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 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 by
Minkowski 34Dpermissive0.253 100.463 100.154 110.102 100.381 110.084 40.134 90.000 10.000 30.000 10.386 40.141 110.279 110.737 100.703 100.014 110.164 90.000 30.663 50.092 100.000 60.224 100.291 50.531 50.056 110.000 50.242 100.000 10.000 30.013 90.331 100.000 60.000 10.035 110.001 10.858 90.059 100.650 100.000 40.056 90.353 100.299 90.670 100.000 10.000 20.284 100.484 90.071 50.594 100.720 100.710 90.027 80.068 110.813 80.000 10.005 60.492 90.164 10.274 100.111 100.571 100.307 110.293 70.307 110.150 50.163 110.531 100.002 30.545 30.932 90.093 110.000 10.000 70.002 80.159 90.368 110.581 90.440 110.228 110.406 50.282 110.294 100.000 30.000 10.189 100.060 10.036 30.000 60.000 30.897 70.000 110.525 80.025 110.205 110.771 110.000 60.000 10.593 80.108 70.044 40.000 40.000 10.000 50.282 110.589 90.094 100.169 100.466 110.227 100.419 110.125 30.757 80.002 10.334 100.000 10.000 40.000 20.357 90.000 10.000 50.000 10.582 60.513 110.337 70.612 110.000 60.000 10.250 100.352 110.136 110.724 100.655 30.280 50.000 70.046 100.000 10.606 110.559 90.159 40.102 10.445 40.655 40.000 10.310 110.117 30.000 50.000 10.581 110.026 10.000 10.265 110.483 100.084 110.097 110.044 90.865 110.142 110.588 60.351 90.272 20.596 110.000 10.003 60.622 100.720 70.096 110.000 10.771 100.016 100.772 90.000 10.302 90.194 40.214 80.621 100.197 11
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
PonderV2 ScanNet2000.346 20.552 40.270 40.175 30.497 50.070 80.239 40.000 10.000 30.000 10.232 110.412 50.584 10.842 30.804 30.212 50.540 40.000 30.433 110.106 60.000 60.590 30.290 60.548 20.243 40.000 50.356 70.000 10.000 30.062 70.398 70.441 50.000 10.104 60.000 20.888 20.076 80.682 40.030 10.094 40.491 60.351 70.869 70.000 10.063 10.403 60.700 20.000 70.660 90.881 30.761 10.050 60.186 50.852 70.000 10.007 50.570 50.100 20.565 20.326 30.641 60.431 30.290 80.621 30.259 20.408 50.622 60.125 10.082 70.950 20.179 30.000 10.263 20.424 20.193 50.558 30.880 10.545 70.375 40.727 20.445 60.499 60.000 30.000 10.475 40.002 40.034 40.083 40.000 30.924 10.290 30.636 30.115 80.400 30.874 30.186 40.000 10.611 50.128 20.113 20.000 40.000 10.000 50.584 60.636 50.103 80.385 50.843 40.283 20.603 30.080 50.825 40.000 30.377 60.000 10.000 40.000 20.457 70.000 10.000 50.000 10.574 80.608 60.481 20.792 30.394 20.000 10.357 60.503 70.261 60.817 70.504 80.304 40.472 30.115 50.000 10.750 30.677 30.202 10.000 70.509 30.729 10.000 10.519 80.000 100.000 50.000 10.620 80.000 20.000 10.660 30.560 40.486 20.384 60.346 40.952 20.247 80.667 20.436 60.269 30.691 30.000 10.010 30.787 50.889 20.880 40.000 10.810 40.336 30.860 60.000 10.606 40.009 50.248 50.681 40.392 6
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.
CeCo0.340 30.551 50.247 70.181 20.475 70.057 110.142 80.000 10.000 30.000 10.387 30.463 30.499 60.924 20.774 60.213 40.257 70.000 30.546 100.100 70.006 50.615 10.177 110.534 40.246 30.000 50.400 20.000 10.338 10.006 100.484 30.609 20.000 10.083 70.000 20.873 60.089 40.661 80.000 40.048 100.560 10.408 40.892 50.000 10.000 20.586 10.616 50.000 70.692 60.900 20.721 60.162 10.228 30.860 50.000 10.000 70.575 20.083 30.550 30.347 20.624 70.410 70.360 30.740 20.109 80.321 90.660 40.000 40.121 40.939 70.143 60.000 10.400 10.003 70.190 60.564 20.652 60.615 50.421 20.304 90.579 10.547 30.000 30.000 10.296 80.000 60.030 50.096 30.000 30.916 30.037 70.551 60.171 40.376 40.865 50.286 20.000 10.633 20.102 90.027 50.011 30.000 10.000 50.474 80.742 20.133 40.311 70.824 60.242 70.503 80.068 60.828 30.000 30.429 30.000 10.063 30.000 20.781 10.000 10.000 50.000 10.665 10.633 40.450 30.818 20.000 60.000 10.429 20.532 40.226 70.825 50.510 70.377 30.709 10.079 80.000 10.753 20.683 20.102 100.063 30.401 100.620 80.000 10.619 20.000 100.000 50.000 10.595 90.000 20.000 10.345 80.564 30.411 40.603 10.384 30.945 40.266 60.643 30.367 80.304 10.663 60.000 10.010 30.726 90.767 60.898 30.000 10.784 70.435 10.861 50.000 10.447 60.000 90.257 40.656 70.377 7
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 40.533 70.279 20.155 40.508 30.073 70.101 110.000 10.058 20.000 10.294 100.233 100.548 20.927 10.788 50.264 10.463 50.000 30.638 70.098 90.014 40.411 70.226 70.525 70.225 60.010 30.397 30.000 10.000 30.192 30.380 80.598 30.000 10.117 20.000 20.883 30.082 60.689 20.000 40.032 110.549 30.417 30.910 30.000 10.000 20.448 50.613 60.000 70.697 50.960 10.759 20.158 20.293 10.883 30.000 10.312 10.583 10.079 40.422 80.068 110.660 40.418 40.298 60.430 80.114 70.526 30.776 10.051 20.679 10.946 30.152 50.000 10.183 30.000 90.211 40.511 60.409 100.565 60.355 50.448 40.512 40.557 20.000 30.000 10.420 50.000 60.007 110.104 20.000 30.125 110.330 20.514 90.146 70.321 70.860 60.174 50.000 10.629 30.075 100.000 90.000 40.000 10.002 40.671 40.712 30.141 30.339 60.856 30.261 60.529 60.067 70.835 10.000 30.369 80.000 10.259 20.000 20.629 30.000 10.487 10.000 10.579 70.646 20.107 110.720 80.122 40.000 10.333 80.505 60.303 50.908 10.503 90.565 10.074 60.324 10.000 10.740 40.661 50.109 80.000 70.427 70.563 110.000 10.579 70.108 50.000 50.000 10.664 30.000 20.000 10.641 40.539 60.416 30.515 20.256 50.940 70.312 30.209 110.620 10.138 90.636 70.000 10.000 80.775 80.861 40.765 70.000 10.801 60.119 90.860 60.000 10.687 10.001 80.192 100.679 60.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
LGroundpermissive0.272 90.485 90.184 90.106 90.476 60.077 60.218 50.000 10.000 30.000 10.547 10.295 80.540 30.746 80.745 90.058 100.112 100.005 10.658 60.077 110.000 60.322 90.178 100.512 80.190 70.199 10.277 90.000 10.000 30.173 40.399 60.000 60.000 10.039 100.000 20.858 90.085 50.676 60.002 20.103 30.498 50.323 80.703 90.000 10.000 20.296 90.549 70.216 10.702 40.768 80.718 80.028 70.092 100.786 100.000 10.000 70.453 100.022 50.251 110.252 60.572 90.348 90.321 50.514 40.063 90.279 100.552 90.000 40.019 100.932 90.132 100.000 10.000 70.000 90.156 110.457 90.623 70.518 80.265 100.358 70.381 90.395 90.000 30.000 10.127 110.012 30.051 10.000 60.000 30.886 90.014 80.437 110.179 30.244 90.826 90.000 60.000 10.599 70.136 10.085 30.000 40.000 10.000 50.565 70.612 80.143 20.207 90.566 90.232 90.446 90.127 20.708 90.000 30.384 50.000 10.000 40.000 20.402 80.000 10.059 30.000 10.525 110.566 80.229 80.659 90.000 60.000 10.265 90.446 80.147 100.720 110.597 50.066 80.000 70.187 30.000 10.726 70.467 110.134 70.000 70.413 90.629 70.000 10.363 100.055 70.022 20.000 10.626 70.000 20.000 10.323 90.479 110.154 100.117 90.028 100.901 90.243 90.415 100.295 110.143 60.610 100.000 10.000 80.777 70.397 110.324 100.000 10.778 90.179 70.702 100.000 10.274 110.404 10.233 60.622 90.398 5
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
AWCS0.305 80.508 80.225 80.142 50.463 80.063 90.195 60.000 10.000 30.000 10.467 20.551 10.504 50.773 50.764 80.142 80.029 110.000 30.626 80.100 70.000 60.360 80.179 90.507 90.137 90.006 40.300 80.000 10.000 30.172 50.364 90.512 40.000 10.056 80.000 20.865 80.093 30.634 110.000 40.071 80.396 90.296 100.876 60.000 10.000 20.373 80.436 100.063 60.749 10.877 40.721 60.131 30.124 80.804 90.000 10.000 70.515 70.010 60.452 70.252 60.578 80.417 50.179 110.484 60.171 40.337 80.606 80.000 40.115 50.937 80.142 70.000 10.008 60.000 90.157 100.484 80.402 110.501 90.339 60.553 30.529 20.478 80.000 30.000 10.404 60.001 50.022 70.077 50.000 30.894 80.219 40.628 40.093 90.305 80.886 10.233 30.000 10.603 60.112 40.023 60.000 40.000 10.000 50.741 20.664 40.097 90.253 80.782 80.264 50.523 70.154 10.707 100.000 30.411 40.000 10.000 40.000 20.332 100.000 10.000 50.000 10.602 30.595 70.185 90.656 100.159 30.000 10.355 70.424 90.154 90.729 90.516 60.220 60.620 20.084 70.000 10.707 80.651 70.173 20.014 60.381 110.582 90.000 10.619 20.049 80.000 50.000 10.702 20.000 20.000 10.302 100.489 90.317 70.334 70.392 20.922 80.254 70.533 80.394 70.129 110.613 90.000 10.000 80.820 20.649 80.749 80.000 10.782 80.282 50.863 40.000 10.288 100.006 60.220 70.633 80.542 2
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.520 10.109 20.108 100.000 10.337 10.000 10.310 90.394 60.494 80.753 70.848 10.256 20.717 20.000 30.842 10.192 20.065 20.449 50.346 10.546 30.190 70.000 50.384 40.000 10.000 30.218 10.505 10.791 10.000 10.136 10.000 20.903 10.073 90.687 30.000 40.168 10.551 20.387 50.941 10.000 10.000 20.397 70.654 30.000 70.714 30.759 90.752 40.118 40.264 20.926 10.000 10.048 20.575 20.000 70.597 10.366 10.755 10.469 10.474 10.798 10.140 60.617 10.692 30.000 40.592 20.971 10.188 20.000 10.133 40.593 10.349 10.650 10.717 40.699 10.455 10.790 10.523 30.636 10.301 10.000 10.622 20.000 60.017 90.259 10.000 30.921 20.337 10.733 10.210 10.514 10.860 60.407 10.000 10.688 10.109 60.000 90.000 40.000 10.151 10.671 40.782 10.115 70.641 10.903 10.349 10.616 10.088 40.832 20.000 30.480 10.000 10.428 10.000 20.497 60.000 10.000 50.000 10.662 20.690 10.612 10.828 10.575 10.000 10.404 40.644 10.325 30.887 20.728 10.009 100.134 50.026 110.000 10.761 10.731 10.172 30.077 20.528 20.727 20.000 10.603 40.220 20.022 20.000 10.740 10.000 20.000 10.661 10.586 10.566 10.436 40.531 10.978 10.457 10.708 10.583 30.141 70.748 10.000 10.026 10.822 10.871 30.879 50.000 10.851 10.405 20.914 10.000 10.682 20.000 90.281 10.738 10.463 4
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.
OA-CNN-L_ScanNet2000.333 50.558 20.269 50.124 70.448 90.080 50.272 30.000 10.000 30.000 10.342 50.515 20.524 40.713 110.789 40.158 70.384 60.000 30.806 30.125 30.000 60.496 40.332 30.498 100.227 50.024 20.474 10.000 10.003 20.071 60.487 20.000 60.000 10.110 40.000 20.876 40.013 110.703 10.000 40.076 60.473 70.355 60.906 40.000 10.000 20.476 40.706 10.000 70.672 80.835 70.748 50.015 100.223 40.860 50.000 10.000 70.572 40.000 70.509 50.313 40.662 20.398 80.396 20.411 90.276 10.527 20.711 20.000 40.076 80.946 30.166 40.000 10.022 50.160 30.183 70.493 70.699 50.637 30.403 30.330 80.406 70.526 40.024 20.000 10.392 70.000 60.016 100.000 60.196 20.915 40.112 60.557 50.197 20.352 60.877 20.000 60.000 10.592 90.103 80.000 90.067 10.000 10.089 20.735 30.625 60.130 60.568 30.836 50.271 30.534 50.043 90.799 50.001 20.445 20.000 10.000 40.024 10.661 20.000 10.262 20.000 10.591 40.517 100.373 50.788 50.021 50.000 10.455 10.517 50.320 40.823 60.200 110.001 110.150 40.100 60.000 10.736 50.668 40.103 90.052 40.662 10.720 30.000 10.602 50.112 40.002 40.000 10.637 60.000 20.000 10.621 60.569 20.398 50.412 50.234 60.949 30.363 20.492 90.495 50.251 40.665 50.000 10.001 70.805 30.833 50.794 60.000 10.821 20.314 40.843 80.000 10.560 50.245 20.262 30.713 20.370 8
PPT-SpUNet-F.T.0.332 60.556 30.270 30.123 80.519 20.091 30.349 20.000 10.000 30.000 10.339 60.383 70.498 70.833 40.807 20.241 30.584 30.000 30.755 40.124 40.000 60.608 20.330 40.530 60.314 10.000 50.374 50.000 10.000 30.197 20.459 40.000 60.000 10.117 20.000 20.876 40.095 10.682 40.000 40.086 50.518 40.433 10.930 20.000 10.000 20.563 30.542 80.077 40.715 20.858 50.756 30.008 110.171 70.874 40.000 10.039 30.550 60.000 70.545 40.256 50.657 50.453 20.351 40.449 70.213 30.392 60.611 70.000 40.037 90.946 30.138 80.000 10.000 70.063 50.308 20.537 40.796 20.673 20.323 80.392 60.400 80.509 50.000 30.000 10.649 10.000 60.023 60.000 60.000 30.914 50.002 100.506 100.163 60.359 50.872 40.000 60.000 10.623 40.112 40.001 80.000 40.000 10.021 30.753 10.565 100.150 10.579 20.806 70.267 40.616 10.042 100.783 70.000 30.374 70.000 10.000 40.000 20.620 50.000 10.000 50.000 10.572 90.634 30.350 60.792 30.000 60.000 10.376 50.535 30.378 20.855 30.672 20.074 70.000 70.185 40.000 10.727 60.660 60.076 110.000 70.432 60.646 50.000 10.594 60.006 90.000 50.000 10.658 40.000 20.000 10.661 10.549 50.300 80.291 80.045 80.942 60.304 40.600 50.572 40.135 100.695 20.000 10.008 50.793 40.942 10.899 20.000 10.816 30.181 60.897 20.000 10.679 30.223 30.264 20.691 30.345 9
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.
OctFormer ScanNet200permissive0.326 70.539 60.265 60.131 60.499 40.110 10.522 10.000 10.000 30.000 10.318 80.427 40.455 90.743 90.765 70.175 60.842 10.000 30.828 20.204 10.033 30.429 60.335 20.601 10.312 20.000 50.357 60.000 10.000 30.047 80.423 50.000 60.000 10.105 50.000 20.873 60.079 70.670 70.000 40.117 20.471 80.432 20.829 80.000 10.000 20.584 20.417 110.089 30.684 70.837 60.705 100.021 90.178 60.892 20.000 10.028 40.505 80.000 70.457 60.200 80.662 20.412 60.244 90.496 50.000 110.451 40.626 50.000 40.102 60.943 60.138 80.000 10.000 70.149 40.291 30.534 50.722 30.632 40.331 70.253 100.453 50.487 70.000 30.000 10.479 30.000 60.022 70.000 60.000 30.900 60.128 50.684 20.164 50.413 20.854 80.000 60.000 10.512 110.074 110.003 70.000 40.000 10.000 50.469 90.613 70.132 50.529 40.871 20.227 100.582 40.026 110.787 60.000 30.339 90.000 10.000 40.000 20.626 40.000 10.029 40.000 10.587 50.612 50.411 40.724 70.000 60.000 10.407 30.552 20.513 10.849 40.655 30.408 20.000 70.296 20.000 10.686 90.645 80.145 50.022 50.414 80.633 60.000 10.637 10.224 10.000 50.000 10.650 50.000 20.000 10.622 50.535 70.343 60.483 30.230 70.943 50.289 50.618 40.596 20.140 80.679 40.000 10.022 20.783 60.620 90.906 10.000 10.806 50.137 80.865 30.000 10.378 70.000 90.168 110.680 50.227 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CSC-Pretrainpermissive0.249 110.455 110.171 100.079 110.418 100.059 100.186 70.000 10.000 30.000 10.335 70.250 90.316 100.766 60.697 110.142 80.170 80.003 20.553 90.112 50.097 10.201 110.186 80.476 110.081 100.000 50.216 110.000 10.000 30.001 110.314 110.000 60.000 10.055 90.000 20.832 110.094 20.659 90.002 20.076 60.310 110.293 110.664 110.000 10.000 20.175 110.634 40.130 20.552 110.686 110.700 110.076 50.110 90.770 110.000 10.000 70.430 110.000 70.319 90.166 90.542 110.327 100.205 100.332 100.052 100.375 70.444 110.000 40.012 110.930 110.203 10.000 10.000 70.046 60.175 80.413 100.592 80.471 100.299 90.152 110.340 100.247 110.000 30.000 10.225 90.058 20.037 20.000 60.207 10.862 100.014 80.548 70.033 100.233 100.816 100.000 60.000 10.542 100.123 30.121 10.019 20.000 10.000 50.463 100.454 110.045 110.128 110.557 100.235 80.441 100.063 80.484 110.000 30.308 110.000 10.000 40.000 20.318 110.000 10.000 50.000 10.545 100.543 90.164 100.734 60.000 60.000 10.215 110.371 100.198 80.743 80.205 100.062 90.000 70.079 80.000 10.683 100.547 100.142 60.000 70.441 50.579 100.000 10.464 90.098 60.041 10.000 10.590 100.000 20.000 10.373 70.494 80.174 90.105 100.001 110.895 100.222 100.537 70.307 100.180 50.625 80.000 10.000 80.591 110.609 100.398 90.000 10.766 110.014 110.638 110.000 10.377 80.004 70.206 90.609 110.465 3
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 Infoavg ap 25%head ap 25%common ap 25%tail ap 25%alarm 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
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TD3D Scannet200permissive0.379 20.603 20.306 20.190 20.635 20.073 20.500 10.000 10.000 10.000 10.495 30.735 20.275 51.000 10.979 20.590 20.000 40.021 20.000 30.146 30.000 20.356 20.173 50.795 10.226 20.000 10.173 20.000 10.000 20.226 20.390 20.000 20.000 10.250 10.000 10.706 20.061 30.885 10.093 20.186 20.259 40.200 10.667 10.000 20.000 10.667 20.825 10.250 40.834 41.000 10.958 10.553 10.111 30.748 10.220 20.051 20.866 20.792 10.390 50.045 50.800 20.302 50.517 10.533 30.113 20.427 10.843 20.000 20.458 10.600 10.000 10.101 20.000 10.259 10.717 20.500 20.615 20.520 20.526 20.457 10.270 40.000 10.000 10.400 20.088 20.294 20.181 20.000 11.000 10.400 10.710 50.103 30.477 50.905 20.061 20.000 10.906 20.102 20.232 10.125 20.000 20.003 20.792 31.000 10.000 20.102 30.125 40.559 50.523 30.075 20.715 10.000 20.424 50.000 10.396 20.250 10.638 10.000 10.000 20.000 10.622 50.833 20.221 10.970 10.250 20.038 10.260 20.415 10.125 21.000 11.000 10.857 20.000 20.908 10.012 10.869 30.836 10.635 10.111 10.625 11.000 10.020 20.510 10.003 30.009 21.000 10.778 10.000 10.000 10.370 30.755 10.288 20.333 30.274 21.000 10.557 10.731 20.456 20.433 30.769 50.000 10.000 20.621 41.000 10.458 40.000 10.196 20.817 10.000 10.472 10.222 30.205 50.689 20.274 3
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Mask3D Scannet2000.445 10.653 10.392 10.254 10.648 10.097 10.125 50.000 10.000 10.000 10.657 10.971 10.451 21.000 11.000 10.640 10.500 10.045 11.000 10.241 20.409 10.363 10.440 10.686 30.300 10.000 10.201 10.000 10.009 10.290 10.556 11.000 10.000 10.063 30.000 10.830 10.573 10.844 20.333 10.204 10.058 50.158 50.552 20.056 10.000 11.000 10.725 40.750 10.927 11.000 10.888 40.042 30.120 20.615 40.226 10.250 10.890 10.792 10.677 20.510 20.818 10.699 10.512 20.167 50.125 10.315 20.943 10.309 10.017 30.200 30.000 10.188 10.000 10.183 30.815 11.000 10.827 10.741 10.442 30.414 40.600 10.000 10.000 10.458 10.049 30.321 10.381 10.000 10.908 20.400 10.841 10.260 10.710 10.966 10.265 10.000 10.924 10.152 10.025 20.500 10.027 10.028 11.000 10.556 50.016 10.080 50.500 10.694 30.608 10.084 10.604 30.194 10.538 30.000 10.500 10.000 20.354 40.000 11.000 10.000 10.761 20.930 10.053 40.890 31.000 10.008 20.262 10.358 21.000 11.000 10.792 40.966 11.000 10.765 20.004 20.930 10.780 20.330 20.027 20.625 10.974 40.050 10.412 50.021 20.000 30.000 20.778 10.000 10.000 10.493 20.746 20.454 10.335 20.396 10.930 50.551 21.000 10.552 10.606 10.853 10.000 10.004 10.806 11.000 10.727 20.000 10.042 30.745 20.000 10.399 40.391 10.630 10.721 10.619 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.permissive0.280 40.488 40.192 50.124 40.593 40.010 40.500 10.000 10.000 10.000 10.447 40.535 40.445 31.000 10.861 40.400 30.225 20.000 30.000 30.142 40.000 20.074 40.342 30.467 50.067 30.000 10.119 50.000 10.000 20.000 40.337 50.000 20.000 10.000 40.000 10.506 50.070 20.804 40.000 30.000 40.333 30.172 30.150 50.000 20.000 10.479 50.745 30.000 50.830 51.000 10.904 30.167 20.090 40.732 20.000 30.000 30.443 40.000 30.500 30.542 10.772 50.396 40.077 50.385 40.044 40.118 50.777 40.000 20.000 40.200 30.000 10.000 30.000 10.148 40.502 40.500 20.419 40.159 50.281 40.404 50.317 30.000 10.000 10.200 30.000 40.077 30.000 30.000 10.750 30.200 30.715 40.021 40.551 20.828 50.000 30.000 10.743 40.059 50.000 30.000 30.000 20.000 30.125 50.648 30.000 20.191 20.500 10.669 40.502 40.000 50.568 40.000 20.516 40.000 10.000 30.000 20.305 50.000 10.000 20.000 10.825 10.833 20.021 50.918 20.000 30.000 30.191 40.346 40.100 40.981 31.000 10.286 40.000 20.000 50.000 30.868 40.648 50.292 30.000 30.375 31.000 10.000 30.500 20.000 40.333 10.000 20.538 50.000 10.000 10.213 50.518 40.098 40.528 10.250 30.997 30.284 50.677 30.398 30.167 40.790 40.000 10.000 20.618 50.903 50.200 50.000 10.333 10.333 40.000 10.442 30.083 40.213 40.587 40.131 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.275 50.466 50.218 40.110 50.625 30.007 50.500 10.000 10.000 10.000 10.000 50.222 50.377 41.000 10.661 50.400 30.000 40.000 30.000 30.119 50.000 20.000 50.277 40.685 40.067 30.000 10.132 30.000 10.000 20.000 40.367 40.000 20.000 10.000 40.000 10.591 30.055 40.783 50.000 30.014 30.500 20.161 40.278 30.000 20.000 10.667 20.768 20.500 20.866 21.000 10.829 50.000 40.019 50.555 50.000 30.000 30.305 50.000 30.750 10.200 40.783 40.429 30.395 30.677 20.020 50.286 30.584 50.000 20.000 40.115 50.000 10.000 30.000 10.145 50.423 50.500 20.364 50.369 40.571 10.448 30.206 50.000 10.000 10.200 30.106 10.065 50.000 30.000 10.750 30.200 30.774 20.000 50.501 30.841 40.000 30.000 10.692 50.063 40.000 30.000 30.000 20.000 30.500 40.649 20.000 20.084 40.125 40.719 10.413 50.004 40.450 50.000 20.638 10.000 10.000 30.000 20.505 30.000 10.000 20.000 10.727 30.833 20.221 20.779 50.000 30.000 30.168 50.311 50.125 20.571 40.500 50.143 50.000 20.250 40.000 30.869 20.667 40.162 50.000 30.250 41.000 10.000 30.500 20.000 40.000 30.000 20.689 40.000 10.000 10.312 40.383 50.114 30.333 30.000 40.997 30.420 30.613 40.212 50.500 20.819 20.000 10.000 20.768 21.000 10.918 10.000 10.000 40.278 50.000 10.333 50.000 50.353 20.546 50.258 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.314 30.529 30.225 30.155 30.578 50.010 30.500 10.000 10.000 10.000 10.515 20.556 30.696 11.000 10.927 30.400 30.083 30.000 31.000 10.252 10.000 20.167 30.350 20.731 20.067 30.000 10.123 40.000 10.000 20.036 30.372 30.000 20.000 10.250 10.000 10.569 40.031 50.810 30.000 30.000 40.630 10.183 20.278 30.000 20.000 10.582 40.589 50.500 20.863 31.000 10.940 20.000 40.144 10.716 30.000 30.000 30.484 30.000 30.500 30.400 30.798 30.500 20.278 40.750 10.093 30.166 40.783 30.000 20.200 20.400 20.000 10.000 30.000 10.219 20.539 30.500 20.578 30.413 30.181 50.457 20.375 20.000 10.000 10.050 50.000 40.077 40.000 30.000 10.500 50.000 50.743 30.250 20.488 40.846 30.000 30.000 10.800 30.069 30.000 30.000 30.000 20.000 31.000 10.607 40.000 20.200 10.500 10.694 20.528 20.063 30.659 20.000 20.594 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.716 40.647 50.221 20.857 40.000 30.000 30.217 30.346 30.071 50.530 51.000 10.429 30.000 20.286 30.000 30.826 50.706 30.208 40.000 30.250 40.744 50.000 30.500 20.042 10.000 30.000 20.746 30.000 10.000 10.517 10.625 30.085 50.333 30.000 41.000 10.378 40.533 50.376 40.042 50.814 30.000 10.000 20.765 31.000 10.600 30.000 10.000 40.667 30.000 10.472 10.333 20.337 30.605 30.305 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.


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 ScanNet0.794 10.941 30.813 160.851 70.782 50.890 20.597 10.916 10.696 70.713 30.979 10.635 10.384 20.793 20.907 60.821 30.790 290.696 100.967 30.903 10.805 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.
PonderV20.785 20.978 10.800 240.833 210.788 30.853 140.545 150.910 40.713 10.705 40.979 10.596 50.390 10.769 100.832 390.821 30.792 280.730 10.975 10.897 30.785 3
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 30.964 20.855 10.843 150.781 60.858 100.575 50.831 300.685 120.714 20.979 10.594 60.310 240.801 10.892 140.841 20.819 30.723 40.940 110.887 50.725 21
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 130.836 180.790 20.875 30.576 40.905 50.704 40.739 10.969 90.611 20.349 90.756 190.958 10.702 410.805 130.708 70.916 290.898 20.801 2
ResLFE_HDS0.772 50.939 40.824 60.854 60.771 80.840 280.564 90.900 70.686 110.677 100.961 140.537 280.348 100.769 100.903 80.785 90.815 50.676 190.939 120.880 100.772 7
PPT-SpUNet-Joint0.766 60.932 50.794 300.829 230.751 200.854 120.540 180.903 60.630 310.672 130.963 120.565 190.357 70.788 30.900 100.737 230.802 140.685 140.950 50.887 50.780 4
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.
OctFormerpermissive0.766 60.925 70.808 200.849 90.786 40.846 240.566 80.876 130.690 90.674 120.960 150.576 150.226 630.753 210.904 70.777 110.815 50.722 50.923 250.877 120.776 6
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net0.764 80.924 80.819 110.840 160.757 150.853 140.580 20.848 230.709 30.643 210.958 180.587 100.295 300.753 210.884 180.758 170.815 50.725 30.927 220.867 190.743 13
OccuSeg+Semantic0.764 80.758 560.796 280.839 170.746 220.907 10.562 100.850 220.680 140.672 130.978 40.610 30.335 150.777 60.819 420.847 10.830 10.691 120.972 20.885 70.727 19
O-CNNpermissive0.762 100.924 80.823 70.844 140.770 90.852 160.577 30.847 250.711 20.640 250.958 180.592 70.217 690.762 150.888 150.758 170.813 90.726 20.932 200.868 180.744 12
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
OA-CNN-L_ScanNet200.756 110.783 420.826 50.858 40.776 70.837 310.548 140.896 100.649 230.675 110.962 130.586 110.335 150.771 90.802 460.770 130.787 310.691 120.936 150.880 100.761 9
PNE0.755 120.786 400.835 40.834 200.758 130.849 190.570 70.836 290.648 240.668 150.978 40.581 140.367 50.683 320.856 270.804 50.801 180.678 160.961 40.889 40.716 26
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer0.755 120.927 60.822 80.836 180.801 10.849 190.516 280.864 190.651 220.680 90.958 180.584 130.282 380.759 170.855 290.728 250.802 140.678 160.880 550.873 170.756 10
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
DMF-Net0.752 140.906 120.793 320.802 380.689 370.825 420.556 110.867 150.681 130.602 400.960 150.555 240.365 60.779 50.859 240.747 200.795 250.717 60.917 280.856 270.764 8
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
PointTransformerV20.752 140.742 630.809 190.872 10.758 130.860 90.552 120.891 110.610 380.687 50.960 150.559 220.304 270.766 130.926 30.767 140.797 210.644 300.942 90.876 150.722 23
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 160.793 380.790 330.807 340.750 210.856 110.524 240.881 120.588 500.642 240.977 70.591 80.274 430.781 40.929 20.804 50.796 220.642 310.947 70.885 70.715 27
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 160.909 100.818 130.811 310.752 180.839 300.485 430.842 260.673 150.644 200.957 220.528 340.305 260.773 80.859 240.788 70.818 40.693 110.916 290.856 270.723 22
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 180.623 890.804 220.859 30.745 230.824 440.501 330.912 30.690 90.685 70.956 230.567 180.320 210.768 120.918 40.720 300.802 140.676 190.921 260.881 90.779 5
StratifiedFormerpermissive0.747 190.901 130.803 230.845 130.757 150.846 240.512 290.825 330.696 70.645 190.956 230.576 150.262 540.744 260.861 230.742 210.770 400.705 80.899 410.860 240.734 14
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
VMNetpermissive0.746 200.870 180.838 20.858 40.729 280.850 180.501 330.874 140.587 510.658 170.956 230.564 200.299 280.765 140.900 100.716 330.812 100.631 360.939 120.858 250.709 28
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)
Virtual MVFusion0.746 200.771 500.819 110.848 110.702 340.865 80.397 810.899 80.699 50.664 160.948 510.588 90.330 170.746 250.851 330.764 150.796 220.704 90.935 160.866 200.728 17
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN0.744 220.842 260.800 240.767 520.740 240.836 330.541 170.914 20.672 160.626 290.958 180.552 250.272 450.777 60.886 170.696 420.801 180.674 220.941 100.858 250.717 24
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-Net0.743 230.620 900.799 270.849 90.730 270.822 460.493 400.897 90.664 170.681 80.955 260.562 210.378 30.760 160.903 80.738 220.801 180.673 230.907 330.877 120.745 11
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 240.816 330.806 210.807 340.752 180.828 400.575 50.839 280.699 50.637 260.954 320.520 370.320 210.755 200.834 370.760 160.772 370.676 190.915 310.862 220.717 24
SAT0.742 240.860 210.765 450.819 260.769 100.848 210.533 200.829 310.663 180.631 280.955 260.586 110.274 430.753 210.896 120.729 240.760 470.666 250.921 260.855 290.733 15
LargeKernel3D0.739 260.909 100.820 100.806 360.740 240.852 160.545 150.826 320.594 490.643 210.955 260.541 270.263 530.723 300.858 260.775 120.767 410.678 160.933 180.848 340.694 33
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNetpermissive0.736 270.859 220.818 130.832 220.709 320.840 280.521 260.853 210.660 200.643 210.951 410.544 260.286 360.731 280.893 130.675 510.772 370.683 150.874 620.852 320.727 19
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
RPN0.736 270.776 460.790 330.851 70.754 170.854 120.491 420.866 170.596 480.686 60.955 260.536 290.342 120.624 470.869 200.787 80.802 140.628 370.927 220.875 160.704 30
IPCA0.731 290.890 140.837 30.864 20.726 290.873 40.530 230.824 340.489 830.647 180.978 40.609 40.336 140.624 470.733 550.758 170.776 350.570 620.949 60.877 120.728 17
SparseConvNet0.725 300.647 860.821 90.846 120.721 300.869 50.533 200.754 540.603 440.614 330.955 260.572 170.325 190.710 310.870 190.724 280.823 20.628 370.934 170.865 210.683 36
PointTransformer++0.725 300.727 710.811 180.819 260.765 110.841 270.502 320.814 390.621 340.623 310.955 260.556 230.284 370.620 490.866 210.781 100.757 510.648 280.932 200.862 220.709 28
MatchingNet0.724 320.812 350.812 170.810 320.735 260.834 350.495 390.860 200.572 580.602 400.954 320.512 390.280 400.757 180.845 350.725 270.780 330.606 470.937 140.851 330.700 32
INS-Conv-semantic0.717 330.751 590.759 480.812 300.704 330.868 60.537 190.842 260.609 400.608 360.953 350.534 310.293 310.616 500.864 220.719 320.793 260.640 320.933 180.845 380.663 42
PointMetaBase0.714 340.835 270.785 350.821 240.684 390.846 240.531 220.865 180.614 350.596 440.953 350.500 420.246 590.674 330.888 150.692 430.764 430.624 390.849 770.844 390.675 38
contrastBoundarypermissive0.705 350.769 530.775 400.809 330.687 380.820 490.439 690.812 400.661 190.591 460.945 590.515 380.171 870.633 440.856 270.720 300.796 220.668 240.889 480.847 350.689 34
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 360.774 480.800 240.793 430.760 120.847 230.471 470.802 430.463 900.634 270.968 110.491 450.271 470.726 290.910 50.706 370.815 50.551 730.878 560.833 400.570 73
RFCR0.702 370.889 150.745 590.813 290.672 420.818 530.493 400.815 380.623 320.610 340.947 530.470 530.249 580.594 530.848 340.705 380.779 340.646 290.892 460.823 460.611 56
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 Click0.701 380.825 310.796 280.723 590.716 310.832 360.433 710.816 360.634 290.609 350.969 90.418 790.344 110.559 650.833 380.715 340.808 120.560 670.902 380.847 350.680 37
JSENetpermissive0.699 390.881 170.762 460.821 240.667 430.800 650.522 250.792 460.613 360.607 370.935 790.492 440.205 740.576 580.853 310.691 450.758 490.652 270.872 650.828 430.649 46
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-Click0.693 400.743 620.794 300.655 820.684 390.822 460.497 380.719 640.622 330.617 320.977 70.447 660.339 130.750 240.664 710.703 400.790 290.596 520.946 80.855 290.647 47
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 410.732 670.772 410.786 440.677 410.866 70.517 270.848 230.509 760.626 290.952 390.536 290.225 650.545 710.704 620.689 480.810 110.564 660.903 370.854 310.729 16
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 420.884 160.754 520.795 410.647 490.818 530.422 730.802 430.612 370.604 380.945 590.462 560.189 820.563 640.853 310.726 260.765 420.632 350.904 350.821 490.606 60
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 430.704 760.741 630.754 560.656 450.829 380.501 330.741 590.609 400.548 540.950 450.522 360.371 40.633 440.756 500.715 340.771 390.623 400.861 730.814 510.658 43
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 440.866 190.748 560.819 260.645 510.794 680.450 590.802 430.587 510.604 380.945 590.464 550.201 770.554 670.840 360.723 290.732 600.602 500.907 330.822 480.603 63
DGNet0.684 450.712 750.784 360.782 480.658 440.835 340.499 370.823 350.641 260.597 430.950 450.487 460.281 390.575 590.619 750.647 640.764 430.620 420.871 680.846 370.688 35
KP-FCNN0.684 450.847 250.758 500.784 460.647 490.814 560.473 460.772 490.605 420.594 450.935 790.450 640.181 850.587 540.805 450.690 460.785 320.614 430.882 520.819 500.632 52
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 450.728 700.757 510.776 490.690 350.804 630.464 520.816 360.577 570.587 470.945 590.508 410.276 420.671 340.710 600.663 560.750 540.589 570.881 530.832 420.653 45
Superpoint Network0.683 480.851 240.728 670.800 400.653 470.806 610.468 490.804 410.572 580.602 400.946 560.453 630.239 620.519 760.822 400.689 480.762 460.595 540.895 440.827 440.630 53
PointContrast_LA_SEM0.683 480.757 570.784 360.786 440.639 530.824 440.408 760.775 480.604 430.541 560.934 830.532 320.269 490.552 680.777 480.645 670.793 260.640 320.913 320.824 450.671 39
VI-PointConv0.676 500.770 520.754 520.783 470.621 570.814 560.552 120.758 520.571 600.557 520.954 320.529 330.268 510.530 740.682 660.675 510.719 630.603 490.888 490.833 400.665 41
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 510.789 390.748 560.763 540.635 550.814 560.407 780.747 560.581 550.573 490.950 450.484 470.271 470.607 510.754 510.649 610.774 360.596 520.883 510.823 460.606 60
SALANet0.670 520.816 330.770 430.768 510.652 480.807 600.451 560.747 560.659 210.545 550.924 890.473 520.149 970.571 610.811 440.635 700.746 550.623 400.892 460.794 640.570 73
PointConvpermissive0.666 530.781 430.759 480.699 670.644 520.822 460.475 450.779 470.564 630.504 720.953 350.428 730.203 760.586 560.754 510.661 570.753 520.588 580.902 380.813 530.642 48
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 530.703 770.781 380.751 580.655 460.830 370.471 470.769 500.474 860.537 580.951 410.475 510.279 410.635 420.698 650.675 510.751 530.553 720.816 840.806 550.703 31
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 550.746 600.708 700.722 600.638 540.820 490.451 560.566 910.599 460.541 560.950 450.510 400.313 230.648 390.819 420.616 750.682 780.590 560.869 690.810 540.656 44
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 560.558 970.751 540.655 820.690 350.722 900.453 550.867 150.579 560.576 480.893 1010.523 350.293 310.733 270.571 790.692 430.659 850.606 470.875 590.804 570.668 40
DCM-Net0.658 560.778 440.702 730.806 360.619 580.813 590.468 490.693 720.494 790.524 640.941 710.449 650.298 290.510 780.821 410.675 510.727 620.568 640.826 820.803 580.637 50
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 580.698 790.743 610.650 840.564 750.820 490.505 310.758 520.631 300.479 760.945 590.480 490.226 630.572 600.774 490.690 460.735 580.614 430.853 760.776 790.597 66
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 590.752 580.734 650.664 800.583 700.815 550.399 800.754 540.639 270.535 600.942 690.470 530.309 250.665 350.539 810.650 600.708 680.635 340.857 750.793 660.642 48
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 600.778 440.731 660.699 670.577 710.829 380.446 610.736 600.477 850.523 660.945 590.454 600.269 490.484 850.749 540.618 730.738 560.599 510.827 810.792 690.621 55
PointConv-SFPN0.641 610.776 460.703 720.721 610.557 780.826 410.451 560.672 770.563 640.483 750.943 680.425 760.162 920.644 400.726 560.659 580.709 670.572 610.875 590.786 740.559 79
MVPNetpermissive0.641 610.831 280.715 680.671 770.590 660.781 740.394 820.679 740.642 250.553 530.937 760.462 560.256 550.649 380.406 940.626 710.691 750.666 250.877 570.792 690.608 59
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 630.717 740.701 740.692 700.576 720.801 640.467 510.716 650.563 640.459 820.953 350.429 720.169 890.581 570.854 300.605 760.710 650.550 740.894 450.793 660.575 71
FPConvpermissive0.639 640.785 410.760 470.713 650.603 610.798 660.392 830.534 960.603 440.524 640.948 510.457 580.250 570.538 720.723 580.598 800.696 730.614 430.872 650.799 590.567 76
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 650.797 370.769 440.641 890.590 660.820 490.461 530.537 950.637 280.536 590.947 530.388 860.206 730.656 360.668 690.647 640.732 600.585 590.868 700.793 660.473 98
PointSPNet0.637 660.734 660.692 810.714 640.576 720.797 670.446 610.743 580.598 470.437 870.942 690.403 820.150 960.626 460.800 470.649 610.697 720.557 700.846 780.777 780.563 77
SConv0.636 670.830 290.697 770.752 570.572 740.780 760.445 630.716 650.529 690.530 610.951 410.446 670.170 880.507 800.666 700.636 690.682 780.541 800.886 500.799 590.594 67
Supervoxel-CNN0.635 680.656 840.711 690.719 620.613 590.757 850.444 660.765 510.534 680.566 500.928 870.478 500.272 450.636 410.531 830.664 550.645 890.508 870.864 720.792 690.611 56
joint point-basedpermissive0.634 690.614 910.778 390.667 790.633 560.825 420.420 740.804 410.467 880.561 510.951 410.494 430.291 330.566 620.458 890.579 860.764 430.559 690.838 790.814 510.598 65
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 700.731 680.688 840.675 740.591 650.784 730.444 660.565 920.610 380.492 730.949 490.456 590.254 560.587 540.706 610.599 790.665 840.612 460.868 700.791 720.579 70
PointNet2-SFPN0.631 710.771 500.692 810.672 750.524 830.837 310.440 680.706 700.538 670.446 840.944 650.421 780.219 680.552 680.751 530.591 820.737 570.543 790.901 400.768 810.557 80
APCF-Net0.631 710.742 630.687 860.672 750.557 780.792 710.408 760.665 780.545 660.508 690.952 390.428 730.186 830.634 430.702 630.620 720.706 690.555 710.873 630.798 610.581 69
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF0.631 710.626 880.745 590.801 390.607 600.751 860.506 300.729 630.565 620.491 740.866 1040.434 680.197 800.595 520.630 740.709 360.705 700.560 670.875 590.740 890.491 93
FusionAwareConv0.630 740.604 930.741 630.766 530.590 660.747 870.501 330.734 610.503 780.527 620.919 930.454 600.323 200.550 700.420 930.678 500.688 760.544 770.896 430.795 630.627 54
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 750.800 360.625 960.719 620.545 800.806 610.445 630.597 860.448 930.519 670.938 750.481 480.328 180.489 840.499 880.657 590.759 480.592 550.881 530.797 620.634 51
SegGroup_sempermissive0.627 760.818 320.747 580.701 660.602 620.764 820.385 870.629 830.490 810.508 690.931 860.409 810.201 770.564 630.725 570.618 730.692 740.539 810.873 630.794 640.548 83
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
dtc_net0.625 770.703 770.751 540.794 420.535 810.848 210.480 440.676 760.528 700.469 790.944 650.454 600.004 1090.464 870.636 730.704 390.758 490.548 760.924 240.787 730.492 92
SIConv0.625 770.830 290.694 790.757 550.563 760.772 800.448 600.647 810.520 720.509 680.949 490.431 710.191 810.496 820.614 760.647 640.672 820.535 830.876 580.783 750.571 72
HPEIN0.618 790.729 690.668 870.647 860.597 640.766 810.414 750.680 730.520 720.525 630.946 560.432 690.215 700.493 830.599 770.638 680.617 940.570 620.897 420.806 550.605 62
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-GCNpermissive0.610 800.858 230.772 410.489 1010.532 820.792 710.404 790.643 820.570 610.507 710.935 790.414 800.046 1060.510 780.702 630.602 780.705 700.549 750.859 740.773 800.534 86
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 810.760 550.667 880.649 850.521 840.793 690.457 540.648 800.528 700.434 890.947 530.401 830.153 950.454 880.721 590.648 630.717 640.536 820.904 350.765 820.485 94
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 820.634 870.743 610.697 690.601 630.781 740.437 700.585 890.493 800.446 840.933 840.394 840.011 1080.654 370.661 720.603 770.733 590.526 840.832 800.761 840.480 95
LAP-D0.594 830.720 720.692 810.637 900.456 930.773 790.391 850.730 620.587 510.445 860.940 730.381 870.288 340.434 910.453 910.591 820.649 870.581 600.777 880.749 880.610 58
DPC0.592 840.720 720.700 750.602 940.480 890.762 840.380 880.713 680.585 540.437 870.940 730.369 890.288 340.434 910.509 870.590 840.639 920.567 650.772 890.755 860.592 68
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 850.766 540.659 910.683 720.470 920.740 890.387 860.620 850.490 810.476 770.922 910.355 920.245 600.511 770.511 860.571 870.643 900.493 910.872 650.762 830.600 64
ROSMRF0.580 860.772 490.707 710.681 730.563 760.764 820.362 900.515 970.465 890.465 810.936 780.427 750.207 720.438 890.577 780.536 900.675 810.486 920.723 950.779 760.524 88
SD-DETR0.576 870.746 600.609 1000.445 1050.517 850.643 1010.366 890.714 670.456 910.468 800.870 1030.432 690.264 520.558 660.674 670.586 850.688 760.482 930.739 930.733 910.537 85
SQN_0.1%0.569 880.676 810.696 780.657 810.497 860.779 770.424 720.548 930.515 740.376 940.902 1000.422 770.357 70.379 950.456 900.596 810.659 850.544 770.685 980.665 1020.556 81
TextureNetpermissive0.566 890.672 830.664 890.671 770.494 870.719 910.445 630.678 750.411 990.396 920.935 790.356 910.225 650.412 930.535 820.565 880.636 930.464 950.794 870.680 990.568 75
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
DVVNet0.562 900.648 850.700 750.770 500.586 690.687 950.333 940.650 790.514 750.475 780.906 970.359 900.223 670.340 970.442 920.422 1010.668 830.501 880.708 960.779 760.534 86
Pointnet++ & Featurepermissive0.557 910.735 650.661 900.686 710.491 880.744 880.392 830.539 940.451 920.375 950.946 560.376 880.205 740.403 940.356 970.553 890.643 900.497 890.824 830.756 850.515 89
GMLPs0.538 920.495 1020.693 800.647 860.471 910.793 690.300 970.477 980.505 770.358 960.903 990.327 950.081 1030.472 860.529 840.448 990.710 650.509 850.746 910.737 900.554 82
PanopticFusion-label0.529 930.491 1030.688 840.604 930.386 980.632 1020.225 1070.705 710.434 960.293 1020.815 1050.348 930.241 610.499 810.669 680.507 920.649 870.442 1010.796 860.602 1050.561 78
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_weak0.516 940.676 810.591 1030.609 910.442 940.774 780.335 930.597 860.422 980.357 970.932 850.341 940.094 1020.298 990.528 850.473 970.676 800.495 900.602 1040.721 940.349 105
Online SegFusion0.515 950.607 920.644 940.579 960.434 950.630 1030.353 910.628 840.440 940.410 900.762 1080.307 970.167 900.520 750.403 950.516 910.565 970.447 990.678 990.701 960.514 90
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, FTSDF0.501 960.558 970.608 1010.424 1070.478 900.690 940.246 1030.586 880.468 870.450 830.911 950.394 840.160 930.438 890.212 1040.432 1000.541 1020.475 940.742 920.727 920.477 96
PCNN0.498 970.559 960.644 940.560 980.420 970.711 930.229 1050.414 990.436 950.352 980.941 710.324 960.155 940.238 1040.387 960.493 930.529 1030.509 850.813 850.751 870.504 91
3DMV0.484 980.484 1040.538 1050.643 880.424 960.606 1060.310 950.574 900.433 970.378 930.796 1060.301 980.214 710.537 730.208 1050.472 980.507 1060.413 1040.693 970.602 1050.539 84
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 990.577 950.611 990.356 1090.321 1060.715 920.299 990.376 1030.328 1060.319 1000.944 650.285 1000.164 910.216 1070.229 1020.484 950.545 1010.456 970.755 900.709 950.475 97
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1000.679 800.604 1020.578 970.380 990.682 960.291 1000.106 1090.483 840.258 1070.920 920.258 1040.025 1070.231 1060.325 980.480 960.560 990.463 960.725 940.666 1010.231 109
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1010.474 1050.623 970.463 1030.366 1010.651 990.310 950.389 1020.349 1040.330 990.937 760.271 1020.126 990.285 1000.224 1030.350 1060.577 960.445 1000.625 1020.723 930.394 101
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
SurfaceConvPF0.442 1020.505 1010.622 980.380 1080.342 1040.654 980.227 1060.397 1010.367 1020.276 1040.924 890.240 1050.198 790.359 960.262 1000.366 1030.581 950.435 1020.640 1010.668 1000.398 100
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 1020.548 990.548 1040.597 950.363 1020.628 1040.300 970.292 1040.374 1010.307 1010.881 1020.268 1030.186 830.238 1040.204 1060.407 1020.506 1070.449 980.667 1000.620 1040.462 99
Tangent Convolutionspermissive0.438 1040.437 1070.646 930.474 1020.369 1000.645 1000.353 910.258 1060.282 1080.279 1030.918 940.298 990.147 980.283 1010.294 990.487 940.562 980.427 1030.619 1030.633 1030.352 104
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1050.525 1000.647 920.522 990.324 1050.488 1090.077 1100.712 690.353 1030.401 910.636 1100.281 1010.176 860.340 970.565 800.175 1100.551 1000.398 1050.370 1100.602 1050.361 103
SPLAT Netcopyleft0.393 1060.472 1060.511 1060.606 920.311 1070.656 970.245 1040.405 1000.328 1060.197 1080.927 880.227 1070.000 1110.001 1110.249 1010.271 1090.510 1040.383 1070.593 1050.699 970.267 107
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 1070.297 1090.491 1070.432 1060.358 1030.612 1050.274 1010.116 1080.411 990.265 1050.904 980.229 1060.079 1040.250 1020.185 1070.320 1070.510 1040.385 1060.548 1060.597 1080.394 101
PointNet++permissive0.339 1080.584 940.478 1080.458 1040.256 1090.360 1100.250 1020.247 1070.278 1090.261 1060.677 1090.183 1080.117 1000.212 1080.145 1090.364 1040.346 1100.232 1100.548 1060.523 1090.252 108
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 1090.353 1080.290 1100.278 1100.166 1100.553 1070.169 1090.286 1050.147 1100.148 1100.908 960.182 1090.064 1050.023 1100.018 1110.354 1050.363 1080.345 1080.546 1080.685 980.278 106
ScanNetpermissive0.306 1100.203 1100.366 1090.501 1000.311 1070.524 1080.211 1080.002 1110.342 1050.189 1090.786 1070.145 1100.102 1010.245 1030.152 1080.318 1080.348 1090.300 1090.460 1090.437 1100.182 110
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 1110.000 1110.041 1110.172 1110.030 1110.062 1110.001 1110.035 1100.004 1110.051 1110.143 1110.019 1110.003 1100.041 1090.050 1100.003 1110.054 1110.018 1110.005 1110.264 1110.082 111


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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
OneFormer3Dcopyleft0.896 11.000 11.000 10.913 40.858 40.951 30.786 90.837 130.916 70.908 20.778 40.803 20.750 101.000 10.976 20.926 40.882 50.995 390.849 1
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception0.884 21.000 10.979 140.872 130.869 20.892 190.806 60.890 50.835 210.892 40.755 100.811 10.779 80.955 390.951 30.876 180.914 10.997 330.840 2
Spherical Mask(CtoF)0.875 31.000 10.991 90.873 120.850 50.946 50.691 180.752 270.926 40.889 60.759 80.794 40.820 21.000 10.912 130.900 70.878 91.000 10.769 14
TD3Dpermissive0.875 31.000 10.976 170.877 100.783 200.970 10.889 10.828 140.945 30.803 140.713 160.720 160.709 131.000 10.936 90.934 30.873 121.000 10.791 11
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Queryformer0.874 51.000 10.978 160.809 290.876 10.936 90.702 150.716 320.920 60.875 90.766 50.772 60.818 41.000 10.995 10.916 50.892 21.000 10.767 15
SoftGroup++0.874 51.000 10.972 180.947 10.839 80.898 180.556 320.913 20.881 130.756 160.828 20.748 100.821 11.000 10.937 80.937 10.887 31.000 10.821 5
Mask3D0.870 71.000 10.985 110.782 370.818 130.938 80.760 100.749 280.923 50.877 80.760 70.785 50.820 21.000 10.912 130.864 290.878 90.983 450.825 4
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ExtMask3D0.867 81.000 11.000 10.756 440.816 140.940 70.795 70.760 260.862 150.888 70.739 120.763 70.774 91.000 10.929 110.878 170.879 71.000 10.819 7
SoftGrouppermissive0.865 91.000 10.969 190.860 150.860 30.913 130.558 290.899 30.911 80.760 150.828 10.736 120.802 60.981 360.919 120.875 190.877 111.000 10.820 6
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
MAFT0.860 101.000 10.990 100.810 280.829 90.949 40.809 50.688 390.836 200.904 30.751 110.796 30.741 111.000 10.864 310.848 360.837 171.000 10.828 3
IPCA-Inst0.851 111.000 10.968 200.884 90.842 70.862 310.693 170.812 190.888 120.677 280.783 30.698 170.807 51.000 10.911 190.865 280.865 141.000 10.757 18
SPFormerpermissive0.851 111.000 10.994 50.806 300.774 220.942 60.637 210.849 110.859 170.889 50.720 150.730 140.665 191.000 10.911 190.868 270.873 131.000 10.796 9
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
Mask3D_evaluation0.843 131.000 10.955 250.847 170.795 160.932 100.750 120.780 240.891 100.818 110.737 130.633 260.703 141.000 10.902 230.870 230.820 190.941 530.805 8
SIM3D0.842 141.000 10.998 30.608 570.717 410.908 140.818 40.699 360.798 280.908 10.760 60.733 130.793 71.000 10.912 130.831 410.883 41.000 10.792 10
ISBNetpermissive0.835 151.000 10.950 260.731 460.819 110.918 110.790 80.740 290.851 190.831 100.661 240.742 110.650 221.000 10.937 70.814 490.836 181.000 10.765 16
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
SphereSeg0.835 151.000 10.963 230.891 70.794 170.954 20.822 30.710 330.961 20.721 200.693 220.530 390.653 211.000 10.867 300.857 320.859 150.991 420.771 13
GraphCut0.832 171.000 10.922 400.724 480.798 150.902 170.701 160.856 90.859 160.715 210.706 170.748 90.640 331.000 10.934 100.862 300.880 61.000 10.729 21
TopoSeg0.832 171.000 10.981 130.933 20.819 120.826 400.524 380.841 120.811 250.681 270.759 90.687 180.727 120.981 360.911 190.883 130.853 161.000 10.756 19
PBNetpermissive0.825 191.000 10.963 220.837 200.843 60.865 260.822 20.647 420.878 140.733 180.639 310.683 190.650 221.000 10.853 320.870 240.820 201.000 10.744 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
SSEC0.820 201.000 10.983 120.924 30.826 100.817 430.415 470.899 40.793 300.673 290.731 140.636 240.653 201.000 10.939 60.804 510.878 81.000 10.780 12
DKNet0.815 211.000 10.930 320.844 180.765 260.915 120.534 360.805 210.805 270.807 130.654 250.763 80.650 221.000 10.794 440.881 140.766 241.000 10.758 17
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN0.806 221.000 10.992 70.789 320.723 390.891 200.650 200.810 200.832 220.665 310.699 200.658 200.700 151.000 10.881 250.832 400.774 220.997 330.613 41
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Box2Mask0.803 231.000 10.962 240.874 110.707 440.887 230.686 190.598 470.961 10.715 220.694 210.469 440.700 151.000 10.912 130.902 60.753 290.997 330.637 35
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
HAISpermissive0.803 231.000 10.994 50.820 240.759 270.855 320.554 330.882 60.827 240.615 370.676 230.638 230.646 311.000 10.912 130.797 540.767 230.994 400.726 22
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Mask-Group0.792 251.000 10.968 210.812 250.766 250.864 270.460 410.815 180.888 110.598 410.651 280.639 220.600 390.918 420.941 40.896 90.721 361.000 10.723 23
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.791 261.000 10.996 40.829 230.767 240.889 220.600 240.819 170.770 350.594 420.620 350.541 360.700 151.000 10.941 40.889 110.763 251.000 10.526 51
SSTNetpermissive0.789 271.000 10.840 540.888 80.717 400.835 360.717 140.684 400.627 500.724 190.652 270.727 150.600 391.000 10.912 130.822 440.757 281.000 10.691 29
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN0.788 281.000 10.978 150.867 140.781 210.833 370.527 370.824 150.806 260.549 500.596 380.551 320.700 151.000 10.853 320.935 20.733 331.000 10.651 32
DANCENET0.786 291.000 10.936 290.783 350.737 360.852 340.742 130.647 420.765 370.811 120.624 340.579 290.632 361.000 10.909 220.898 80.696 410.944 490.601 44
DENet0.786 291.000 10.929 330.736 450.750 330.720 560.755 110.934 10.794 290.590 430.561 440.537 370.650 221.000 10.882 240.804 520.789 211.000 10.719 24
DualGroup0.782 311.000 10.927 340.811 260.772 230.853 330.631 230.805 210.773 320.613 380.611 360.610 270.650 220.835 530.881 250.879 160.750 311.000 10.675 30
PointGroup0.778 321.000 10.900 440.798 310.715 420.863 280.493 390.706 340.895 90.569 480.701 180.576 300.639 341.000 10.880 270.851 340.719 370.997 330.709 26
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]
PE0.776 331.000 10.900 450.860 150.728 380.869 240.400 480.857 80.774 310.568 490.701 190.602 280.646 310.933 410.843 350.890 100.691 450.997 330.709 25
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA0.767 341.000 10.937 280.810 270.740 350.906 150.550 340.800 230.706 420.577 470.624 330.544 350.596 440.857 450.879 290.880 150.750 300.992 410.658 31
DD-UNet+Group0.764 351.000 10.897 470.837 190.753 300.830 390.459 430.824 150.699 440.629 350.653 260.438 470.650 221.000 10.880 270.858 310.690 461.000 10.650 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
INS-Conv-instance0.762 361.000 10.923 370.765 400.785 190.905 160.600 240.655 410.646 490.683 260.647 290.530 380.650 221.000 10.824 370.830 420.693 440.944 490.644 34
Dyco3Dcopyleft0.761 371.000 10.935 300.893 60.752 320.863 290.600 240.588 480.742 390.641 330.633 320.546 340.550 460.857 450.789 460.853 330.762 260.987 430.699 27
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance0.742 381.000 10.923 370.785 330.745 340.867 250.557 300.578 510.729 400.670 300.644 300.488 420.577 451.000 10.794 440.830 420.620 541.000 10.550 47
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg0.739 391.000 10.899 460.759 420.753 310.823 410.282 530.691 380.658 470.582 460.594 390.547 330.628 371.000 10.795 430.868 260.728 351.000 10.692 28
3D-MPA0.737 401.000 10.933 310.785 330.794 180.831 380.279 550.588 480.695 450.616 360.559 450.556 310.650 221.000 10.809 410.875 200.696 421.000 10.608 43
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.731 411.000 10.992 70.779 390.609 530.746 510.308 520.867 70.601 530.607 390.539 480.519 400.550 461.000 10.824 370.869 250.729 341.000 10.616 39
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS0.725 421.000 10.885 500.653 540.657 500.801 440.576 280.695 370.828 230.698 240.534 490.457 460.500 530.857 450.831 360.841 380.627 521.000 10.619 38
SSEN0.724 431.000 10.926 350.781 380.661 480.845 350.596 270.529 540.764 380.653 320.489 550.461 450.500 530.859 440.765 470.872 220.761 271.000 10.577 45
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF0.718 441.000 10.945 270.901 50.754 290.817 420.460 410.700 350.772 330.688 250.568 430.000 660.500 530.981 360.606 570.872 210.740 321.000 10.614 40
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
Sparse R-CNN0.714 451.000 10.926 360.694 490.699 460.890 210.636 220.516 550.693 460.743 170.588 400.369 510.601 380.594 590.800 420.886 120.676 470.986 440.546 48
SALoss-ResNet0.695 461.000 10.855 520.579 600.589 550.735 540.484 400.588 480.856 180.634 340.571 420.298 520.500 531.000 10.824 370.818 450.702 400.935 560.545 49
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
PanopticFusion-inst0.693 471.000 10.852 530.655 530.616 520.788 460.334 500.763 250.771 340.457 600.555 460.652 210.518 500.857 450.765 470.732 600.631 500.944 490.577 46
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS0.688 481.000 10.913 410.730 470.737 370.743 530.442 440.855 100.655 480.546 510.546 470.263 540.508 520.889 430.568 580.771 570.705 390.889 590.625 37
3D-BoNet0.687 491.000 10.887 490.836 210.587 560.643 630.550 340.620 440.724 410.522 550.501 530.243 550.512 511.000 10.751 490.807 500.661 490.909 580.612 42
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
ClickSeg_Instance0.685 501.000 10.818 560.600 580.715 430.795 450.557 300.533 530.591 550.601 400.519 510.429 490.638 350.938 400.706 520.817 470.624 530.944 490.502 53
PCJC0.684 511.000 10.895 480.757 430.659 490.862 300.189 620.739 300.606 520.712 230.581 410.515 410.650 220.857 450.357 630.785 550.631 510.889 590.635 36
SPG_WSIS0.678 521.000 10.880 510.836 210.701 450.727 550.273 570.607 460.706 430.541 530.515 520.174 580.600 390.857 450.716 510.846 370.711 381.000 10.506 52
One_Thing_One_Clickpermissive0.675 531.000 10.823 550.782 360.621 510.766 480.211 590.736 310.560 570.586 440.522 500.636 250.453 570.641 570.853 320.850 350.694 430.997 330.411 58
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_inspermissive0.637 541.000 10.923 390.593 590.561 570.746 520.143 640.504 560.766 360.485 580.442 560.372 500.530 490.714 540.815 400.775 560.673 481.000 10.431 57
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASCpermissive0.615 550.711 620.802 570.540 610.757 280.777 470.029 650.577 520.588 560.521 560.600 370.436 480.534 480.697 550.616 560.838 390.526 560.980 460.534 50
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone0.605 561.000 10.909 420.764 410.603 540.704 570.415 460.301 610.548 580.461 590.394 570.267 530.386 590.857 450.649 550.817 460.504 580.959 470.356 61
3D-SISpermissive0.558 571.000 10.773 580.614 560.503 600.691 590.200 600.412 570.498 610.546 520.311 620.103 620.600 390.857 450.382 600.799 530.445 640.938 550.371 59
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.544 580.500 650.655 640.661 520.663 470.765 490.432 450.214 640.612 510.584 450.499 540.204 570.286 630.429 620.655 540.650 650.539 550.950 480.499 54
Hier3Dcopyleft0.540 591.000 10.727 590.626 550.467 630.693 580.200 600.412 570.480 620.528 540.318 610.077 650.600 390.688 560.382 600.768 580.472 600.941 530.350 62
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class0.497 600.250 670.902 430.689 500.540 580.747 500.276 560.610 450.268 660.489 570.348 580.000 660.243 660.220 650.663 530.814 480.459 620.928 570.496 55
Sem_Recon_ins0.484 610.764 610.608 660.470 630.521 590.637 640.311 510.218 630.348 650.365 640.223 630.222 560.258 640.629 580.734 500.596 660.509 570.858 620.444 56
tmp0.474 621.000 10.727 590.433 650.481 620.673 610.022 670.380 590.517 600.436 620.338 600.128 600.343 610.429 620.291 650.728 610.473 590.833 630.300 64
SemRegionNet-20cls0.470 631.000 10.727 590.447 640.481 610.678 600.024 660.380 590.518 590.440 610.339 590.128 600.350 600.429 620.212 660.711 620.465 610.833 630.290 65
ASIS0.422 640.333 660.707 620.676 510.401 640.650 620.350 490.177 650.594 540.376 630.202 640.077 640.404 580.571 600.197 670.674 640.447 630.500 660.260 66
3D-BEVIS0.401 650.667 630.687 630.419 660.137 670.587 650.188 630.235 620.359 640.211 660.093 670.080 630.311 620.571 600.382 600.754 590.300 660.874 610.357 60
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.390 660.556 640.636 650.493 620.353 650.539 660.271 580.160 660.450 630.359 650.178 650.146 590.250 650.143 660.347 640.698 630.436 650.667 650.331 63
MaskRCNN 2d->3d Proj0.261 670.903 600.081 670.008 670.233 660.175 670.280 540.106 670.150 670.203 670.175 660.480 430.218 670.143 660.542 590.404 670.153 670.393 670.049 67


This table lists the benchmark results for the 2D 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
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 20.512 10.422 170.898