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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PonderV2 ScanNet2000.346 50.552 70.270 80.175 60.497 80.070 130.239 70.000 10.000 40.000 10.232 150.412 70.584 30.842 30.804 50.212 80.540 90.000 30.433 150.106 110.000 90.590 40.290 100.548 50.243 60.000 80.356 100.000 10.000 30.062 100.398 110.441 60.000 10.104 100.000 20.888 40.076 100.682 90.030 30.094 70.491 100.351 120.869 90.000 10.063 10.403 110.700 20.000 100.660 120.881 80.761 40.050 90.186 80.852 110.000 10.007 80.570 70.100 20.565 20.326 60.641 110.431 50.290 130.621 50.259 50.408 100.622 100.125 30.082 100.950 40.179 50.000 10.263 30.424 60.193 90.558 60.880 20.545 120.375 70.727 30.445 90.499 80.000 40.000 10.475 70.002 60.034 60.083 80.000 50.924 30.290 40.636 70.115 130.400 50.874 40.186 80.000 10.611 90.128 30.113 20.000 40.000 10.000 80.584 110.636 100.103 120.385 90.843 70.283 40.603 60.080 70.825 70.000 60.377 110.000 10.000 70.000 40.457 120.000 30.000 60.000 10.574 120.608 80.481 30.792 40.394 40.000 10.357 100.503 110.261 100.817 110.504 130.304 70.472 40.115 90.000 20.750 70.677 70.202 20.000 100.509 80.729 60.000 10.519 110.000 140.000 60.000 10.620 120.000 20.000 10.660 60.560 60.486 50.384 70.346 80.952 60.247 130.667 50.436 110.269 30.691 60.000 10.010 70.787 90.889 30.880 40.000 10.810 80.336 40.860 80.000 10.606 60.009 80.248 90.681 70.392 10
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.
IMFSegNet0.337 80.535 110.266 100.169 80.527 30.072 110.147 120.000 10.000 40.000 10.341 80.152 150.544 50.678 150.803 70.264 30.868 20.000 30.853 40.181 50.040 50.398 100.357 30.366 160.208 100.000 80.317 110.000 10.000 30.187 60.418 90.274 80.000 10.118 40.000 20.884 50.095 20.715 20.000 70.095 60.592 30.424 50.472 160.000 10.000 20.426 100.564 90.000 100.692 80.915 50.759 50.001 160.170 110.831 120.000 10.004 100.493 110.000 70.492 70.228 120.675 50.396 120.382 60.277 160.311 30.442 90.551 140.177 20.066 120.947 60.126 150.000 10.051 90.544 40.263 40.469 130.786 50.633 70.311 130.708 40.422 110.432 130.000 40.000 10.497 40.000 80.038 30.122 50.000 50.910 80.251 50.655 60.211 30.343 110.840 130.204 60.000 10.637 50.112 50.000 110.000 40.000 10.000 80.743 40.660 90.143 50.359 100.821 100.264 90.571 90.054 120.810 80.000 60.380 100.000 10.133 40.094 20.576 90.667 10.000 60.000 10.546 140.572 100.361 90.699 110.000 90.000 10.406 60.524 80.568 10.829 80.505 120.196 100.119 70.263 50.032 10.755 50.683 50.036 160.026 70.634 50.791 50.000 10.383 140.109 70.000 60.000 10.645 80.000 20.000 10.469 110.545 80.373 100.297 90.447 50.953 50.300 80.728 30.509 90.132 150.642 110.000 10.031 40.824 40.769 70.839 90.000 10.810 80.000 150.867 40.000 10.378 110.004 110.177 150.644 120.442 7
GSTran0.339 70.536 100.273 60.169 70.491 90.071 120.365 30.000 10.000 40.000 10.178 160.246 130.458 130.754 80.788 90.316 10.834 50.000 30.872 20.202 30.079 30.318 130.286 110.538 70.156 130.004 70.310 120.000 10.000 30.009 140.397 120.297 70.000 10.093 110.000 20.876 80.060 130.690 50.000 70.086 80.517 80.358 100.667 130.000 10.000 20.473 60.670 30.000 100.731 30.896 70.765 20.061 80.256 50.889 60.000 10.000 110.480 130.000 70.412 120.279 80.690 40.366 130.373 70.466 90.357 10.514 50.648 80.024 60.615 20.949 50.183 40.000 10.162 70.564 30.196 80.535 80.413 140.638 50.410 40.682 50.445 90.470 110.289 20.000 10.358 120.000 80.022 100.161 40.008 40.877 130.495 10.461 150.161 100.348 100.853 110.199 70.000 10.643 40.109 80.014 80.000 40.000 10.000 80.681 70.705 60.079 150.441 80.872 30.282 50.593 70.096 50.786 110.021 30.495 10.000 10.118 50.000 40.487 110.000 30.002 50.000 10.589 80.563 120.144 150.682 130.109 70.000 10.235 150.455 120.368 60.659 160.609 60.000 160.060 100.033 150.000 20.746 80.648 120.084 140.000 100.803 10.832 10.000 10.614 40.000 140.497 10.000 10.597 130.000 20.000 10.621 90.506 120.459 60.252 110.228 120.913 130.369 40.665 60.598 40.139 100.666 80.000 10.097 20.841 20.698 110.857 60.000 10.811 70.129 110.784 130.000 10.386 100.012 70.317 30.696 50.425 8
DITR0.409 20.616 10.351 10.215 30.651 10.238 10.400 20.000 10.340 10.000 10.534 20.476 40.585 20.687 140.853 10.143 120.854 30.000 30.865 30.167 60.000 90.175 160.573 10.617 20.372 10.362 10.591 10.000 10.000 30.330 10.494 20.247 90.000 10.385 10.000 20.878 70.037 150.791 10.053 20.118 30.479 110.429 40.940 30.000 10.000 20.461 80.562 100.093 50.628 140.991 10.762 30.135 30.270 30.917 30.000 10.140 40.597 20.000 70.361 130.375 10.730 20.431 50.459 30.410 130.008 150.656 10.814 10.036 50.554 40.947 60.139 110.000 10.263 30.896 10.191 100.615 40.839 30.757 10.399 60.877 10.504 50.524 60.000 40.000 10.587 30.000 80.022 100.077 90.921 10.928 20.132 90.670 40.759 10.652 10.862 70.091 100.000 10.662 30.072 160.000 110.000 40.000 10.496 10.852 20.752 20.152 30.743 10.953 10.301 30.625 30.053 130.913 10.399 10.452 50.000 10.000 70.000 40.742 20.000 30.000 60.000 10.694 20.643 40.444 60.784 70.000 90.000 10.571 10.614 30.491 30.938 10.559 90.357 50.107 80.404 10.000 20.796 20.688 40.148 60.186 10.629 60.827 20.000 10.558 100.198 40.000 60.000 10.723 20.000 20.000 10.833 10.619 10.609 20.478 40.617 10.959 40.370 30.597 100.737 20.191 50.752 20.000 10.118 10.853 10.925 20.670 130.000 10.831 30.000 150.873 30.000 10.699 10.005 100.360 10.723 30.235 14
BFANet ScanNet200permissive0.360 40.553 60.293 40.193 40.483 100.096 50.266 60.000 10.000 40.000 10.298 130.255 110.661 10.810 50.810 30.194 90.785 60.000 30.000 160.161 70.000 90.494 60.382 20.574 40.258 40.000 80.372 80.000 10.000 30.043 120.436 70.000 100.000 10.239 20.000 20.901 30.105 10.689 60.025 40.128 20.614 20.436 10.493 150.000 10.000 20.526 40.546 120.109 40.651 130.953 40.753 80.101 60.143 120.897 40.000 10.431 10.469 140.000 70.522 50.337 50.661 80.459 20.409 40.666 40.102 120.508 60.757 40.000 80.060 130.970 30.497 10.000 10.376 20.511 50.262 50.688 20.921 10.617 90.321 120.590 60.491 60.556 30.000 40.000 10.481 50.093 10.043 20.284 20.000 50.875 140.135 80.669 50.124 120.394 60.849 120.298 20.000 10.476 160.088 130.042 50.000 40.000 10.254 30.653 100.741 40.215 10.573 50.852 60.266 80.654 10.056 110.835 30.000 60.492 20.000 10.000 70.000 40.612 80.000 30.000 60.000 10.616 50.469 160.460 40.698 120.516 20.000 10.378 80.563 40.476 40.863 50.574 80.330 60.000 110.282 40.000 20.760 40.710 20.233 10.000 100.641 40.814 30.000 10.585 80.053 110.000 60.000 10.629 100.000 20.000 10.678 30.528 110.534 40.129 130.596 20.973 30.264 110.772 20.526 80.139 100.707 40.000 10.000 120.764 130.591 150.848 70.000 10.827 40.338 30.806 120.000 10.568 70.151 60.358 20.659 100.510 4
ALS-MinkowskiNetcopyleft0.414 10.610 20.322 30.271 10.542 20.153 20.159 110.000 10.000 40.000 10.404 40.503 30.532 70.672 160.804 50.285 20.888 10.000 30.900 10.226 10.087 20.598 30.342 50.671 10.217 90.087 30.449 30.000 10.000 30.253 20.477 51.000 10.000 10.118 40.000 20.905 10.071 120.710 30.076 10.047 150.665 10.376 90.981 10.000 10.000 20.466 70.632 60.113 30.769 10.956 30.795 10.031 100.314 10.936 10.000 10.390 20.601 10.000 70.458 80.366 20.719 30.440 40.564 10.699 30.314 20.464 70.784 20.200 10.283 60.973 10.142 90.000 10.250 50.285 70.220 60.718 10.752 60.723 20.460 10.248 150.475 70.463 120.000 40.000 10.446 80.021 40.025 80.285 10.000 50.972 10.149 70.769 10.230 20.535 20.879 20.252 40.000 10.693 10.129 20.000 110.000 40.000 10.447 20.958 10.662 80.159 20.598 30.780 130.344 20.646 20.106 40.893 20.135 20.455 40.000 10.194 30.259 10.726 30.475 20.000 60.000 10.741 10.865 10.571 20.817 30.445 30.000 10.506 20.630 20.230 110.916 20.728 10.635 11.000 10.252 60.000 20.804 10.697 30.137 90.043 60.717 20.807 40.000 10.510 120.245 10.000 60.000 10.709 30.000 20.000 10.703 20.572 30.646 10.223 120.531 30.984 10.397 20.813 10.798 10.135 130.800 10.000 10.097 20.832 30.752 90.842 80.000 10.852 10.149 90.846 100.000 10.666 50.359 20.252 80.777 10.690 2
PTv3 ScanNet2000.393 30.592 30.330 20.216 20.520 40.109 40.108 150.000 10.337 20.000 10.310 120.394 80.494 120.753 90.848 20.256 50.717 70.000 30.842 50.192 40.065 40.449 70.346 40.546 60.190 110.000 80.384 60.000 10.000 30.218 30.505 10.791 20.000 10.136 30.000 20.903 20.073 110.687 80.000 70.168 10.551 50.387 80.941 20.000 10.000 20.397 120.654 40.000 100.714 50.759 140.752 90.118 50.264 40.926 20.000 10.048 50.575 40.000 70.597 10.366 20.755 10.469 10.474 20.798 10.140 90.617 20.692 60.000 80.592 30.971 20.188 30.000 10.133 80.593 20.349 10.650 30.717 80.699 30.455 20.790 20.523 30.636 10.301 10.000 10.622 20.000 80.017 140.259 30.000 50.921 40.337 20.733 20.210 40.514 30.860 80.407 10.000 10.688 20.109 80.000 110.000 40.000 10.151 40.671 80.782 10.115 110.641 20.903 20.349 10.616 40.088 60.832 50.000 60.480 30.000 10.428 10.000 40.497 100.000 30.000 60.000 10.662 40.690 20.612 10.828 10.575 10.000 10.404 70.644 10.325 70.887 40.728 10.009 140.134 60.026 160.000 20.761 30.731 10.172 40.077 30.528 70.727 70.000 10.603 50.220 30.022 30.000 10.740 10.000 20.000 10.661 40.586 20.566 30.436 50.531 30.978 20.457 10.708 40.583 60.141 80.748 30.000 10.026 50.822 50.871 40.879 50.000 10.851 20.405 20.914 10.000 10.682 30.000 140.281 40.738 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)
L3DETR-ScanNet_2000.336 90.533 120.279 50.155 90.508 60.073 100.101 160.000 10.058 30.000 10.294 140.233 140.548 40.927 10.788 90.264 30.463 100.000 30.638 110.098 140.014 70.411 90.226 120.525 110.225 80.010 50.397 50.000 10.000 30.192 50.380 130.598 40.000 10.117 60.000 20.883 60.082 80.689 60.000 70.032 160.549 60.417 60.910 50.000 10.000 20.448 90.613 80.000 100.697 70.960 20.759 50.158 20.293 20.883 70.000 10.312 30.583 30.079 40.422 110.068 160.660 90.418 70.298 110.430 110.114 100.526 40.776 30.051 40.679 10.946 80.152 70.000 10.183 60.000 140.211 70.511 100.409 150.565 110.355 80.448 80.512 40.557 20.000 40.000 10.420 90.000 80.007 160.104 60.000 50.125 160.330 30.514 130.146 110.321 120.860 80.174 90.000 10.629 70.075 140.000 110.000 40.000 10.002 70.671 80.712 50.141 70.339 110.856 50.261 110.529 110.067 90.835 30.000 60.369 130.000 10.259 20.000 40.629 50.000 30.487 10.000 10.579 110.646 30.107 160.720 100.122 60.000 10.333 120.505 100.303 90.908 30.503 140.565 20.074 90.324 20.000 20.740 90.661 90.109 110.000 100.427 120.563 160.000 10.579 90.108 80.000 60.000 10.664 50.000 20.000 10.641 70.539 90.416 70.515 20.256 90.940 110.312 60.209 160.620 30.138 120.636 120.000 10.000 120.775 120.861 50.765 110.000 10.801 110.119 120.860 80.000 10.687 20.001 130.192 140.679 90.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet2000.333 100.558 40.269 90.124 120.448 140.080 80.272 50.000 10.000 40.000 10.342 70.515 20.524 80.713 130.789 80.158 110.384 110.000 30.806 70.125 80.000 90.496 50.332 70.498 140.227 70.024 40.474 20.000 10.003 20.071 90.487 30.000 100.000 10.110 80.000 20.876 80.013 160.703 40.000 70.076 100.473 120.355 110.906 60.000 10.000 20.476 50.706 10.000 100.672 110.835 120.748 100.015 140.223 70.860 90.000 10.000 110.572 60.000 70.509 60.313 70.662 60.398 110.396 50.411 120.276 40.527 30.711 50.000 80.076 110.946 80.166 60.000 10.022 100.160 80.183 120.493 110.699 90.637 60.403 50.330 120.406 120.526 50.024 30.000 10.392 110.000 80.016 150.000 110.196 30.915 60.112 110.557 90.197 50.352 90.877 30.000 110.000 10.592 130.103 110.000 110.067 10.000 10.089 50.735 60.625 110.130 100.568 60.836 80.271 60.534 100.043 140.799 90.001 50.445 60.000 10.000 70.024 30.661 40.000 30.262 20.000 10.591 70.517 140.373 80.788 60.021 80.000 10.455 30.517 90.320 80.823 100.200 160.001 150.150 50.100 100.000 20.736 100.668 80.103 120.052 50.662 30.720 80.000 10.602 60.112 60.002 50.000 10.637 90.000 20.000 10.621 90.569 40.398 90.412 60.234 100.949 70.363 50.492 140.495 100.251 40.665 90.000 10.001 110.805 70.833 60.794 100.000 10.821 50.314 50.843 110.000 10.560 80.245 30.262 60.713 40.370 12
PPT-SpUNet-F.T.0.332 110.556 50.270 70.123 130.519 50.091 60.349 40.000 10.000 40.000 10.339 90.383 90.498 110.833 40.807 40.241 60.584 80.000 30.755 80.124 90.000 90.608 20.330 80.530 100.314 20.000 80.374 70.000 10.000 30.197 40.459 60.000 100.000 10.117 60.000 20.876 80.095 20.682 90.000 70.086 80.518 70.433 20.930 40.000 10.000 20.563 30.542 130.077 70.715 40.858 100.756 70.008 150.171 100.874 80.000 10.039 60.550 80.000 70.545 40.256 90.657 100.453 30.351 90.449 100.213 60.392 110.611 110.000 80.037 140.946 80.138 120.000 10.000 120.063 100.308 20.537 70.796 40.673 40.323 110.392 100.400 130.509 70.000 40.000 10.649 10.000 80.023 90.000 110.000 50.914 70.002 150.506 140.163 90.359 80.872 50.000 110.000 10.623 80.112 50.001 100.000 40.000 10.021 60.753 30.565 150.150 40.579 40.806 110.267 70.616 40.042 150.783 120.000 60.374 120.000 10.000 70.000 40.620 70.000 30.000 60.000 10.572 130.634 50.350 100.792 40.000 90.000 10.376 90.535 60.378 50.855 60.672 30.074 110.000 110.185 80.000 20.727 110.660 100.076 150.000 100.432 110.646 100.000 10.594 70.006 130.000 60.000 10.658 60.000 20.000 10.661 40.549 70.300 130.291 100.045 130.942 100.304 70.600 90.572 70.135 130.695 50.000 10.008 90.793 80.942 10.899 20.000 10.816 60.181 70.897 20.000 10.679 40.223 40.264 50.691 60.345 13
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
OctFormer ScanNet200permissive0.326 120.539 90.265 110.131 110.499 70.110 30.522 10.000 10.000 40.000 10.318 110.427 60.455 140.743 110.765 120.175 100.842 40.000 30.828 60.204 20.033 60.429 80.335 60.601 30.312 30.000 80.357 90.000 10.000 30.047 110.423 80.000 100.000 10.105 90.000 20.873 110.079 90.670 120.000 70.117 40.471 130.432 30.829 100.000 10.000 20.584 20.417 160.089 60.684 100.837 110.705 150.021 130.178 90.892 50.000 10.028 70.505 100.000 70.457 90.200 130.662 60.412 90.244 140.496 70.000 160.451 80.626 90.000 80.102 90.943 110.138 120.000 10.000 120.149 90.291 30.534 90.722 70.632 80.331 100.253 140.453 80.487 90.000 40.000 10.479 60.000 80.022 100.000 110.000 50.900 90.128 100.684 30.164 80.413 40.854 100.000 110.000 10.512 150.074 150.003 90.000 40.000 10.000 80.469 140.613 120.132 90.529 70.871 40.227 150.582 80.026 160.787 100.000 60.339 140.000 10.000 70.000 40.626 60.000 30.029 40.000 10.587 90.612 70.411 70.724 90.000 90.000 10.407 50.552 50.513 20.849 70.655 40.408 30.000 110.296 30.000 20.686 140.645 130.145 70.022 80.414 130.633 110.000 10.637 10.224 20.000 60.000 10.650 70.000 20.000 10.622 80.535 100.343 110.483 30.230 110.943 90.289 90.618 80.596 50.140 90.679 70.000 10.022 60.783 100.620 130.906 10.000 10.806 100.137 100.865 50.000 10.378 110.000 140.168 160.680 80.227 15
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo0.340 60.551 80.247 120.181 50.475 120.057 160.142 130.000 10.000 40.000 10.387 50.463 50.499 100.924 20.774 110.213 70.257 120.000 30.546 140.100 120.006 80.615 10.177 160.534 80.246 50.000 80.400 40.000 10.338 10.006 150.484 40.609 30.000 10.083 120.000 20.873 110.089 60.661 130.000 70.048 140.560 40.408 70.892 70.000 10.000 20.586 10.616 70.000 100.692 80.900 60.721 110.162 10.228 60.860 90.000 10.000 110.575 40.083 30.550 30.347 40.624 120.410 100.360 80.740 20.109 110.321 140.660 70.000 80.121 70.939 120.143 80.000 10.400 10.003 120.190 110.564 50.652 100.615 100.421 30.304 130.579 10.547 40.000 40.000 10.296 130.000 80.030 70.096 70.000 50.916 50.037 120.551 100.171 70.376 70.865 60.286 30.000 10.633 60.102 120.027 60.011 30.000 10.000 80.474 130.742 30.133 80.311 120.824 90.242 120.503 130.068 80.828 60.000 60.429 70.000 10.063 60.000 40.781 10.000 30.000 60.000 10.665 30.633 60.450 50.818 20.000 90.000 10.429 40.532 70.226 120.825 90.510 110.377 40.709 20.079 120.000 20.753 60.683 50.102 130.063 40.401 150.620 130.000 10.619 20.000 140.000 60.000 10.595 140.000 20.000 10.345 130.564 50.411 80.603 10.384 70.945 80.266 100.643 70.367 130.304 10.663 100.000 10.010 70.726 140.767 80.898 30.000 10.784 120.435 10.861 70.000 10.447 90.000 140.257 70.656 110.377 11
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS0.305 130.508 130.225 130.142 100.463 130.063 140.195 90.000 10.000 40.000 10.467 30.551 10.504 90.773 60.764 130.142 130.029 160.000 30.626 120.100 120.000 90.360 110.179 140.507 130.137 140.006 60.300 130.000 10.000 30.172 80.364 140.512 50.000 10.056 130.000 20.865 130.093 50.634 160.000 70.071 120.396 140.296 150.876 80.000 10.000 20.373 130.436 150.063 90.749 20.877 90.721 110.131 40.124 130.804 140.000 10.000 110.515 90.010 60.452 100.252 100.578 130.417 80.179 160.484 80.171 70.337 130.606 120.000 80.115 80.937 130.142 90.000 10.008 110.000 140.157 150.484 120.402 160.501 140.339 90.553 70.529 20.478 100.000 40.000 10.404 100.001 70.022 100.077 90.000 50.894 110.219 60.628 80.093 140.305 130.886 10.233 50.000 10.603 100.112 50.023 70.000 40.000 10.000 80.741 50.664 70.097 130.253 130.782 120.264 90.523 120.154 10.707 150.000 60.411 80.000 10.000 70.000 40.332 150.000 30.000 60.000 10.602 60.595 90.185 130.656 150.159 50.000 10.355 110.424 140.154 140.729 130.516 100.220 90.620 30.084 110.000 20.707 130.651 110.173 30.014 90.381 160.582 140.000 10.619 20.049 120.000 60.000 10.702 40.000 20.000 10.302 150.489 140.317 120.334 80.392 60.922 120.254 120.533 130.394 120.129 160.613 140.000 10.000 120.820 60.649 120.749 120.000 10.782 130.282 60.863 60.000 10.288 150.006 90.220 110.633 130.542 3
LGroundpermissive0.272 140.485 140.184 140.106 140.476 110.077 90.218 80.000 10.000 40.000 10.547 10.295 100.540 60.746 100.745 140.058 150.112 150.005 10.658 100.077 160.000 90.322 120.178 150.512 120.190 110.199 20.277 140.000 10.000 30.173 70.399 100.000 100.000 10.039 150.000 20.858 140.085 70.676 110.002 50.103 50.498 90.323 130.703 110.000 10.000 20.296 140.549 110.216 10.702 60.768 130.718 130.028 110.092 150.786 150.000 10.000 110.453 150.022 50.251 160.252 100.572 140.348 140.321 100.514 60.063 130.279 150.552 130.000 80.019 150.932 140.132 140.000 10.000 120.000 140.156 160.457 140.623 110.518 130.265 150.358 110.381 140.395 140.000 40.000 10.127 160.012 50.051 10.000 110.000 50.886 120.014 130.437 160.179 60.244 140.826 140.000 110.000 10.599 110.136 10.085 30.000 40.000 10.000 80.565 120.612 130.143 50.207 140.566 140.232 140.446 140.127 20.708 140.000 60.384 90.000 10.000 70.000 40.402 130.000 30.059 30.000 10.525 160.566 110.229 120.659 140.000 90.000 10.265 130.446 130.147 150.720 150.597 70.066 120.000 110.187 70.000 20.726 120.467 160.134 100.000 100.413 140.629 120.000 10.363 150.055 100.022 30.000 10.626 110.000 20.000 10.323 140.479 160.154 150.117 140.028 150.901 140.243 140.415 150.295 160.143 70.610 150.000 10.000 120.777 110.397 160.324 150.000 10.778 140.179 80.702 150.000 10.274 160.404 10.233 100.622 140.398 9
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 160.455 160.171 150.079 160.418 150.059 150.186 100.000 10.000 40.000 10.335 100.250 120.316 150.766 70.697 160.142 130.170 130.003 20.553 130.112 100.097 10.201 150.186 130.476 150.081 150.000 80.216 160.000 10.000 30.001 160.314 160.000 100.000 10.055 140.000 20.832 160.094 40.659 140.002 50.076 100.310 160.293 160.664 140.000 10.000 20.175 160.634 50.130 20.552 160.686 160.700 160.076 70.110 140.770 160.000 10.000 110.430 160.000 70.319 140.166 140.542 160.327 150.205 150.332 140.052 140.375 120.444 160.000 80.012 160.930 160.203 20.000 10.000 120.046 110.175 130.413 150.592 120.471 150.299 140.152 160.340 150.247 160.000 40.000 10.225 140.058 30.037 40.000 110.207 20.862 150.014 130.548 110.033 150.233 150.816 150.000 110.000 10.542 140.123 40.121 10.019 20.000 10.000 80.463 150.454 160.045 160.128 160.557 150.235 130.441 150.063 100.484 160.000 60.308 160.000 10.000 70.000 40.318 160.000 30.000 60.000 10.545 150.543 130.164 140.734 80.000 90.000 10.215 160.371 150.198 130.743 120.205 150.062 130.000 110.079 120.000 20.683 150.547 150.142 80.000 100.441 100.579 150.000 10.464 130.098 90.041 20.000 10.590 150.000 20.000 10.373 120.494 130.174 140.105 150.001 160.895 150.222 150.537 120.307 150.180 60.625 130.000 10.000 120.591 160.609 140.398 140.000 10.766 160.014 140.638 160.000 10.377 130.004 110.206 130.609 160.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 150.463 150.154 160.102 150.381 160.084 70.134 140.000 10.000 40.000 10.386 60.141 160.279 160.737 120.703 150.014 160.164 140.000 30.663 90.092 150.000 90.224 140.291 90.531 90.056 160.000 80.242 150.000 10.000 30.013 130.331 150.000 100.000 10.035 160.001 10.858 140.059 140.650 150.000 70.056 130.353 150.299 140.670 120.000 10.000 20.284 150.484 140.071 80.594 150.720 150.710 140.027 120.068 160.813 130.000 10.005 90.492 120.164 10.274 150.111 150.571 150.307 160.293 120.307 150.150 80.163 160.531 150.002 70.545 50.932 140.093 160.000 10.000 120.002 130.159 140.368 160.581 130.440 160.228 160.406 90.282 160.294 150.000 40.000 10.189 150.060 20.036 50.000 110.000 50.897 100.000 160.525 120.025 160.205 160.771 160.000 110.000 10.593 120.108 100.044 40.000 40.000 10.000 80.282 160.589 140.094 140.169 150.466 160.227 150.419 160.125 30.757 130.002 40.334 150.000 10.000 70.000 40.357 140.000 30.000 60.000 10.582 100.513 150.337 110.612 160.000 90.000 10.250 140.352 160.136 160.724 140.655 40.280 80.000 110.046 140.000 20.606 160.559 140.159 50.102 20.445 90.655 90.000 10.310 160.117 50.000 60.000 10.581 160.026 10.000 10.265 160.483 150.084 160.097 160.044 140.865 160.142 160.588 110.351 140.272 20.596 160.000 10.003 100.622 150.720 100.096 160.000 10.771 150.016 130.772 140.000 10.302 140.194 50.214 120.621 150.197 16
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019


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




Method Infoavg ap 50%head ap 50%common ap 50%tail ap 50%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
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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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.


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 10.911 100.812 210.854 70.770 120.856 140.555 150.943 10.660 240.735 20.979 10.606 70.492 10.792 40.934 30.841 20.819 50.716 80.947 100.906 10.822 1
PTv3 ScanNet0.794 20.941 30.813 200.851 90.782 60.890 30.597 10.916 50.696 90.713 50.979 10.635 20.384 30.793 30.907 100.821 50.790 330.696 130.967 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)
DITR ScanNet0.793 30.811 390.852 20.889 10.774 90.907 10.592 20.927 30.719 10.718 30.961 170.652 10.348 120.817 10.927 50.795 90.824 20.749 10.948 90.887 70.771 11
PonderV20.785 40.978 10.800 290.833 260.788 40.853 190.545 190.910 80.713 20.705 60.979 10.596 90.390 20.769 150.832 440.821 50.792 320.730 20.975 10.897 50.785 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.
Mix3Dpermissive0.781 50.964 20.855 10.843 180.781 70.858 130.575 70.831 360.685 150.714 40.979 10.594 100.310 290.801 20.892 180.841 20.819 50.723 50.940 150.887 70.725 27
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 60.861 210.818 150.836 230.790 30.875 50.576 60.905 90.704 60.739 10.969 110.611 30.349 110.756 250.958 10.702 480.805 160.708 90.916 350.898 40.801 3
TTT-KD0.773 70.646 940.818 150.809 380.774 90.878 40.581 30.943 10.687 130.704 70.978 50.607 60.336 180.775 110.912 80.838 40.823 30.694 140.967 30.899 30.794 5
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 80.939 40.824 70.854 70.771 110.840 330.564 110.900 110.686 140.677 140.961 170.537 340.348 120.769 150.903 120.785 130.815 80.676 250.939 160.880 130.772 10
PPT-SpUNet-Joint0.766 90.932 50.794 350.829 280.751 250.854 170.540 230.903 100.630 370.672 170.963 150.565 240.357 90.788 50.900 140.737 280.802 170.685 190.950 70.887 70.780 7
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 90.925 70.808 250.849 110.786 50.846 290.566 100.876 180.690 110.674 160.960 190.576 200.226 700.753 270.904 110.777 150.815 80.722 60.923 300.877 160.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net0.764 110.924 80.819 130.840 200.757 200.853 190.580 40.848 290.709 40.643 270.958 230.587 150.295 360.753 270.884 220.758 220.815 80.725 40.927 260.867 250.743 18
OccuSeg+Semantic0.764 110.758 600.796 330.839 210.746 280.907 10.562 120.850 280.680 170.672 170.978 50.610 40.335 200.777 90.819 480.847 10.830 10.691 160.972 20.885 100.727 25
O-CNNpermissive0.762 130.924 80.823 80.844 170.770 120.852 210.577 50.847 310.711 30.640 310.958 230.592 110.217 760.762 200.888 190.758 220.813 120.726 30.932 240.868 240.744 17
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
DiffSegNet0.758 140.725 770.789 400.843 180.762 160.856 140.562 120.920 40.657 270.658 210.958 230.589 130.337 170.782 60.879 230.787 110.779 380.678 210.926 280.880 130.799 4
DTC0.757 150.843 270.820 110.847 140.791 20.862 110.511 360.870 200.707 50.652 230.954 380.604 80.279 470.760 210.942 20.734 290.766 470.701 120.884 570.874 220.736 19
OA-CNN-L_ScanNet200.756 160.783 460.826 60.858 50.776 80.837 360.548 180.896 140.649 290.675 150.962 160.586 160.335 200.771 140.802 520.770 180.787 350.691 160.936 190.880 130.761 13
PNE0.755 170.786 440.835 50.834 250.758 180.849 240.570 90.836 350.648 300.668 190.978 50.581 190.367 70.683 380.856 320.804 70.801 210.678 210.961 50.889 60.716 32
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer0.755 170.927 60.822 90.836 230.801 10.849 240.516 330.864 250.651 280.680 130.958 230.584 180.282 440.759 230.855 340.728 310.802 170.678 210.880 620.873 230.756 15
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
PointTransformerV20.752 190.742 680.809 240.872 20.758 180.860 120.552 160.891 160.610 440.687 80.960 190.559 280.304 320.766 180.926 60.767 190.797 250.644 360.942 130.876 190.722 29
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 190.906 130.793 370.802 440.689 430.825 490.556 140.867 210.681 160.602 470.960 190.555 300.365 80.779 80.859 290.747 250.795 290.717 70.917 340.856 330.764 12
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
BPNetcopyleft0.749 210.909 110.818 150.811 360.752 230.839 350.485 500.842 320.673 190.644 260.957 280.528 400.305 310.773 120.859 290.788 100.818 70.693 150.916 350.856 330.723 28
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer0.749 210.793 420.790 380.807 400.750 270.856 140.524 290.881 170.588 560.642 300.977 90.591 120.274 500.781 70.929 40.804 70.796 260.642 370.947 100.885 100.715 33
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP0.748 230.623 970.804 270.859 40.745 290.824 510.501 400.912 70.690 110.685 100.956 290.567 230.320 260.768 170.918 70.720 360.802 170.676 250.921 320.881 120.779 8
StratifiedFormerpermissive0.747 240.901 140.803 280.845 160.757 200.846 290.512 350.825 390.696 90.645 250.956 290.576 200.262 610.744 320.861 280.742 260.770 450.705 100.899 470.860 300.734 20
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
VMNetpermissive0.746 250.870 190.838 30.858 50.729 340.850 230.501 400.874 190.587 570.658 210.956 290.564 250.299 340.765 190.900 140.716 390.812 130.631 420.939 160.858 310.709 34
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 MVFusion0.746 250.771 540.819 130.848 130.702 400.865 100.397 880.899 120.699 70.664 200.948 580.588 140.330 220.746 310.851 380.764 200.796 260.704 110.935 200.866 260.728 23
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
DiffSeg3D20.745 270.725 770.814 190.837 220.751 250.831 430.514 340.896 140.674 180.684 110.960 190.564 250.303 330.773 120.820 470.713 420.798 240.690 180.923 300.875 200.757 14
Retro-FPN0.744 280.842 280.800 290.767 580.740 300.836 380.541 210.914 60.672 200.626 350.958 230.552 310.272 520.777 90.886 210.696 490.801 210.674 280.941 140.858 310.717 30
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-Net0.743 290.620 980.799 320.849 110.730 330.822 530.493 470.897 130.664 210.681 120.955 320.562 270.378 40.760 210.903 120.738 270.801 210.673 290.907 390.877 160.745 16
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 300.816 360.806 260.807 400.752 230.828 470.575 70.839 340.699 70.637 320.954 380.520 430.320 260.755 260.834 420.760 210.772 420.676 250.915 370.862 280.717 30
SAT0.742 300.860 220.765 520.819 310.769 140.848 260.533 250.829 370.663 220.631 340.955 320.586 160.274 500.753 270.896 160.729 300.760 530.666 310.921 320.855 350.733 21
LargeKernel3D0.739 320.909 110.820 110.806 420.740 300.852 210.545 190.826 380.594 550.643 270.955 320.541 330.263 600.723 360.858 310.775 170.767 460.678 210.933 220.848 400.694 39
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNetpermissive0.736 330.859 230.818 150.832 270.709 380.840 330.521 310.853 270.660 240.643 270.951 480.544 320.286 420.731 340.893 170.675 580.772 420.683 200.874 690.852 380.727 25
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
RPN0.736 330.776 500.790 380.851 90.754 220.854 170.491 490.866 230.596 540.686 90.955 320.536 350.342 150.624 530.869 250.787 110.802 170.628 430.927 260.875 200.704 36
IPCA0.731 350.890 150.837 40.864 30.726 350.873 60.530 280.824 400.489 900.647 240.978 50.609 50.336 180.624 530.733 610.758 220.776 400.570 680.949 80.877 160.728 23
PointTransformer++0.725 360.727 760.811 230.819 310.765 150.841 320.502 390.814 450.621 400.623 370.955 320.556 290.284 430.620 550.866 260.781 140.757 570.648 340.932 240.862 280.709 34
SparseConvNet0.725 360.647 930.821 100.846 150.721 360.869 70.533 250.754 610.603 500.614 390.955 320.572 220.325 240.710 370.870 240.724 340.823 30.628 430.934 210.865 270.683 42
MatchingNet0.724 380.812 380.812 210.810 370.735 320.834 400.495 460.860 260.572 640.602 470.954 380.512 450.280 460.757 240.845 400.725 330.780 370.606 530.937 180.851 390.700 38
INS-Conv-semantic0.717 390.751 630.759 550.812 350.704 390.868 80.537 240.842 320.609 460.608 430.953 420.534 370.293 370.616 560.864 270.719 380.793 300.640 380.933 220.845 440.663 48
PointMetaBase0.714 400.835 290.785 410.821 290.684 450.846 290.531 270.865 240.614 410.596 510.953 420.500 480.246 660.674 390.888 190.692 500.764 490.624 450.849 840.844 450.675 44
contrastBoundarypermissive0.705 410.769 570.775 460.809 380.687 440.820 560.439 760.812 460.661 230.591 530.945 660.515 440.171 940.633 500.856 320.720 360.796 260.668 300.889 540.847 410.689 40
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 420.774 520.800 290.793 490.760 170.847 280.471 540.802 490.463 970.634 330.968 130.491 510.271 540.726 350.910 90.706 440.815 80.551 800.878 630.833 460.570 80
RFCR0.702 430.889 160.745 660.813 340.672 480.818 600.493 470.815 440.623 380.610 410.947 600.470 600.249 650.594 590.848 390.705 450.779 380.646 350.892 520.823 520.611 63
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 Click0.701 440.825 330.796 330.723 650.716 370.832 420.433 780.816 420.634 350.609 420.969 110.418 860.344 140.559 710.833 430.715 400.808 150.560 740.902 440.847 410.680 43
JSENetpermissive0.699 450.881 180.762 530.821 290.667 490.800 720.522 300.792 520.613 420.607 440.935 860.492 500.205 810.576 640.853 360.691 520.758 550.652 330.872 720.828 490.649 52
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-Click0.693 460.743 670.794 350.655 880.684 450.822 530.497 450.719 710.622 390.617 380.977 90.447 730.339 160.750 300.664 770.703 470.790 330.596 580.946 120.855 350.647 53
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 470.732 720.772 470.786 500.677 470.866 90.517 320.848 290.509 830.626 350.952 460.536 350.225 720.545 770.704 680.689 550.810 140.564 730.903 430.854 370.729 22
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 480.884 170.754 590.795 470.647 560.818 600.422 800.802 490.612 430.604 450.945 660.462 630.189 890.563 700.853 360.726 320.765 480.632 410.904 410.821 550.606 67
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 490.704 830.741 700.754 620.656 510.829 450.501 400.741 660.609 460.548 610.950 520.522 420.371 50.633 500.756 560.715 400.771 440.623 460.861 800.814 580.658 49
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 500.866 200.748 630.819 310.645 580.794 750.450 660.802 490.587 570.604 450.945 660.464 620.201 840.554 730.840 410.723 350.732 670.602 560.907 390.822 540.603 70
KP-FCNN0.684 510.847 260.758 570.784 520.647 560.814 630.473 530.772 550.605 480.594 520.935 860.450 710.181 920.587 600.805 510.690 530.785 360.614 490.882 590.819 560.632 59
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 510.728 750.757 580.776 550.690 410.804 700.464 590.816 420.577 630.587 540.945 660.508 470.276 490.671 400.710 660.663 630.750 610.589 630.881 600.832 480.653 51
DGNet0.684 510.712 820.784 420.782 540.658 500.835 390.499 440.823 410.641 320.597 500.950 520.487 530.281 450.575 650.619 810.647 710.764 490.620 480.871 750.846 430.688 41
PointContrast_LA_SEM0.683 540.757 610.784 420.786 500.639 600.824 510.408 830.775 540.604 490.541 630.934 900.532 380.269 560.552 740.777 540.645 740.793 300.640 380.913 380.824 510.671 45
Superpoint Network0.683 540.851 250.728 740.800 460.653 530.806 680.468 560.804 470.572 640.602 470.946 630.453 700.239 690.519 820.822 450.689 550.762 520.595 600.895 500.827 500.630 60
VI-PointConv0.676 560.770 560.754 590.783 530.621 640.814 630.552 160.758 590.571 660.557 590.954 380.529 390.268 580.530 800.682 720.675 580.719 700.603 550.888 550.833 460.665 47
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 570.789 430.748 630.763 600.635 620.814 630.407 850.747 630.581 610.573 560.950 520.484 540.271 540.607 570.754 570.649 680.774 410.596 580.883 580.823 520.606 67
SALANet0.670 580.816 360.770 500.768 570.652 540.807 670.451 630.747 630.659 260.545 620.924 960.473 590.149 1040.571 670.811 500.635 770.746 620.623 460.892 520.794 710.570 80
O3DSeg0.668 590.822 340.771 490.496 1080.651 550.833 410.541 210.761 580.555 720.611 400.966 140.489 520.370 60.388 1020.580 840.776 160.751 590.570 680.956 60.817 570.646 54
PointASNLpermissive0.666 600.703 840.781 440.751 640.655 520.830 440.471 540.769 560.474 930.537 650.951 480.475 580.279 470.635 480.698 710.675 580.751 590.553 790.816 910.806 620.703 37
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 600.781 470.759 550.699 730.644 590.822 530.475 520.779 530.564 690.504 790.953 420.428 800.203 830.586 620.754 570.661 640.753 580.588 640.902 440.813 600.642 55
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 620.746 650.708 770.722 660.638 610.820 560.451 630.566 990.599 520.541 630.950 520.510 460.313 280.648 450.819 480.616 820.682 850.590 620.869 760.810 610.656 50
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 630.778 480.702 800.806 420.619 650.813 660.468 560.693 790.494 860.524 710.941 780.449 720.298 350.510 840.821 460.675 580.727 690.568 710.826 890.803 650.637 57
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN0.658 630.558 1050.751 610.655 880.690 410.722 970.453 620.867 210.579 620.576 550.893 1080.523 410.293 370.733 330.571 860.692 500.659 920.606 530.875 660.804 640.668 46
HPGCNN0.656 650.698 860.743 680.650 900.564 820.820 560.505 380.758 590.631 360.479 830.945 660.480 560.226 700.572 660.774 550.690 530.735 650.614 490.853 830.776 860.597 73
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 660.752 620.734 720.664 860.583 770.815 620.399 870.754 610.639 330.535 670.942 760.470 600.309 300.665 410.539 880.650 670.708 750.635 400.857 820.793 730.642 55
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 670.778 480.731 730.699 730.577 780.829 450.446 680.736 670.477 920.523 730.945 660.454 670.269 560.484 920.749 600.618 800.738 630.599 570.827 880.792 760.621 62
PointConv-SFPN0.641 680.776 500.703 790.721 670.557 850.826 480.451 630.672 840.563 700.483 820.943 750.425 830.162 990.644 460.726 620.659 650.709 740.572 670.875 660.786 810.559 86
MVPNetpermissive0.641 680.831 300.715 750.671 830.590 730.781 810.394 890.679 810.642 310.553 600.937 830.462 630.256 620.649 440.406 1020.626 780.691 820.666 310.877 640.792 760.608 66
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 700.717 810.701 810.692 760.576 790.801 710.467 580.716 720.563 700.459 890.953 420.429 790.169 960.581 630.854 350.605 830.710 720.550 810.894 510.793 730.575 78
FPConvpermissive0.639 710.785 450.760 540.713 710.603 680.798 730.392 900.534 1040.603 500.524 710.948 580.457 650.250 640.538 780.723 640.598 870.696 800.614 490.872 720.799 660.567 83
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 720.797 410.769 510.641 960.590 730.820 560.461 600.537 1030.637 340.536 660.947 600.388 930.206 800.656 420.668 750.647 710.732 670.585 650.868 770.793 730.473 106
PointSPNet0.637 730.734 710.692 880.714 700.576 790.797 740.446 680.743 650.598 530.437 940.942 760.403 890.150 1030.626 520.800 530.649 680.697 790.557 770.846 850.777 850.563 84
SConv0.636 740.830 310.697 840.752 630.572 810.780 830.445 700.716 720.529 760.530 680.951 480.446 740.170 950.507 870.666 760.636 760.682 850.541 870.886 560.799 660.594 74
Supervoxel-CNN0.635 750.656 910.711 760.719 680.613 660.757 920.444 730.765 570.534 750.566 570.928 940.478 570.272 520.636 470.531 900.664 620.645 960.508 940.864 790.792 760.611 63
joint point-basedpermissive0.634 760.614 990.778 450.667 850.633 630.825 490.420 810.804 470.467 950.561 580.951 480.494 490.291 390.566 680.458 970.579 930.764 490.559 760.838 860.814 580.598 72
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 770.731 730.688 910.675 800.591 720.784 800.444 730.565 1000.610 440.492 800.949 560.456 660.254 630.587 600.706 670.599 860.665 910.612 520.868 770.791 790.579 77
3DSM_DMMF0.631 780.626 960.745 660.801 450.607 670.751 930.506 370.729 700.565 680.491 810.866 1110.434 750.197 870.595 580.630 800.709 430.705 770.560 740.875 660.740 960.491 101
PointNet2-SFPN0.631 780.771 540.692 880.672 810.524 900.837 360.440 750.706 770.538 740.446 910.944 720.421 850.219 750.552 740.751 590.591 890.737 640.543 860.901 460.768 880.557 87
APCF-Net0.631 780.742 680.687 930.672 810.557 850.792 780.408 830.665 850.545 730.508 760.952 460.428 800.186 900.634 490.702 690.620 790.706 760.555 780.873 700.798 680.581 76
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 810.604 1010.741 700.766 590.590 730.747 940.501 400.734 680.503 850.527 690.919 1000.454 670.323 250.550 760.420 1010.678 570.688 830.544 840.896 490.795 700.627 61
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 820.800 400.625 1040.719 680.545 870.806 680.445 700.597 930.448 1000.519 740.938 820.481 550.328 230.489 910.499 950.657 660.759 540.592 610.881 600.797 690.634 58
SegGroup_sempermissive0.627 830.818 350.747 650.701 720.602 690.764 890.385 940.629 900.490 880.508 760.931 930.409 880.201 840.564 690.725 630.618 800.692 810.539 880.873 700.794 710.548 90
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 840.830 310.694 860.757 610.563 830.772 870.448 670.647 880.520 790.509 750.949 560.431 780.191 880.496 890.614 820.647 710.672 890.535 900.876 650.783 820.571 79
dtc_net0.625 840.703 840.751 610.794 480.535 880.848 260.480 510.676 830.528 770.469 860.944 720.454 670.004 1170.464 940.636 790.704 460.758 550.548 830.924 290.787 800.492 100
HPEIN0.618 860.729 740.668 940.647 920.597 710.766 880.414 820.680 800.520 790.525 700.946 630.432 760.215 770.493 900.599 830.638 750.617 1010.570 680.897 480.806 620.605 69
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-GCNpermissive0.610 870.858 240.772 470.489 1090.532 890.792 780.404 860.643 890.570 670.507 780.935 860.414 870.046 1140.510 840.702 690.602 850.705 770.549 820.859 810.773 870.534 93
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 880.760 590.667 950.649 910.521 910.793 760.457 610.648 870.528 770.434 960.947 600.401 900.153 1020.454 950.721 650.648 700.717 710.536 890.904 410.765 890.485 102
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 890.634 950.743 680.697 750.601 700.781 810.437 770.585 960.493 870.446 910.933 910.394 910.011 1160.654 430.661 780.603 840.733 660.526 910.832 870.761 910.480 103
LAP-D0.594 900.720 790.692 880.637 970.456 1010.773 860.391 920.730 690.587 570.445 930.940 800.381 940.288 400.434 980.453 990.591 890.649 940.581 660.777 950.749 950.610 65
DPC0.592 910.720 790.700 820.602 1010.480 970.762 910.380 950.713 750.585 600.437 940.940 800.369 960.288 400.434 980.509 940.590 910.639 990.567 720.772 970.755 930.592 75
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 920.766 580.659 990.683 780.470 1000.740 960.387 930.620 920.490 880.476 840.922 980.355 990.245 670.511 830.511 930.571 940.643 970.493 980.872 720.762 900.600 71
ROSMRF0.580 930.772 530.707 780.681 790.563 830.764 890.362 970.515 1050.465 960.465 880.936 850.427 820.207 790.438 960.577 850.536 970.675 880.486 990.723 1030.779 830.524 96
SD-DETR0.576 940.746 650.609 1080.445 1130.517 920.643 1080.366 960.714 740.456 980.468 870.870 1100.432 760.264 590.558 720.674 730.586 920.688 830.482 1000.739 1010.733 980.537 92
SQN_0.1%0.569 950.676 880.696 850.657 870.497 930.779 840.424 790.548 1010.515 810.376 1010.902 1070.422 840.357 90.379 1030.456 980.596 880.659 920.544 840.685 1060.665 1090.556 88
TextureNetpermissive0.566 960.672 900.664 960.671 830.494 950.719 980.445 700.678 820.411 1060.396 990.935 860.356 980.225 720.412 1000.535 890.565 950.636 1000.464 1020.794 940.680 1060.568 82
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
DVVNet0.562 970.648 920.700 820.770 560.586 760.687 1020.333 1010.650 860.514 820.475 850.906 1040.359 970.223 740.340 1050.442 1000.422 1080.668 900.501 950.708 1040.779 830.534 93
Pointnet++ & Featurepermissive0.557 980.735 700.661 980.686 770.491 960.744 950.392 900.539 1020.451 990.375 1020.946 630.376 950.205 810.403 1010.356 1050.553 960.643 970.497 960.824 900.756 920.515 97
GMLPs0.538 990.495 1100.693 870.647 920.471 990.793 760.300 1040.477 1060.505 840.358 1040.903 1060.327 1020.081 1110.472 930.529 910.448 1060.710 720.509 920.746 990.737 970.554 89
PanopticFusion-label0.529 1000.491 1110.688 910.604 1000.386 1060.632 1090.225 1140.705 780.434 1030.293 1100.815 1120.348 1000.241 680.499 880.669 740.507 990.649 940.442 1080.796 930.602 1130.561 85
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 1010.676 880.591 1110.609 980.442 1020.774 850.335 1000.597 930.422 1050.357 1050.932 920.341 1010.094 1100.298 1070.528 920.473 1040.676 870.495 970.602 1120.721 1010.349 113
Online SegFusion0.515 1020.607 1000.644 1020.579 1030.434 1030.630 1100.353 980.628 910.440 1010.410 970.762 1160.307 1040.167 970.520 810.403 1030.516 980.565 1040.447 1060.678 1070.701 1030.514 98
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 1030.558 1050.608 1090.424 1150.478 980.690 1010.246 1100.586 950.468 940.450 900.911 1020.394 910.160 1000.438 960.212 1120.432 1070.541 1100.475 1010.742 1000.727 990.477 104
PCNN0.498 1040.559 1040.644 1020.560 1050.420 1050.711 1000.229 1120.414 1070.436 1020.352 1060.941 780.324 1030.155 1010.238 1120.387 1040.493 1000.529 1110.509 920.813 920.751 940.504 99
Weakly-Openseg v30.489 1050.749 640.664 960.646 940.496 940.559 1140.122 1170.577 970.257 1170.364 1030.805 1130.198 1150.096 1090.510 840.496 960.361 1120.563 1050.359 1150.777 950.644 1100.532 95
3DMV0.484 1060.484 1120.538 1130.643 950.424 1040.606 1130.310 1020.574 980.433 1040.378 1000.796 1140.301 1050.214 780.537 790.208 1130.472 1050.507 1140.413 1110.693 1050.602 1130.539 91
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1070.577 1030.611 1070.356 1170.321 1140.715 990.299 1060.376 1110.328 1130.319 1080.944 720.285 1070.164 980.216 1150.229 1100.484 1020.545 1090.456 1040.755 980.709 1020.475 105
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1080.679 870.604 1100.578 1040.380 1070.682 1030.291 1070.106 1170.483 910.258 1150.920 990.258 1110.025 1150.231 1140.325 1060.480 1030.560 1070.463 1030.725 1020.666 1080.231 117
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1090.474 1130.623 1050.463 1110.366 1090.651 1060.310 1020.389 1100.349 1110.330 1070.937 830.271 1090.126 1060.285 1080.224 1110.350 1140.577 1030.445 1070.625 1100.723 1000.394 109
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 1100.548 1070.548 1120.597 1020.363 1100.628 1110.300 1040.292 1120.374 1080.307 1090.881 1090.268 1100.186 900.238 1120.204 1140.407 1090.506 1150.449 1050.667 1080.620 1120.462 107
SurfaceConvPF0.442 1100.505 1090.622 1060.380 1160.342 1120.654 1050.227 1130.397 1090.367 1090.276 1120.924 960.240 1120.198 860.359 1040.262 1080.366 1100.581 1020.435 1090.640 1090.668 1070.398 108
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1120.437 1150.646 1010.474 1100.369 1080.645 1070.353 980.258 1140.282 1150.279 1110.918 1010.298 1060.147 1050.283 1090.294 1070.487 1010.562 1060.427 1100.619 1110.633 1110.352 112
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1130.525 1080.647 1000.522 1060.324 1130.488 1170.077 1180.712 760.353 1100.401 980.636 1180.281 1080.176 930.340 1050.565 870.175 1180.551 1080.398 1120.370 1180.602 1130.361 111
SPLAT Netcopyleft0.393 1140.472 1140.511 1140.606 990.311 1150.656 1040.245 1110.405 1080.328 1130.197 1160.927 950.227 1140.000 1190.001 1190.249 1090.271 1170.510 1120.383 1140.593 1130.699 1040.267 115
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 1150.297 1170.491 1150.432 1140.358 1110.612 1120.274 1080.116 1160.411 1060.265 1130.904 1050.229 1130.079 1120.250 1100.185 1150.320 1150.510 1120.385 1130.548 1140.597 1160.394 109
PointNet++permissive0.339 1160.584 1020.478 1160.458 1120.256 1170.360 1180.250 1090.247 1150.278 1160.261 1140.677 1170.183 1160.117 1070.212 1160.145 1170.364 1110.346 1180.232 1180.548 1140.523 1170.252 116
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 1170.353 1160.290 1180.278 1180.166 1180.553 1150.169 1160.286 1130.147 1180.148 1180.908 1030.182 1170.064 1130.023 1180.018 1190.354 1130.363 1160.345 1160.546 1160.685 1050.278 114
ScanNetpermissive0.306 1180.203 1180.366 1170.501 1070.311 1150.524 1160.211 1150.002 1190.342 1120.189 1170.786 1150.145 1180.102 1080.245 1110.152 1160.318 1160.348 1170.300 1170.460 1170.437 1180.182 118
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 1190.000 1190.041 1190.172 1190.030 1190.062 1190.001 1190.035 1180.004 1190.051 1190.143 1190.019 1190.003 1180.041 1170.050 1180.003 1190.054 1190.018 1190.005 1190.264 1190.082 119


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
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
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 10.761 460.473 560.477 510.795 380.066 500.529 520.658 220.460 380.461 460.380 480.331 620.859 440.401 550.692 530.653 331.000 10.348 54
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 490.528 660.708 570.626 440.580 340.745 480.063 510.627 410.240 540.400 540.497 420.464 360.515 491.000 10.475 470.745 390.571 461.000 10.429 48
NeuralBF0.555 500.667 560.896 150.843 170.517 450.751 460.029 550.519 530.414 420.439 420.465 440.000 720.484 510.857 450.287 600.693 520.651 351.000 10.485 41
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
MTML0.549 511.000 10.807 320.588 520.327 580.647 540.004 620.815 70.180 570.418 490.364 580.182 560.445 541.000 10.442 500.688 540.571 471.000 10.396 50
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance0.539 521.000 10.621 610.300 590.530 420.698 500.127 430.533 510.222 550.430 470.400 530.365 490.574 460.938 410.472 480.659 560.543 540.944 530.347 55
One_Thing_One_Clickpermissive0.529 530.667 560.718 530.777 270.399 540.683 510.000 650.669 320.138 600.391 550.374 570.539 300.360 610.641 600.556 410.774 320.593 420.997 370.251 60
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN0.515 541.000 10.538 660.282 600.468 520.790 390.173 340.345 620.429 390.413 510.484 430.176 570.595 440.591 610.522 440.668 550.476 590.986 510.327 56
Occipital-SCS0.512 551.000 10.716 540.509 550.506 470.611 570.092 470.602 460.177 580.346 580.383 550.165 580.442 550.850 520.386 570.618 600.543 550.889 580.389 51
3D-BoNet0.488 561.000 10.672 600.590 510.301 600.484 670.098 460.620 430.306 510.341 590.259 620.125 600.434 570.796 550.402 540.499 660.513 570.909 570.439 46
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
PanopticFusion-inst0.478 570.667 560.712 560.595 500.259 630.550 630.000 650.613 440.175 590.250 640.434 470.437 400.411 590.857 450.485 460.591 630.267 690.944 530.359 53
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS0.470 580.667 560.685 590.677 420.372 560.562 610.000 650.482 570.244 530.316 610.298 590.052 670.442 560.857 450.267 610.702 480.559 501.000 10.287 58
SALoss-ResNet0.459 591.000 10.737 520.159 700.259 620.587 590.138 410.475 580.217 560.416 500.408 520.128 590.315 630.714 560.411 530.536 650.590 430.873 620.304 57
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)
MASCpermissive0.447 600.528 660.555 640.381 570.382 550.633 550.002 630.509 550.260 520.361 570.432 490.327 520.451 530.571 620.367 580.639 580.386 600.980 520.276 59
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 610.667 560.773 430.185 670.317 590.656 530.000 650.407 610.134 610.381 560.267 610.217 550.476 520.714 560.452 490.629 590.514 561.000 10.222 63
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 621.000 10.432 690.245 620.190 640.577 600.013 590.263 640.033 670.320 600.240 630.075 630.422 580.857 450.117 670.699 490.271 680.883 600.235 62
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 630.667 560.542 650.264 610.157 670.550 620.000 650.205 670.009 690.270 630.218 640.075 630.500 500.688 590.007 730.698 500.301 650.459 700.200 64
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 640.667 560.715 550.233 630.189 650.479 680.008 600.218 650.067 660.201 660.173 650.107 610.123 680.438 640.150 640.615 610.355 610.916 560.093 72
R-PointNet0.306 650.500 680.405 700.311 580.348 570.589 580.054 520.068 700.126 620.283 620.290 600.028 680.219 660.214 670.331 590.396 700.275 660.821 640.245 61
Region-18class0.284 660.250 720.751 510.228 650.270 610.521 640.000 650.468 590.008 710.205 650.127 660.000 720.068 700.070 710.262 630.652 570.323 630.740 660.173 65
SemRegionNet-20cls0.250 670.333 690.613 620.229 640.163 660.493 650.000 650.304 630.107 630.147 690.100 680.052 660.231 640.119 690.039 690.445 680.325 620.654 670.141 68
3D-BEVIS0.248 680.667 560.566 630.076 710.035 730.394 710.027 570.035 720.098 640.099 710.030 720.025 690.098 690.375 660.126 660.604 620.181 710.854 630.171 66
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp0.248 680.667 560.437 680.188 660.153 680.491 660.000 650.208 660.094 650.153 680.099 690.057 650.217 670.119 690.039 690.466 670.302 640.640 680.140 69
Sem_Recon_ins0.227 700.764 550.486 670.069 720.098 700.426 700.017 580.067 710.015 680.172 670.100 670.096 620.054 720.183 680.135 650.366 710.260 700.614 690.168 67
ASIS0.199 710.333 690.253 720.167 690.140 690.438 690.000 650.177 680.008 700.121 700.069 700.004 710.231 650.429 650.036 710.445 690.273 670.333 720.119 71
Sgpn_scannet0.143 720.208 730.390 710.169 680.065 710.275 720.029 560.069 690.000 720.087 720.043 710.014 700.027 730.000 720.112 680.351 720.168 720.438 710.138 70
MaskRCNN 2d->3d Proj0.058 730.333 690.002 730.000 730.053 720.002 730.002 640.021 730.000 720.045 730.024 730.238 540.065 710.000 720.014 720.107 730.020 730.110 730.006 73


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 10.723 10.714 10.954 20.454 10.509 10.773 10.895 10.756 10.820 10.653 10.935 10.891 10.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2Dcopyleft0.670 20.822 30.795 30.836 20.659 30.481 20.451 130.769 40.656 30.567 40.931 30.395 60.390 50.700 40.534 40.689 100.770 20.574 30.865 90.831 30.675 5
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MVF-GNN(2D)0.636 30.606 140.794 40.434 160.688 10.337 80.464 120.798 30.632 50.589 30.908 80.420 20.329 120.743 20.594 20.738 20.676 50.527 40.906 20.818 60.715 3
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 230.648 40.463 30.549 20.742 70.676 20.628 20.961 10.420 20.379 60.684 80.381 180.732 30.723 30.599 20.827 160.851 20.634 7
CMX0.613 50.681 80.725 120.502 120.634 60.297 180.478 100.830 20.651 40.537 70.924 40.375 70.315 140.686 70.451 140.714 50.543 210.504 60.894 70.823 50.688 4
DMMF_3d0.605 60.651 90.744 100.782 30.637 50.387 40.536 30.732 80.590 70.540 60.856 210.359 110.306 150.596 140.539 30.627 200.706 40.497 80.785 210.757 190.476 22
EMSANet0.600 70.716 40.746 90.395 180.614 90.382 50.523 40.713 110.571 110.503 100.922 60.404 50.397 40.655 90.400 160.626 210.663 60.469 130.900 40.827 40.577 14
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
MCA-Net0.595 80.533 200.756 80.746 40.590 100.334 100.506 70.670 150.587 80.500 120.905 100.366 100.352 90.601 130.506 80.669 160.648 90.501 70.839 150.769 150.516 21
RFBNet0.592 90.616 110.758 70.659 50.581 110.330 110.469 110.655 180.543 140.524 80.924 40.355 130.336 110.572 170.479 100.671 140.648 90.480 100.814 190.814 70.614 10
FAN_NV_RVC0.586 100.510 210.764 60.079 260.620 80.330 110.494 80.753 50.573 90.556 50.884 160.405 40.303 160.718 30.452 130.672 130.658 70.509 50.898 50.813 80.727 2
DCRedNet0.583 110.682 70.723 130.542 110.510 200.310 150.451 130.668 160.549 130.520 90.920 70.375 70.446 20.528 200.417 150.670 150.577 180.478 110.862 100.806 90.628 9
MIX6D_RVC0.582 120.695 50.687 170.225 210.632 70.328 130.550 10.748 60.623 60.494 150.890 140.350 150.254 230.688 60.454 120.716 40.597 170.489 90.881 80.768 160.575 15
SSMAcopyleft0.577 130.695 50.716 150.439 140.563 140.314 140.444 150.719 90.551 120.503 100.887 150.346 160.348 100.603 120.353 200.709 60.600 150.457 140.901 30.786 110.599 13
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DMMF0.567 140.623 100.767 50.238 200.571 130.347 60.413 190.719 90.472 200.418 220.895 130.357 120.260 220.696 50.523 70.666 170.642 110.437 180.895 60.793 100.603 12
UNIV_CNP_RVC_UE0.566 150.569 190.686 190.435 150.524 170.294 190.421 180.712 120.543 140.463 170.872 170.320 170.363 80.611 110.477 110.686 110.627 120.443 170.862 100.775 140.639 6
EMSAFormer0.564 160.581 160.736 110.564 100.546 160.219 230.517 50.675 140.486 190.427 210.904 110.352 140.320 130.589 150.528 50.708 70.464 240.413 220.847 140.786 110.611 11
SN_RN152pyrx8_RVCcopyleft0.546 170.572 170.663 210.638 70.518 180.298 170.366 240.633 210.510 170.446 190.864 190.296 200.267 190.542 190.346 210.704 80.575 190.431 190.853 130.766 170.630 8
UDSSEG_RVC0.545 180.610 130.661 220.588 80.556 150.268 210.482 90.642 200.572 100.475 160.836 230.312 180.367 70.630 100.189 230.639 190.495 230.452 150.826 170.756 200.541 17
segfomer with 6d0.542 190.594 150.687 170.146 240.579 120.308 160.515 60.703 130.472 200.498 130.868 180.369 90.282 170.589 150.390 170.701 90.556 200.416 210.860 120.759 180.539 19
FuseNetpermissive0.535 200.570 180.681 200.182 220.512 190.290 200.431 160.659 170.504 180.495 140.903 120.308 190.428 30.523 210.365 190.676 120.621 140.470 120.762 220.779 130.541 17
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 210.613 120.722 140.418 170.358 260.337 80.370 230.479 240.443 220.368 240.907 90.207 230.213 250.464 240.525 60.618 220.657 80.450 160.788 200.721 230.408 25
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 220.481 240.612 230.579 90.456 220.343 70.384 210.623 220.525 160.381 230.845 220.254 220.264 210.557 180.182 240.581 240.598 160.429 200.760 230.661 250.446 24
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 230.505 220.709 160.092 250.427 230.241 220.411 200.654 190.385 260.457 180.861 200.053 260.279 180.503 220.481 90.645 180.626 130.365 240.748 240.725 220.529 20
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 240.490 230.581 240.289 190.507 210.067 260.379 220.610 230.417 240.435 200.822 250.278 210.267 190.503 220.228 220.616 230.533 220.375 230.820 180.729 210.560 16
Enet (reimpl)0.376 250.264 260.452 260.452 130.365 240.181 240.143 260.456 250.409 250.346 250.769 260.164 240.218 240.359 250.123 260.403 260.381 260.313 260.571 250.685 240.472 23
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)permissive0.330 260.293 250.521 250.657 60.361 250.161 250.250 250.004 260.440 230.183 260.836 230.125 250.060 260.319 260.132 250.417 250.412 250.344 250.541 260.427 260.109 26
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17


This table lists the benchmark results for the 2D semantic instance scenario.




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
EMSANet (Instance)0.241 10.401 10.439 10.085 10.242 10.220 10.081 10.289 20.117 20.121 10.182 10.126 10.346 10.181 20.181 20.358 10.156 10.675 20.131 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UniDet_RVC0.205 20.381 20.323 30.037 30.226 30.177 30.063 20.277 30.120 10.067 30.131 30.074 30.317 20.080 30.235 10.289 30.141 30.678 10.080 3
FKNet0.204 30.334 30.358 20.038 20.234 20.184 20.025 30.318 10.042 40.088 20.141 20.053 40.300 30.207 10.171 30.292 20.149 20.636 30.109 2
MaskRCNN_ScanNetpermissive0.119 40.129 40.212 40.002 40.112 40.148 40.014 40.205 40.044 30.066 40.078 40.095 20.142 40.030 40.128 40.139 40.080 40.459 40.057 4
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17


This table lists the benchmark results for the scene type classification scenario.




Method Infoavg recallapartmentbathroombedroom / hotelbookstore / libraryconference roomcopy/mail roomhallwaykitchenlaundry roomliving room / loungemiscofficestorage / basement / garage
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LAST-PCL-type0.780 10.250 31.000 11.000 11.000 11.000 11.000 10.500 21.000 10.500 20.889 10.000 21.000 11.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, and Jian Zhang: Language-Assisted 3D Scene Understanding. arxiv23.12
multi-taskpermissive0.700 20.500 11.000 10.882 30.500 31.000 11.000 10.500 21.000 11.000 10.778 20.000 20.938 20.000 3
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE0.691 30.500 10.938 30.824 41.000 11.000 10.500 31.000 10.857 30.500 20.556 40.000 20.812 30.500 2
SE-ResNeXt-SSMA0.498 40.000 50.812 40.941 20.500 30.500 40.500 30.500 20.429 50.500 20.667 30.500 10.625 40.000 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 50.250 30.812 40.529 50.500 30.500 40.000 50.500 20.571 40.000 50.556 40.000 20.375 50.000 3