937 810.462 610.256 600.649 420.406 1000.626 760.691 800.666 290.877 620.792 740.608 64
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 680.717 790.701 790.692 740.576 770.801 690.467 560.716 700.563 680.459 870.953 400.429 770.169 940.581 610.854 330.605 810.710 700.550 790.894 490.793 710.575 76
FPConvpermissive0.639 690.785 440.760 520.713 690.603 660.798 710.392 880.534 1020.603 480.524 690.948 560.457 630.250 620.538 760.723 620.598 850.696 780.614 470.872 700.799 640.567 81
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 700.797 400.769 490.641 940.590 710.820 540.461 580.537 1010.637 320.536 640.947 580.388 910.206 780.656 400.668 730.647 690.732 650.585 630.868 750.793 710.473 104
PointSPNet0.637 710.734 700.692 860.714 680.576 770.797 720.446 660.743 630.598 510.437 920.942 740.403 870.150 1010.626 500.800 510.649 660.697 770.557 750.846 830.777 830.563 82
SConv0.636 720.830 310.697 820.752 610.572 790.780 810.445 680.716 700.529 740.530 660.951 460.446 720.170 930.507 850.666 740.636 740.682 830.541 850.886 540.799 640.594 72
Supervoxel-CNN0.635 730.656 890.711 740.719 660.613 640.757 900.444 710.765 550.534 730.566 550.928 920.478 550.272 500.636 450.531 880.664 600.645 940.508 920.864 770.792 740.611 61
joint point-basedpermissive0.634 740.614 970.778 430.667 830.633 610.825 470.420 790.804 450.467 930.561 560.951 460.494 470.291 370.566 660.458 950.579 910.764 470.559 740.838 840.814 560.598 70
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 750.731 720.688 890.675 780.591 700.784 780.444 710.565 980.610 420.492 780.949 540.456 640.254 610.587 580.706 650.599 840.665 890.612 500.868 750.791 770.579 75
3DSM_DMMF0.631 760.626 940.745 640.801 430.607 650.751 910.506 350.729 680.565 660.491 790.866 1090.434 730.197 850.595 560.630 780.709 410.705 750.560 720.875 640.740 940.491 99
PointNet2-SFPN0.631 760.771 530.692 860.672 790.524 880.837 340.440 730.706 750.538 720.446 890.944 700.421 830.219 730.552 720.751 570.591 870.737 620.543 840.901 440.768 860.557 85
APCF-Net0.631 760.742 670.687 910.672 790.557 830.792 760.408 810.665 830.545 710.508 740.952 440.428 780.186 880.634 470.702 670.620 770.706 740.555 760.873 680.798 660.581 74
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 790.604 990.741 680.766 570.590 710.747 920.501 380.734 660.503 830.527 670.919 980.454 650.323 230.550 740.420 990.678 550.688 810.544 820.896 470.795 680.627 59
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 800.800 390.625 1020.719 660.545 850.806 660.445 680.597 910.448 980.519 720.938 800.481 530.328 210.489 890.499 930.657 640.759 520.592 590.881 580.797 670.634 56
SegGroup_sempermissive0.627 810.818 350.747 630.701 700.602 670.764 870.385 920.629 880.490 860.508 740.931 910.409 860.201 820.564 670.725 610.618 780.692 790.539 860.873 680.794 690.548 88
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 820.830 310.694 840.757 590.563 810.772 850.448 650.647 860.520 770.509 730.949 540.431 760.191 860.496 870.614 800.647 690.672 870.535 880.876 630.783 800.571 77
dtc_net0.625 820.703 820.751 590.794 460.535 860.848 240.480 490.676 810.528 750.469 840.944 700.454 650.004 1150.464 920.636 770.704 440.758 530.548 810.924 270.787 780.492 98
HPEIN0.618 840.729 730.668 920.647 900.597 690.766 860.414 800.680 780.520 770.525 680.946 610.432 740.215 750.493 880.599 810.638 730.617 990.570 660.897 460.806 600.605 67
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCNpermissive0.610 850.858 240.772 450.489 1070.532 870.792 760.404 840.643 870.570 650.507 760.935 840.414 850.046 1120.510 820.702 670.602 830.705 750.549 800.859 790.773 850.534 91
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 860.760 580.667 930.649 890.521 890.793 740.457 590.648 850.528 750.434 940.947 580.401 880.153 1000.454 930.721 630.648 680.717 690.536 870.904 390.765 870.485 100
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 870.634 930.743 660.697 730.601 680.781 790.437 750.585 940.493 850.446 890.933 890.394 890.011 1140.654 410.661 760.603 820.733 640.526 890.832 850.761 890.480 101
LAP-D0.594 880.720 770.692 860.637 950.456 990.773 840.391 900.730 670.587 550.445 910.940 780.381 920.288 380.434 960.453 970.591 870.649 920.581 640.777 930.749 930.610 63
DPC0.592 890.720 770.700 800.602 990.480 950.762 890.380 930.713 730.585 580.437 920.940 780.369 940.288 380.434 960.509 920.590 890.639 970.567 700.772 950.755 910.592 73
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 900.766 570.659 970.683 760.470 980.740 940.387 910.620 900.490 860.476 820.922 960.355 970.245 650.511 810.511 910.571 920.643 950.493 960.872 700.762 880.600 69
ROSMRF0.580 910.772 520.707 760.681 770.563 810.764 870.362 950.515 1030.465 940.465 860.936 830.427 800.207 770.438 940.577 830.536 950.675 860.486 970.723 1010.779 810.524 94
SD-DETR0.576 920.746 640.609 1060.445 1110.517 900.643 1060.366 940.714 720.456 960.468 850.870 1080.432 740.264 570.558 700.674 710.586 900.688 810.482 980.739 990.733 960.537 90
SQN_0.1%0.569 930.676 860.696 830.657 850.497 910.779 820.424 770.548 990.515 790.376 990.902 1050.422 820.357 90.379 1010.456 960.596 860.659 900.544 820.685 1040.665 1070.556 86
TextureNetpermissive0.566 940.672 880.664 940.671 810.494 930.719 960.445 680.678 800.411 1040.396 970.935 840.356 960.225 700.412 980.535 870.565 930.636 980.464 1000.794 920.680 1040.568 80
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet0.562 950.648 900.700 800.770 540.586 740.687 1000.333 990.650 840.514 800.475 830.906 1020.359 950.223 720.340 1030.442 980.422 1060.668 880.501 930.708 1020.779 810.534 91
Pointnet++ & Featurepermissive0.557 960.735 690.661 960.686 750.491 940.744 930.392 880.539 1000.451 970.375 1000.946 610.376 930.205 790.403 990.356 1030.553 940.643 950.497 940.824 880.756 900.515 95
GMLPs0.538 970.495 1080.693 850.647 900.471 970.793 740.300 1020.477 1040.505 820.358 1020.903 1040.327 1000.081 1090.472 910.529 890.448 1040.710 700.509 900.746 970.737 950.554 87
PanopticFusion-label0.529 980.491 1090.688 890.604 980.386 1040.632 1070.225 1120.705 760.434 1010.293 1080.815 1100.348 980.241 660.499 860.669 720.507 970.649 920.442 1060.796 910.602 1110.561 83
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 990.676 860.591 1090.609 960.442 1000.774 830.335 980.597 910.422 1030.357 1030.932 900.341 990.094 1080.298 1050.528 900.473 1020.676 850.495 950.602 1100.721 990.349 111
Online SegFusion0.515 1000.607 980.644 1000.579 1010.434 1010.630 1080.353 960.628 890.440 990.410 950.762 1140.307 1020.167 950.520 790.403 1010.516 960.565 1020.447 1040.678 1050.701 1010.514 96
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF0.501 1010.558 1030.608 1070.424 1130.478 960.690 990.246 1080.586 930.468 920.450 880.911 1000.394 890.160 980.438 940.212 1100.432 1050.541 1080.475 990.742 980.727 970.477 102
PCNN0.498 1020.559 1020.644 1000.560 1030.420 1030.711 980.229 1100.414 1050.436 1000.352 1040.941 760.324 1010.155 990.238 1100.387 1020.493 980.529 1090.509 900.813 900.751 920.504 97
Weakly-Openseg v30.489 1030.749 630.664 940.646 920.496 920.559 1120.122 1150.577 950.257 1150.364 1010.805 1110.198 1130.096 1070.510 820.496 940.361 1100.563 1030.359 1130.777 930.644 1080.532 93
3DMV0.484 1040.484 1100.538 1110.643 930.424 1020.606 1110.310 1000.574 960.433 1020.378 980.796 1120.301 1030.214 760.537 770.208 1110.472 1030.507 1120.413 1090.693 1030.602 1110.539 89
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1050.577 1010.611 1050.356 1150.321 1120.715 970.299 1040.376 1090.328 1110.319 1060.944 700.285 1050.164 960.216 1130.229 1080.484 1000.545 1070.456 1020.755 960.709 1000.475 103
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1060.679 850.604 1080.578 1020.380 1050.682 1010.291 1050.106 1150.483 890.258 1130.920 970.258 1090.025 1130.231 1120.325 1040.480 1010.560 1050.463 1010.725 1000.666 1060.231 115
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1070.474 1110.623 1030.463 1090.366 1070.651 1040.310 1000.389 1080.349 1090.330 1050.937 810.271 1070.126 1040.285 1060.224 1090.350 1120.577 1010.445 1050.625 1080.723 980.394 107
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET20.442 1080.548 1050.548 1100.597 1000.363 1080.628 1090.300 1020.292 1100.374 1060.307 1070.881 1070.268 1080.186 880.238 1100.204 1120.407 1070.506 1130.449 1030.667 1060.620 1100.462 105
SurfaceConvPF0.442 1080.505 1070.622 1040.380 1140.342 1100.654 1030.227 1110.397 1070.367 1070.276 1100.924 940.240 1100.198 840.359 1020.262 1060.366 1080.581 1000.435 1070.640 1070.668 1050.398 106
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1100.437 1130.646 990.474 1080.369 1060.645 1050.353 960.258 1120.282 1130.279 1090.918 990.298 1040.147 1030.283 1070.294 1050.487 990.562 1040.427 1080.619 1090.633 1090.352 110
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1110.525 1060.647 980.522 1040.324 1110.488 1150.077 1160.712 740.353 1080.401 960.636 1160.281 1060.176 910.340 1030.565 850.175 1160.551 1060.398 1100.370 1160.602 1110.361 109
SPLAT Netcopyleft0.393 1120.472 1120.511 1120.606 970.311 1130.656 1020.245 1090.405 1060.328 1110.197 1140.927 930.227 1120.000 1170.001 1170.249 1070.271 1150.510 1100.383 1120.593 1110.699 1020.267 113
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 1130.297 1150.491 1130.432 1120.358 1090.612 1100.274 1060.116 1140.411 1040.265 1110.904 1030.229 1110.079 1100.250 1080.185 1130.320 1130.510 1100.385 1110.548 1120.597 1140.394 107
PointNet++permissive0.339 1140.584 1000.478 1140.458 1100.256 1150.360 1160.250 1070.247 1130.278 1140.261 1120.677 1150.183 1140.117 1050.212 1140.145 1150.364 1090.346 1160.232 1160.548 1120.523 1150.252 114
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 1150.353 1140.290 1160.278 1160.166 1160.553 1130.169 1140.286 1110.147 1160.148 1160.908 1010.182 1150.064 1110.023 1160.018 1170.354 1110.363 1140.345 1140.546 1140.685 1030.278 112
ScanNetpermissive0.306 1160.203 1160.366 1150.501 1050.311 1130.524 1140.211 1130.002 1170.342 1100.189 1150.786 1130.145 1160.102 1060.245 1090.152 1140.318 1140.348 1150.300 1150.460 1150.437 1160.182 116
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 1170.000 1170.041 1170.172 1170.030 1170.062 1170.001 1170.035 1160.004 1170.051 1170.143 1170.019 1170.003 1160.041 1150.050 1160.003 1170.054 1170.018 1170.005 1170.264 1170.082 117


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PointRel0.816 11.000 10.971 60.908 60.743 20.923 50.573 60.714 220.695 160.734 80.747 20.725 90.809 11.000 10.814 70.899 30.820 31.000 10.610 16
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Spherical Mask(CtoF)0.812 21.000 10.973 50.852 130.718 50.917 70.574 40.677 280.748 100.729 120.715 60.795 20.809 11.000 10.831 30.854 90.787 101.000 10.638 5
EV3D0.811 31.000 10.968 70.852 130.717 60.921 60.574 50.677 280.748 100.730 110.703 110.795 20.809 11.000 10.831 30.854 90.778 141.000 10.638 6
SIM3D0.803 41.000 10.967 80.863 120.692 160.924 40.552 90.732 210.667 200.732 100.662 140.796 10.789 91.000 10.803 80.864 60.766 191.000 10.643 4
OneFormer3Dcopyleft0.801 51.000 10.973 40.909 50.698 130.928 20.582 30.668 330.685 170.780 20.687 120.698 170.702 141.000 10.794 100.900 20.784 120.986 500.635 7
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Competitor-SPFormer0.800 61.000 10.986 20.845 150.705 110.915 80.532 110.733 200.757 90.733 90.708 80.698 160.648 330.981 360.890 10.830 180.796 70.997 370.644 3
UniPerception0.800 61.000 10.930 100.872 100.727 40.862 220.454 170.764 130.820 10.746 60.706 90.750 50.772 100.926 430.764 160.818 260.826 10.997 370.660 2
InsSSM0.799 81.000 10.915 120.710 390.729 30.925 30.664 10.670 310.770 60.766 30.739 30.737 60.700 151.000 10.792 110.829 200.815 40.997 370.625 9
TST3D0.795 91.000 10.929 110.918 40.709 90.884 170.596 20.704 250.769 70.734 70.644 190.699 150.751 121.000 10.794 90.876 50.757 210.997 370.550 30
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former0.791 101.000 10.980 30.837 180.626 240.897 100.543 100.759 150.800 50.766 40.659 150.769 40.697 181.000 10.791 120.707 460.791 91.000 10.610 15
ExtMask3D0.789 111.000 10.988 10.756 320.706 100.912 90.429 180.647 380.806 40.755 50.673 130.689 180.772 111.000 10.789 130.852 110.811 51.000 10.617 12
Queryformer0.787 121.000 10.933 90.601 480.754 10.886 150.558 80.661 350.767 80.665 170.716 50.639 230.808 51.000 10.844 20.897 40.804 61.000 10.624 10
MAFT0.786 131.000 10.894 170.807 220.694 150.893 130.486 130.674 300.740 120.786 10.704 100.727 80.739 131.000 10.707 220.849 130.756 221.000 10.685 1
Mask3D0.780 141.000 10.786 410.716 370.696 140.885 160.500 120.714 220.810 30.672 160.715 60.679 190.809 11.000 10.831 30.833 170.787 101.000 10.602 18
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 150.903 540.903 140.806 230.609 300.886 140.568 70.815 60.705 150.711 130.655 160.652 220.685 211.000 10.789 140.809 270.776 161.000 10.583 23
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 161.000 10.803 340.937 10.684 170.865 190.213 330.870 20.664 210.571 230.758 10.702 130.807 61.000 10.653 290.902 10.792 81.000 10.626 8
SoftGrouppermissive0.761 171.000 10.808 300.845 150.716 70.862 210.243 300.824 40.655 230.620 180.734 40.699 140.791 80.981 360.716 200.844 140.769 171.000 10.594 21
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNetpermissive0.757 181.000 10.904 130.731 350.678 180.895 110.458 150.644 400.670 190.710 140.620 240.732 70.650 231.000 10.756 170.778 300.779 131.000 10.614 13
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
TD3Dpermissive0.751 191.000 10.774 420.867 110.621 260.934 10.404 190.706 240.812 20.605 210.633 220.626 240.690 201.000 10.640 310.820 230.777 151.000 10.612 14
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNetpermissive0.747 201.000 10.818 260.837 190.713 80.844 240.457 160.647 380.711 140.614 190.617 260.657 210.650 231.000 10.692 230.822 220.765 201.000 10.595 20
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut0.732 211.000 10.788 390.724 360.642 230.859 230.248 290.787 110.618 260.596 220.653 180.722 110.583 451.000 10.766 150.861 70.825 21.000 10.504 36
IPCA-Inst0.731 221.000 10.788 400.884 90.698 120.788 400.252 280.760 140.646 240.511 310.637 210.665 200.804 71.000 10.644 300.778 310.747 241.000 10.561 27
TopoSeg0.725 231.000 10.806 330.933 20.668 200.758 440.272 270.734 190.630 250.549 270.654 170.606 250.697 190.966 400.612 350.839 150.754 231.000 10.573 24
DKNet0.718 241.000 10.814 270.782 260.619 270.872 180.224 310.751 170.569 300.677 150.585 300.724 100.633 350.981 360.515 450.819 240.736 251.000 10.617 11
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 251.000 10.850 190.924 30.648 210.747 470.162 350.862 30.572 290.520 290.624 230.549 280.649 321.000 10.560 400.706 470.768 181.000 10.591 22
HAISpermissive0.699 261.000 10.849 200.820 200.675 190.808 340.279 250.757 160.465 360.517 300.596 280.559 270.600 391.000 10.654 280.767 330.676 290.994 460.560 28
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 271.000 10.697 580.888 80.556 370.803 350.387 200.626 420.417 410.556 260.585 310.702 120.600 391.000 10.824 60.720 450.692 271.000 10.509 35
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 281.000 10.799 360.811 210.622 250.817 290.376 210.805 90.590 280.487 350.568 340.525 320.650 230.835 530.600 360.829 190.655 321.000 10.526 32
SphereSeg0.680 291.000 10.856 180.744 330.618 280.893 120.151 360.651 370.713 130.537 280.579 330.430 420.651 221.000 10.389 560.744 400.697 260.991 480.601 19
DANCENET0.680 291.000 10.807 310.733 340.600 310.768 430.375 220.543 500.538 310.610 200.599 270.498 330.632 370.981 360.739 190.856 80.633 380.882 610.454 45
Box2Mask0.677 311.000 10.847 210.771 280.509 460.816 300.277 260.558 490.482 330.562 250.640 200.448 380.700 151.000 10.666 240.852 120.578 450.997 370.488 40
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 321.000 10.758 500.682 410.576 350.842 250.477 140.504 560.524 320.567 240.585 320.451 370.557 471.000 10.751 180.797 280.563 481.000 10.467 44
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 331.000 10.822 250.764 310.616 290.815 310.139 400.694 270.597 270.459 390.566 350.599 260.600 390.516 630.715 210.819 250.635 361.000 10.603 17
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 341.000 10.760 480.667 430.581 330.863 200.323 230.655 360.477 340.473 370.549 370.432 410.650 231.000 10.655 270.738 410.585 440.944 530.472 43
CSC-Pretrained0.648 351.000 10.810 280.768 290.523 440.813 320.143 390.819 50.389 440.422 480.511 410.443 390.650 231.000 10.624 330.732 420.634 371.000 10.375 52
PE0.645 361.000 10.773 440.798 250.538 390.786 410.088 480.799 100.350 480.435 460.547 380.545 290.646 340.933 420.562 390.761 360.556 530.997 370.501 38
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 371.000 10.758 490.582 540.539 380.826 280.046 530.765 120.372 460.436 450.588 290.539 310.650 231.000 10.577 370.750 380.653 340.997 370.495 39
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 381.000 10.841 220.893 70.531 410.802 360.115 450.588 470.448 380.438 430.537 400.430 430.550 480.857 450.534 430.764 350.657 310.987 490.568 25
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 391.000 10.895 160.800 240.480 500.676 520.144 380.737 180.354 470.447 400.400 540.365 490.700 151.000 10.569 380.836 160.599 401.000 10.473 42
PointGroup0.636 401.000 10.765 450.624 450.505 480.797 370.116 440.696 260.384 450.441 410.559 360.476 350.596 421.000 10.666 240.756 370.556 520.997 370.513 34
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group0.635 410.667 560.797 380.714 380.562 360.774 420.146 370.810 80.429 400.476 360.546 390.399 450.633 351.000 10.632 320.722 440.609 391.000 10.514 33
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
Mask3D_evaluation0.631 421.000 10.829 240.606 470.646 220.836 260.068 490.511 540.462 370.507 320.619 250.389 470.610 381.000 10.432 510.828 210.673 300.788 650.552 29
DENet0.629 431.000 10.797 370.608 460.589 320.627 560.219 320.882 10.310 500.402 530.383 560.396 460.650 231.000 10.663 260.543 640.691 281.000 10.568 26
3D-MPA0.611 441.000 10.833 230.765 300.526 430.756 450.136 420.588 470.470 350.438 440.432 500.358 510.650 230.857 450.429 520.765 340.557 511.000 10.430 47
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 451.000 10.801 350.599 490.535 400.728 490.286 240.436 600.679 180.491 330.433 480.256 530.404 600.857 450.620 340.724 430.510 581.000 10.539 31
AOIA0.601 461.000 10.761 470.687 400.485 490.828 270.008 600.663 340.405 430.405 520.425 510.490 340.596 420.714 560.553 420.779 290.597 410.992 470.424 49
PCJC0.578 471.000 10.810 290.583 530.449 530.813 330.042 540.603 450.341 490.490 340.465 450.410 440.650 230.835 530.264 620.694 510.561 490.889 580.504 37
SSEN0.575 481.000 10.761 460.473 560.477 510.795 380.066 500.529 520.658 220.460 380.461 460.380 480.331 620.859 440.401 550.692 530.653 33