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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PonderV2 ScanNet2000.346 20.552 40.270 40.175 30.497 50.070 80.239 40.000 10.000 30.000 10.232 110.412 50.584 10.842 30.804 30.212 50.540 40.000 30.433 110.106 60.000 60.590 30.290 60.548 20.243 40.000 50.356 70.000 10.000 30.062 70.398 70.441 50.000 10.104 60.000 20.888 20.076 80.682 40.030 10.094 40.491 60.351 70.869 70.000 10.063 10.403 60.700 20.000 70.660 90.881 30.761 10.050 60.186 50.852 70.000 10.007 50.570 50.100 20.565 20.326 30.641 60.431 30.290 80.621 30.259 20.408 50.622 60.125 10.082 70.950 20.179 30.000 10.263 20.424 20.193 50.558 30.880 10.545 70.375 40.727 20.445 60.499 60.000 30.000 10.475 40.002 40.034 40.083 40.000 30.924 10.290 30.636 30.115 80.400 30.874 30.186 40.000 10.611 50.128 20.113 20.000 40.000 10.000 50.584 60.636 50.103 80.385 50.843 40.283 20.603 30.080 50.825 40.000 30.377 60.000 10.000 40.000 20.457 70.000 10.000 50.000 10.574 80.608 60.481 20.792 30.394 20.000 10.357 60.503 70.261 60.817 70.504 80.304 40.472 30.115 50.000 10.750 30.677 30.202 10.000 70.509 30.729 10.000 10.519 80.000 100.000 50.000 10.620 80.000 20.000 10.660 30.560 40.486 20.384 60.346 40.952 20.247 80.667 20.436 60.269 30.691 30.000 10.010 30.787 50.889 20.880 40.000 10.810 40.336 30.860 60.000 10.606 40.009 50.248 50.681 40.392 6
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.520 10.109 20.108 100.000 10.337 10.000 10.310 90.394 60.494 80.753 70.848 10.256 20.717 20.000 30.842 10.192 20.065 20.449 50.346 10.546 30.190 70.000 50.384 40.000 10.000 30.218 10.505 10.791 10.000 10.136 10.000 20.903 10.073 90.687 30.000 40.168 10.551 20.387 50.941 10.000 10.000 20.397 70.654 30.000 70.714 30.759 90.752 40.118 40.264 20.926 10.000 10.048 20.575 20.000 70.597 10.366 10.755 10.469 10.474 10.798 10.140 60.617 10.692 30.000 40.592 20.971 10.188 20.000 10.133 40.593 10.349 10.650 10.717 40.699 10.455 10.790 10.523 30.636 10.301 10.000 10.622 20.000 60.017 90.259 10.000 30.921 20.337 10.733 10.210 10.514 10.860 60.407 10.000 10.688 10.109 60.000 90.000 40.000 10.151 10.671 40.782 10.115 70.641 10.903 10.349 10.616 10.088 40.832 20.000 30.480 10.000 10.428 10.000 20.497 60.000 10.000 50.000 10.662 20.690 10.612 10.828 10.575 10.000 10.404 40.644 10.325 30.887 20.728 10.009 100.134 50.026 110.000 10.761 10.731 10.172 30.077 20.528 20.727 20.000 10.603 40.220 20.022 20.000 10.740 10.000 20.000 10.661 10.586 10.566 10.436 40.531 10.978 10.457 10.708 10.583 30.141 70.748 10.000 10.026 10.822 10.871 30.879 50.000 10.851 10.405 20.914 10.000 10.682 20.000 90.281 10.738 10.463 4
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
L3DETR-ScanNet_2000.336 40.533 70.279 20.155 40.508 30.073 70.101 110.000 10.058 20.000 10.294 100.233 100.548 20.927 10.788 50.264 10.463 50.000 30.638 70.098 90.014 40.411 70.226 70.525 70.225 60.010 30.397 30.000 10.000 30.192 30.380 80.598 30.000 10.117 20.000 20.883 30.082 60.689 20.000 40.032 110.549 30.417 30.910 30.000 10.000 20.448 50.613 60.000 70.697 50.960 10.759 20.158 20.293 10.883 30.000 10.312 10.583 10.079 40.422 80.068 110.660 40.418 40.298 60.430 80.114 70.526 30.776 10.051 20.679 10.946 30.152 50.000 10.183 30.000 90.211 40.511 60.409 100.565 60.355 50.448 40.512 40.557 20.000 30.000 10.420 50.000 60.007 110.104 20.000 30.125 110.330 20.514 90.146 70.321 70.860 60.174 50.000 10.629 30.075 100.000 90.000 40.000 10.002 40.671 40.712 30.141 30.339 60.856 30.261 60.529 60.067 70.835 10.000 30.369 80.000 10.259 20.000 20.629 30.000 10.487 10.000 10.579 70.646 20.107 110.720 80.122 40.000 10.333 80.505 60.303 50.908 10.503 90.565 10.074 60.324 10.000 10.740 40.661 50.109 80.000 70.427 70.563 110.000 10.579 70.108 50.000 50.000 10.664 30.000 20.000 10.641 40.539 60.416 30.515 20.256 50.940 70.312 30.209 110.620 10.138 90.636 70.000 10.000 80.775 80.861 40.765 70.000 10.801 60.119 90.860 60.000 10.687 10.001 80.192 100.679 60.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet2000.333 50.558 20.269 50.124 70.448 90.080 50.272 30.000 10.000 30.000 10.342 50.515 20.524 40.713 110.789 40.158 70.384 60.000 30.806 30.125 30.000 60.496 40.332 30.498 100.227 50.024 20.474 10.000 10.003 20.071 60.487 20.000 60.000 10.110 40.000 20.876 40.013 110.703 10.000 40.076 60.473 70.355 60.906 40.000 10.000 20.476 40.706 10.000 70.672 80.835 70.748 50.015 100.223 40.860 50.000 10.000 70.572 40.000 70.509 50.313 40.662 20.398 80.396 20.411 90.276 10.527 20.711 20.000 40.076 80.946 30.166 40.000 10.022 50.160 30.183 70.493 70.699 50.637 30.403 30.330 80.406 70.526 40.024 20.000 10.392 70.000 60.016 100.000 60.196 20.915 40.112 60.557 50.197 20.352 60.877 20.000 60.000 10.592 90.103 80.000 90.067 10.000 10.089 20.735 30.625 60.130 60.568 30.836 50.271 30.534 50.043 90.799 50.001 20.445 20.000 10.000 40.024 10.661 20.000 10.262 20.000 10.591 40.517 100.373 50.788 50.021 50.000 10.455 10.517 50.320 40.823 60.200 110.001 110.150 40.100 60.000 10.736 50.668 40.103 90.052 40.662 10.720 30.000 10.602 50.112 40.002 40.000 10.637 60.000 20.000 10.621 60.569 20.398 50.412 50.234 60.949 30.363 20.492 90.495 50.251 40.665 50.000 10.001 70.805 30.833 50.794 60.000 10.821 20.314 40.843 80.000 10.560 50.245 20.262 30.713 20.370 8
PPT-SpUNet-F.T.0.332 60.556 30.270 30.123 80.519 20.091 30.349 20.000 10.000 30.000 10.339 60.383 70.498 70.833 40.807 20.241 30.584 30.000 30.755 40.124 40.000 60.608 20.330 40.530 60.314 10.000 50.374 50.000 10.000 30.197 20.459 40.000 60.000 10.117 20.000 20.876 40.095 10.682 40.000 40.086 50.518 40.433 10.930 20.000 10.000 20.563 30.542 80.077 40.715 20.858 50.756 30.008 110.171 70.874 40.000 10.039 30.550 60.000 70.545 40.256 50.657 50.453 20.351 40.449 70.213 30.392 60.611 70.000 40.037 90.946 30.138 80.000 10.000 70.063 50.308 20.537 40.796 20.673 20.323 80.392 60.400 80.509 50.000 30.000 10.649 10.000 60.023 60.000 60.000 30.914 50.002 100.506 100.163 60.359 50.872 40.000 60.000 10.623 40.112 40.001 80.000 40.000 10.021 30.753 10.565 100.150 10.579 20.806 70.267 40.616 10.042 100.783 70.000 30.374 70.000 10.000 40.000 20.620 50.000 10.000 50.000 10.572 90.634 30.350 60.792 30.000 60.000 10.376 50.535 30.378 20.855 30.672 20.074 70.000 70.185 40.000 10.727 60.660 60.076 110.000 70.432 60.646 50.000 10.594 60.006 90.000 50.000 10.658 40.000 20.000 10.661 10.549 50.300 80.291 80.045 80.942 60.304 40.600 50.572 40.135 100.695 20.000 10.008 50.793 40.942 10.899 20.000 10.816 30.181 60.897 20.000 10.679 30.223 30.264 20.691 30.345 9
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OctFormer ScanNet200permissive0.326 70.539 60.265 60.131 60.499 40.110 10.522 10.000 10.000 30.000 10.318 80.427 40.455 90.743 90.765 70.175 60.842 10.000 30.828 20.204 10.033 30.429 60.335 20.601 10.312 20.000 50.357 60.000 10.000 30.047 80.423 50.000 60.000 10.105 50.000 20.873 60.079 70.670 70.000 40.117 20.471 80.432 20.829 80.000 10.000 20.584 20.417 110.089 30.684 70.837 60.705 100.021 90.178 60.892 20.000 10.028 40.505 80.000 70.457 60.200 80.662 20.412 60.244 90.496 50.000 110.451 40.626 50.000 40.102 60.943 60.138 80.000 10.000 70.149 40.291 30.534 50.722 30.632 40.331 70.253 100.453 50.487 70.000 30.000 10.479 30.000 60.022 70.000 60.000 30.900 60.128 50.684 20.164 50.413 20.854 80.000 60.000 10.512 110.074 110.003 70.000 40.000 10.000 50.469 90.613 70.132 50.529 40.871 20.227 100.582 40.026 110.787 60.000 30.339 90.000 10.000 40.000 20.626 40.000 10.029 40.000 10.587 50.612 50.411 40.724 70.000 60.000 10.407 30.552 20.513 10.849 40.655 30.408 20.000 70.296 20.000 10.686 90.645 80.145 50.022 50.414 80.633 60.000 10.637 10.224 10.000 50.000 10.650 50.000 20.000 10.622 50.535 70.343 60.483 30.230 70.943 50.289 50.618 40.596 20.140 80.679 40.000 10.022 20.783 60.620 90.906 10.000 10.806 50.137 80.865 30.000 10.378 70.000 90.168 110.680 50.227 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo0.340 30.551 50.247 70.181 20.475 70.057 110.142 80.000 10.000 30.000 10.387 30.463 30.499 60.924 20.774 60.213 40.257 70.000 30.546 100.100 70.006 50.615 10.177 110.534 40.246 30.000 50.400 20.000 10.338 10.006 100.484 30.609 20.000 10.083 70.000 20.873 60.089 40.661 80.000 40.048 100.560 10.408 40.892 50.000 10.000 20.586 10.616 50.000 70.692 60.900 20.721 60.162 10.228 30.860 50.000 10.000 70.575 20.083 30.550 30.347 20.624 70.410 70.360 30.740 20.109 80.321 90.660 40.000 40.121 40.939 70.143 60.000 10.400 10.003 70.190 60.564 20.652 60.615 50.421 20.304 90.579 10.547 30.000 30.000 10.296 80.000 60.030 50.096 30.000 30.916 30.037 70.551 60.171 40.376 40.865 50.286 20.000 10.633 20.102 90.027 50.011 30.000 10.000 50.474 80.742 20.133 40.311 70.824 60.242 70.503 80.068 60.828 30.000 30.429 30.000 10.063 30.000 20.781 10.000 10.000 50.000 10.665 10.633 40.450 30.818 20.000 60.000 10.429 20.532 40.226 70.825 50.510 70.377 30.709 10.079 80.000 10.753 20.683 20.102 100.063 30.401 100.620 80.000 10.619 20.000 100.000 50.000 10.595 90.000 20.000 10.345 80.564 30.411 40.603 10.384 30.945 40.266 60.643 30.367 80.304 10.663 60.000 10.010 30.726 90.767 60.898 30.000 10.784 70.435 10.861 50.000 10.447 60.000 90.257 40.656 70.377 7
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS0.305 80.508 80.225 80.142 50.463 80.063 90.195 60.000 10.000 30.000 10.467 20.551 10.504 50.773 50.764 80.142 80.029 110.000 30.626 80.100 70.000 60.360 80.179 90.507 90.137 90.006 40.300 80.000 10.000 30.172 50.364 90.512 40.000 10.056 80.000 20.865 80.093 30.634 110.000 40.071 80.396 90.296 100.876 60.000 10.000 20.373 80.436 100.063 60.749 10.877 40.721 60.131 30.124 80.804 90.000 10.000 70.515 70.010 60.452 70.252 60.578 80.417 50.179 110.484 60.171 40.337 80.606 80.000 40.115 50.937 80.142 70.000 10.008 60.000 90.157 100.484 80.402 110.501 90.339 60.553 30.529 20.478 80.000 30.000 10.404 60.001 50.022 70.077 50.000 30.894 80.219 40.628 40.093 90.305 80.886 10.233 30.000 10.603 60.112 40.023 60.000 40.000 10.000 50.741 20.664 40.097 90.253 80.782 80.264 50.523 70.154 10.707 100.000 30.411 40.000 10.000 40.000 20.332 100.000 10.000 50.000 10.602 30.595 70.185 90.656 100.159 30.000 10.355 70.424 90.154 90.729 90.516 60.220 60.620 20.084 70.000 10.707 80.651 70.173 20.014 60.381 110.582 90.000 10.619 20.049 80.000 50.000 10.702 20.000 20.000 10.302 100.489 90.317 70.334 70.392 20.922 80.254 70.533 80.394 70.129 110.613 90.000 10.000 80.820 20.649 80.749 80.000 10.782 80.282 50.863 40.000 10.288 100.006 60.220 70.633 80.542 2
LGroundpermissive0.272 90.485 90.184 90.106 90.476 60.077 60.218 50.000 10.000 30.000 10.547 10.295 80.540 30.746 80.745 90.058 100.112 100.005 10.658 60.077 110.000 60.322 90.178 100.512 80.190 70.199 10.277 90.000 10.000 30.173 40.399 60.000 60.000 10.039 100.000 20.858 90.085 50.676 60.002 20.103 30.498 50.323 80.703 90.000 10.000 20.296 90.549 70.216 10.702 40.768 80.718 80.028 70.092 100.786 100.000 10.000 70.453 100.022 50.251 110.252 60.572 90.348 90.321 50.514 40.063 90.279 100.552 90.000 40.019 100.932 90.132 100.000 10.000 70.000 90.156 110.457 90.623 70.518 80.265 100.358 70.381 90.395 90.000 30.000 10.127 110.012 30.051 10.000 60.000 30.886 90.014 80.437 110.179 30.244 90.826 90.000 60.000 10.599 70.136 10.085 30.000 40.000 10.000 50.565 70.612 80.143 20.207 90.566 90.232 90.446 90.127 20.708 90.000 30.384 50.000 10.000 40.000 20.402 80.000 10.059 30.000 10.525 110.566 80.229 80.659 90.000 60.000 10.265 90.446 80.147 100.720 110.597 50.066 80.000 70.187 30.000 10.726 70.467 110.134 70.000 70.413 90.629 70.000 10.363 100.055 70.022 20.000 10.626 70.000 20.000 10.323 90.479 110.154 100.117 90.028 100.901 90.243 90.415 100.295 110.143 60.610 100.000 10.000 80.777 70.397 110.324 100.000 10.778 90.179 70.702 100.000 10.274 110.404 10.233 60.622 90.398 5
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 110.455 110.171 100.079 110.418 100.059 100.186 70.000 10.000 30.000 10.335 70.250 90.316 100.766 60.697 110.142 80.170 80.003 20.553 90.112 50.097 10.201 110.186 80.476 110.081 100.000 50.216 110.000 10.000 30.001 110.314 110.000 60.000 10.055 90.000 20.832 110.094 20.659 90.002 20.076 60.310 110.293 110.664 110.000 10.000 20.175 110.634 40.130 20.552 110.686 110.700 110.076 50.110 90.770 110.000 10.000 70.430 110.000 70.319 90.166 90.542 110.327 100.205 100.332 100.052 100.375 70.444 110.000 40.012 110.930 110.203 10.000 10.000 70.046 60.175 80.413 100.592 80.471 100.299 90.152 110.340 100.247 110.000 30.000 10.225 90.058 20.037 20.000 60.207 10.862 100.014 80.548 70.033 100.233 100.816 100.000 60.000 10.542 100.123 30.121 10.019 20.000 10.000 50.463 100.454 110.045 110.128 110.557 100.235 80.441 100.063 80.484 110.000 30.308 110.000 10.000 40.000 20.318 110.000 10.000 50.000 10.545 100.543 90.164 100.734 60.000 60.000 10.215 110.371 100.198 80.743 80.205 100.062 90.000 70.079 80.000 10.683 100.547 100.142 60.000 70.441 50.579 100.000 10.464 90.098 60.041 10.000 10.590 100.000 20.000 10.373 70.494 80.174 90.105 100.001 110.895 100.222 100.537 70.307 100.180 50.625 80.000 10.000 80.591 110.609 100.398 90.000 10.766 110.014 110.638 110.000 10.377 80.004 70.206 90.609 110.465 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 100.463 100.154 110.102 100.381 110.084 40.134 90.000 10.000 30.000 10.386 40.141 110.279 110.737 100.703 100.014 110.164 90.000 30.663 50.092 100.000 60.224 100.291 50.531 50.056 110.000 50.242 100.000 10.000 30.013 90.331 100.000 60.000 10.035 110.001 10.858 90.059 100.650 100.000 40.056 90.353 100.299 90.670 100.000 10.000 20.284 100.484 90.071 50.594 100.720 100.710 90.027 80.068 110.813 80.000 10.005 60.492 90.164 10.274 100.111 100.571 100.307 110.293 70.307 110.150 50.163 110.531 100.002 30.545 30.932 90.093 110.000 10.000 70.002 80.159 90.368 110.581 90.440 110.228 110.406 50.282 110.294 100.000 30.000 10.189 100.060 10.036 30.000 60.000 30.897 70.000 110.525 80.025 110.205 110.771 110.000 60.000 10.593 80.108 70.044 40.000 40.000 10.000 50.282 110.589 90.094 100.169 100.466 110.227 100.419 110.125 30.757 80.002 10.334 100.000 10.000 40.000 20.357 90.000 10.000 50.000 10.582 60.513 110.337 70.612 110.000 60.000 10.250 100.352 110.136 110.724 100.655 30.280 50.000 70.046 100.000 10.606 110.559 90.159 40.102 10.445 40.655 40.000 10.310 110.117 30.000 50.000 10.581 110.026 10.000 10.265 110.483 100.084 110.097 110.044 90.865 110.142 110.588 60.351 90.272 20.596 110.000 10.003 60.622 100.720 70.096 110.000 10.771 100.016 100.772 90.000 10.302 90.194 40.214 80.621 100.197 11
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019


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




Method Infoavg aphead apcommon aptail apalarm 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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
TD3D Scannet200permissive0.211 20.332 20.177 20.103 20.337 20.036 20.222 40.000 10.000 10.000 10.031 10.342 10.093 40.852 10.452 40.559 20.000 20.004 20.000 30.039 10.000 20.309 20.047 40.380 20.028 20.000 10.080 20.000 10.000 20.147 10.192 30.000 20.000 10.083 10.000 10.395 20.039 40.662 10.000 10.000 20.074 10.135 10.296 20.000 20.000 10.231 50.646 10.139 30.633 31.000 10.705 10.048 10.088 20.439 20.184 20.039 20.266 20.551 20.260 30.026 50.463 20.046 30.252 20.249 30.083 20.372 10.411 10.000 20.414 10.323 10.000 10.052 20.000 10.157 10.278 20.278 20.237 20.015 20.321 20.253 10.060 40.000 10.000 10.272 20.008 10.169 20.032 20.000 10.404 10.356 20.283 20.073 30.028 50.617 20.038 20.000 10.494 20.037 20.215 10.083 20.000 20.003 20.486 30.694 10.000 20.040 40.083 40.219 50.209 20.007 10.483 10.000 20.125 40.000 10.150 20.014 10.544 10.000 10.000 20.000 10.260 50.143 50.200 10.610 30.028 20.032 10.145 10.059 20.046 40.740 20.806 10.543 20.000 20.108 20.008 10.222 50.669 20.456 10.074 10.224 10.586 10.006 20.451 20.000 10.002 10.889 10.282 20.000 10.000 10.252 20.413 20.111 20.074 20.240 10.893 10.266 20.144 30.293 20.281 20.604 20.000 10.000 20.379 50.963 10.250 40.000 10.160 10.420 20.000 10.343 30.207 20.079 50.315 20.052 2
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Mask3D Scannet2000.278 10.383 10.263 10.168 10.506 10.068 10.083 50.000 10.000 10.000 10.023 20.149 40.302 10.778 30.647 10.569 10.500 10.031 10.014 20.027 20.173 10.311 10.195 10.351 30.258 10.000 10.082 10.000 10.003 10.037 20.391 11.000 10.000 10.014 20.000 10.572 10.573 10.661 20.000 10.003 10.005 40.082 40.349 10.028 10.000 10.605 10.515 30.509 10.711 11.000 10.665 30.015 20.107 10.402 40.201 10.083 10.304 10.759 10.491 10.378 10.572 10.119 10.277 10.013 50.089 10.283 20.411 20.267 10.006 30.156 20.000 10.116 10.000 10.105 30.556 10.514 10.396 10.275 10.323 10.215 20.380 10.000 10.000 10.356 10.005 20.208 10.325 10.000 10.050 40.400 10.561 10.258 10.179 10.722 10.147 10.000 10.586 10.063 10.015 20.139 10.016 10.028 10.708 10.418 20.016 10.048 30.500 10.489 10.349 10.001 20.475 20.086 10.365 10.000 10.500 10.000 20.323 30.000 10.222 10.000 10.497 10.626 10.044 30.795 10.556 10.008 20.121 40.265 10.667 10.789 10.568 20.579 10.444 10.176 10.004 20.474 10.752 10.233 20.014 20.002 40.570 20.007 10.377 50.000 10.000 20.000 20.337 10.000 10.000 10.384 10.465 10.287 10.085 10.048 20.816 50.467 10.810 10.377 10.415 10.744 10.000 10.004 10.724 10.778 20.590 10.000 10.032 20.441 10.000 10.377 20.391 10.427 10.321 10.192 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.permissive0.130 40.246 40.083 40.043 50.299 40.000 50.278 10.000 10.000 10.000 10.022 30.175 30.122 20.537 40.521 20.400 30.000 20.000 30.000 30.008 30.000 20.048 40.076 30.182 50.000 40.000 10.022 40.000 10.000 20.000 30.141 50.000 20.000 10.000 30.000 10.210 40.063 20.547 50.000 10.000 20.000 50.100 20.026 50.000 20.000 10.241 40.488 40.000 40.564 51.000 10.672 20.000 30.021 40.486 10.000 30.000 30.067 40.000 30.194 50.033 40.415 40.026 40.025 50.271 10.004 40.094 50.142 50.000 20.000 40.111 30.000 10.000 30.000 10.088 40.083 50.278 20.110 40.000 40.082 50.199 50.137 30.000 10.000 10.000 30.000 30.041 40.000 30.000 10.308 20.067 30.280 30.016 40.101 30.373 50.000 30.000 10.319 40.007 40.000 30.000 30.000 20.000 30.028 50.355 50.000 20.101 10.444 20.289 20.114 50.000 30.394 30.000 20.032 50.000 10.000 30.000 20.201 50.000 10.000 20.000 10.384 20.248 40.000 50.529 40.000 30.000 30.133 30.020 50.089 30.720 30.500 40.099 40.000 20.000 50.000 30.238 40.334 50.190 30.000 30.000 50.317 50.000 30.472 10.000 10.000 20.000 20.094 50.000 10.000 10.082 50.236 40.004 50.019 40.000 30.883 20.061 50.262 20.217 40.000 40.557 50.000 10.000 20.460 40.761 40.156 50.000 10.000 30.259 40.000 10.394 10.019 40.084 40.232 40.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.123 50.223 50.082 50.046 40.308 30.004 30.278 10.000 10.000 10.000 10.000 50.032 50.105 30.537 40.348 50.378 40.000 20.000 30.000 30.000 50.000 20.000 50.037 50.323 40.000 40.000 10.013 50.000 10.000 20.000 30.235 20.000 20.000 10.000 30.000 10.231 30.045 30.564 40.000 10.000 20.006 30.078 50.065 30.000 20.000 10.259 30.516 20.000 40.600 41.000 10.578 50.000 30.000 50.184 50.000 30.000 30.034 50.000 30.211 40.089 30.394 50.018 50.064 40.171 40.001 50.144 30.172 40.000 20.000 40.044 40.000 10.000 30.000 10.064 50.126 40.278 20.093 50.000 40.094 40.214 30.011 50.000 10.000 10.000 30.000 30.022 50.000 30.000 10.275 30.000 40.275 40.000 50.098 40.407 40.000 30.000 10.250 50.007 50.000 30.000 30.000 20.000 30.333 40.376 40.000 20.000 50.042 50.285 30.119 40.000 30.224 50.000 20.184 30.000 10.000 30.000 20.244 40.000 10.000 20.000 10.377 30.378 20.051 20.424 50.000 30.000 30.116 50.030 40.125 20.441 40.444 50.063 50.000 20.042 30.000 30.297 20.483 30.096 50.000 30.028 20.338 40.000 30.444 30.000 10.000 20.000 20.189 40.000 10.000 10.141 40.152 50.017 40.000 50.000 30.838 40.193 30.111 50.105 50.198 30.588 30.000 10.000 20.542 30.343 50.267 30.000 10.000 30.108 50.000 10.333 40.000 50.228 20.202 50.022 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.154 30.275 30.108 30.060 30.295 50.002 40.278 10.000 10.000 10.000 10.006 40.272 20.064 50.815 20.503 30.333 50.000 20.000 30.556 10.001 40.000 20.148 30.078 20.448 10.007 30.000 10.024 30.000 10.000 20.000 30.190 40.000 20.000 10.000 30.000 10.209 50.031 50.573 30.000 10.000 20.041 20.099 30.037 40.000 20.000 10.327 20.364 50.181 20.642 21.000 10.654 40.000 30.023 30.429 30.000 30.000 30.097 30.000 30.278 20.267 20.434 30.048 20.092 30.257 20.030 30.097 40.189 30.000 20.089 20.000 50.000 10.000 30.000 10.115 20.166 30.222 50.222 30.003 30.127 30.213 40.169 20.000 10.000 10.000 30.000 30.044 30.000 30.000 10.000 50.000 40.268 50.222 20.130 20.494 30.000 30.000 10.363 30.015 30.000 30.000 30.000 20.000 30.611 20.400 30.000 20.056 20.278 30.242 40.180 30.000 30.383 40.000 20.209 20.000 10.000 30.000 20.364 20.000 10.000 20.000 10.323 40.302 30.019 40.654 20.000 30.000 30.141 20.045 30.000 50.427 50.514 30.143 30.000 20.028 40.000 30.252 30.402 40.156 40.000 30.028 20.470 30.000 30.444 30.000 10.000 20.000 20.205 30.000 10.000 10.203 30.381 30.026 30.037 30.000 30.881 30.099 40.135 40.239 30.000 40.585 40.000 10.000 20.616 20.778 20.322 20.000 10.000 30.407 30.000 10.333 40.148 30.177 30.242 30.028 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3 ScanNet0.794 10.941 30.813 180.851 70.782 60.890 20.597 10.916 20.696 80.713 30.979 10.635 10.384 20.793 20.907 80.821 40.790 300.696 110.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV20.785 20.978 10.800 260.833 220.788 40.853 160.545 160.910 50.713 10.705 40.979 10.596 70.390 10.769 110.832 410.821 40.792 290.730 10.975 10.897 40.785 4
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3Dpermissive0.781 30.964 20.855 10.843 160.781 70.858 120.575 60.831 320.685 140.714 20.979 10.594 80.310 260.801 10.892 160.841 20.819 40.723 40.940 130.887 60.725 23
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 140.836 190.790 30.875 40.576 50.905 60.704 50.739 10.969 100.611 20.349 100.756 210.958 10.702 440.805 140.708 70.916 310.898 30.801 2
TTT-KD0.773 50.646 900.818 140.809 340.774 90.878 30.581 20.943 10.687 120.704 50.978 40.607 50.336 150.775 80.912 60.838 30.823 20.694 120.967 30.899 20.794 3
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 60.939 40.824 60.854 60.771 100.840 300.564 100.900 80.686 130.677 110.961 160.537 300.348 110.769 110.903 100.785 100.815 60.676 210.939 140.880 110.772 8
PPT-SpUNet-Joint0.766 70.932 50.794 320.829 240.751 220.854 140.540 200.903 70.630 330.672 140.963 140.565 210.357 80.788 30.900 120.737 250.802 150.685 160.950 70.887 60.780 5
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 70.925 70.808 220.849 90.786 50.846 260.566 90.876 140.690 100.674 130.960 170.576 170.226 660.753 230.904 90.777 120.815 60.722 50.923 270.877 130.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net0.764 90.924 80.819 120.840 170.757 170.853 160.580 30.848 250.709 30.643 230.958 200.587 120.295 320.753 230.884 200.758 190.815 60.725 30.927 240.867 210.743 14
OccuSeg+Semantic0.764 90.758 580.796 300.839 180.746 240.907 10.562 110.850 240.680 160.672 140.978 40.610 30.335 170.777 60.819 440.847 10.830 10.691 140.972 20.885 80.727 21
O-CNNpermissive0.762 110.924 80.823 70.844 150.770 110.852 180.577 40.847 270.711 20.640 270.958 200.592 90.217 720.762 160.888 170.758 190.813 100.726 20.932 220.868 200.744 13
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DTC0.757 120.843 260.820 100.847 120.791 20.862 100.511 320.870 160.707 40.652 190.954 340.604 60.279 430.760 170.942 20.734 260.766 430.701 100.884 530.874 180.736 15
OA-CNN-L_ScanNet200.756 130.783 440.826 50.858 40.776 80.837 330.548 150.896 110.649 250.675 120.962 150.586 130.335 170.771 100.802 480.770 150.787 320.691 140.936 170.880 110.761 10
ConDaFormer0.755 140.927 60.822 80.836 190.801 10.849 210.516 300.864 210.651 240.680 100.958 200.584 150.282 400.759 190.855 310.728 280.802 150.678 180.880 580.873 190.756 11
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PNE0.755 140.786 420.835 40.834 210.758 150.849 210.570 80.836 310.648 260.668 160.978 40.581 160.367 60.683 340.856 290.804 60.801 190.678 180.961 50.889 50.716 28
P. Hermosilla: Point Neighborhood Embeddings.
DMF-Net0.752 160.906 120.793 340.802 400.689 390.825 450.556 120.867 170.681 150.602 430.960 170.555 260.365 70.779 50.859 260.747 220.795 260.717 60.917 300.856 290.764 9
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointTransformerV20.752 160.742 660.809 210.872 10.758 150.860 110.552 130.891 120.610 400.687 60.960 170.559 240.304 290.766 140.926 40.767 160.797 220.644 320.942 110.876 160.722 25
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 180.793 400.790 350.807 360.750 230.856 130.524 260.881 130.588 520.642 260.977 80.591 100.274 460.781 40.929 30.804 60.796 230.642 330.947 90.885 80.715 29
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 180.909 100.818 140.811 320.752 200.839 320.485 460.842 280.673 170.644 220.957 240.528 360.305 280.773 90.859 260.788 80.818 50.693 130.916 310.856 290.723 24
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 200.623 930.804 240.859 30.745 250.824 470.501 360.912 40.690 100.685 80.956 250.567 200.320 230.768 130.918 50.720 330.802 150.676 210.921 280.881 100.779 6
StratifiedFormerpermissive0.747 210.901 130.803 250.845 140.757 170.846 260.512 310.825 350.696 80.645 210.956 250.576 170.262 570.744 280.861 250.742 230.770 410.705 80.899 430.860 260.734 16
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
VMNetpermissive0.746 220.870 180.838 20.858 40.729 300.850 200.501 360.874 150.587 530.658 180.956 250.564 220.299 300.765 150.900 120.716 360.812 110.631 380.939 140.858 270.709 30
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 MVFusion0.746 220.771 520.819 120.848 110.702 360.865 90.397 840.899 90.699 60.664 170.948 540.588 110.330 190.746 270.851 350.764 170.796 230.704 90.935 180.866 220.728 19
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN0.744 240.842 270.800 260.767 540.740 260.836 350.541 180.914 30.672 180.626 310.958 200.552 270.272 480.777 60.886 190.696 450.801 190.674 240.941 120.858 270.717 26
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-Net0.743 250.620 940.799 290.849 90.730 290.822 490.493 430.897 100.664 190.681 90.955 280.562 230.378 30.760 170.903 100.738 240.801 190.673 250.907 350.877 130.745 12
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 260.860 210.765 480.819 270.769 120.848 230.533 220.829 330.663 200.631 300.955 280.586 130.274 460.753 230.896 140.729 270.760 490.666 270.921 280.855 310.733 17
LRPNet0.742 260.816 350.806 230.807 360.752 200.828 430.575 60.839 300.699 60.637 280.954 340.520 390.320 230.755 220.834 390.760 180.772 380.676 210.915 330.862 240.717 26
LargeKernel3D0.739 280.909 100.820 100.806 380.740 260.852 180.545 160.826 340.594 510.643 230.955 280.541 290.263 560.723 320.858 280.775 140.767 420.678 180.933 200.848 360.694 35
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNetpermissive0.736 290.859 220.818 140.832 230.709 340.840 300.521 280.853 230.660 220.643 230.951 440.544 280.286 380.731 300.893 150.675 540.772 380.683 170.874 650.852 340.727 21
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
RPN0.736 290.776 480.790 350.851 70.754 190.854 140.491 450.866 190.596 500.686 70.955 280.536 310.342 130.624 490.869 220.787 90.802 150.628 390.927 240.875 170.704 32
IPCA0.731 310.890 140.837 30.864 20.726 310.873 50.530 250.824 360.489 860.647 200.978 40.609 40.336 150.624 490.733 570.758 190.776 360.570 640.949 80.877 130.728 19
SparseConvNet0.725 320.647 890.821 90.846 130.721 320.869 60.533 220.754 570.603 460.614 350.955 280.572 190.325 210.710 330.870 210.724 310.823 20.628 390.934 190.865 230.683 38
PointTransformer++0.725 320.727 740.811 200.819 270.765 130.841 290.502 350.814 410.621 360.623 330.955 280.556 250.284 390.620 510.866 230.781 110.757 530.648 300.932 220.862 240.709 30
MatchingNet0.724 340.812 370.812 190.810 330.735 280.834 370.495 420.860 220.572 600.602 430.954 340.512 410.280 420.757 200.845 370.725 300.780 340.606 490.937 160.851 350.700 34
INS-Conv-semantic0.717 350.751 610.759 510.812 310.704 350.868 70.537 210.842 280.609 420.608 390.953 380.534 330.293 330.616 520.864 240.719 350.793 270.640 340.933 200.845 400.663 44
PointMetaBase0.714 360.835 280.785 370.821 250.684 410.846 260.531 240.865 200.614 370.596 470.953 380.500 440.246 620.674 350.888 170.692 460.764 450.624 410.849 800.844 410.675 40
contrastBoundarypermissive0.705 370.769 550.775 420.809 340.687 400.820 520.439 720.812 420.661 210.591 490.945 620.515 400.171 900.633 460.856 290.720 330.796 230.668 260.889 500.847 370.689 36
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 380.774 500.800 260.793 450.760 140.847 250.471 500.802 450.463 930.634 290.968 120.491 470.271 500.726 310.910 70.706 400.815 60.551 760.878 590.833 420.570 76
RFCR0.702 390.889 150.745 620.813 300.672 440.818 560.493 430.815 400.623 340.610 370.947 560.470 560.249 610.594 550.848 360.705 410.779 350.646 310.892 480.823 480.611 59
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 Click0.701 400.825 320.796 300.723 610.716 330.832 390.433 740.816 380.634 310.609 380.969 100.418 820.344 120.559 670.833 400.715 370.808 130.560 700.902 400.847 370.680 39
JSENetpermissive0.699 410.881 170.762 490.821 250.667 450.800 680.522 270.792 480.613 380.607 400.935 820.492 460.205 770.576 600.853 330.691 480.758 510.652 290.872 680.828 450.649 48
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-Click0.693 420.743 650.794 320.655 840.684 410.822 490.497 410.719 670.622 350.617 340.977 80.447 690.339 140.750 260.664 730.703 430.790 300.596 540.946 100.855 310.647 49
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 430.732 700.772 430.786 460.677 430.866 80.517 290.848 250.509 790.626 310.952 420.536 310.225 680.545 730.704 640.689 510.810 120.564 690.903 390.854 330.729 18
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 440.884 160.754 550.795 430.647 520.818 560.422 760.802 450.612 390.604 410.945 620.462 590.189 850.563 660.853 330.726 290.765 440.632 370.904 370.821 510.606 63
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 450.704 790.741 660.754 580.656 470.829 410.501 360.741 620.609 420.548 570.950 480.522 380.371 40.633 460.756 520.715 370.771 400.623 420.861 760.814 540.658 45
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 460.866 190.748 590.819 270.645 540.794 710.450 620.802 450.587 530.604 410.945 620.464 580.201 800.554 690.840 380.723 320.732 630.602 520.907 350.822 500.603 66
KP-FCNN0.684 470.847 250.758 530.784 480.647 520.814 590.473 490.772 510.605 440.594 480.935 820.450 670.181 880.587 560.805 470.690 490.785 330.614 450.882 550.819 520.632 55
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 470.728 730.757 540.776 510.690 370.804 660.464 550.816 380.577 590.587 500.945 620.508 430.276 450.671 360.710 620.663 590.750 570.589 590.881 560.832 440.653 47
DGNet0.684 470.712 780.784 380.782 500.658 460.835 360.499 400.823 370.641 280.597 460.950 480.487 490.281 410.575 610.619 770.647 670.764 450.620 440.871 710.846 390.688 37
PointContrast_LA_SEM0.683 500.757 590.784 380.786 460.639 560.824 470.408 790.775 500.604 450.541 590.934 860.532 340.269 520.552 700.777 500.645 700.793 270.640 340.913 340.824 470.671 41
Superpoint Network0.683 500.851 240.728 700.800 420.653 490.806 640.468 520.804 430.572 600.602 430.946 590.453 660.239 650.519 780.822 420.689 510.762 480.595 560.895 460.827 460.630 56
VI-PointConv0.676 520.770 540.754 550.783 490.621 600.814 590.552 130.758 550.571 620.557 550.954 340.529 350.268 540.530 760.682 680.675 540.719 660.603 510.888 510.833 420.665 43
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 530.789 410.748 590.763 560.635 580.814 590.407 810.747 590.581 570.573 520.950 480.484 500.271 500.607 530.754 530.649 640.774 370.596 540.883 540.823 480.606 63
SALANet0.670 540.816 350.770 460.768 530.652 500.807 630.451 590.747 590.659 230.545 580.924 920.473 550.149 1000.571 630.811 460.635 730.746 580.623 420.892 480.794 670.570 76
O3DSeg0.668 550.822 330.771 450.496 1040.651 510.833 380.541 180.761 540.555 680.611 360.966 130.489 480.370 50.388 980.580 800.776 130.751 550.570 640.956 60.817 530.646 50
PointConvpermissive0.666 560.781 450.759 510.699 690.644 550.822 490.475 480.779 490.564 650.504 750.953 380.428 760.203 790.586 580.754 530.661 600.753 540.588 600.902 400.813 560.642 51
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 560.703 800.781 400.751 600.655 480.830 400.471 500.769 520.474 890.537 610.951 440.475 540.279 430.635 440.698 670.675 540.751 550.553 750.816 870.806 580.703 33
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 580.746 630.708 730.722 620.638 570.820 520.451 590.566 950.599 480.541 590.950 480.510 420.313 250.648 410.819 440.616 780.682 810.590 580.869 720.810 570.656 46
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 590.558 1010.751 570.655 840.690 370.722 930.453 580.867 170.579 580.576 510.893 1040.523 370.293 330.733 290.571 820.692 460.659 880.606 490.875 620.804 600.668 42
DCM-Net0.658 590.778 460.702 760.806 380.619 610.813 620.468 520.693 750.494 820.524 670.941 740.449 680.298 310.510 800.821 430.675 540.727 650.568 670.826 850.803 610.637 53
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 610.698 820.743 640.650 860.564 780.820 520.505 340.758 550.631 320.479 790.945 620.480 520.226 660.572 620.774 510.690 490.735 610.614 450.853 790.776 820.597 69
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 620.752 600.734 680.664 820.583 730.815 580.399 830.754 570.639 290.535 630.942 720.470 560.309 270.665 370.539 840.650 630.708 710.635 360.857 780.793 690.642 51
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 630.778 460.731 690.699 690.577 740.829 410.446 640.736 630.477 880.523 690.945 620.454 630.269 520.484 880.749 560.618 760.738 590.599 530.827 840.792 720.621 58
PointConv-SFPN0.641 640.776 480.703 750.721 630.557 810.826 440.451 590.672 800.563 660.483 780.943 710.425 790.162 950.644 420.726 580.659 610.709 700.572 630.875 620.786 770.559 82
MVPNetpermissive0.641 640.831 290.715 710.671 790.590 690.781 770.394 850.679 770.642 270.553 560.937 790.462 590.256 580.649 400.406 980.626 740.691 780.666 270.877 600.792 720.608 62
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 660.717 770.701 770.692 720.576 750.801 670.467 540.716 680.563 660.459 850.953 380.429 750.169 920.581 590.854 320.605 790.710 680.550 770.894 470.793 690.575 74
FPConvpermissive0.639 670.785 430.760 500.713 670.603 640.798 690.392 860.534 1000.603 460.524 670.948 540.457 610.250 600.538 740.723 600.598 830.696 760.614 450.872 680.799 620.567 79
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 680.797 390.769 470.641 920.590 690.820 520.461 560.537 990.637 300.536 620.947 560.388 890.206 760.656 380.668 710.647 670.732 630.585 610.868 730.793 690.473 102
PointSPNet0.637 690.734 690.692 840.714 660.576 750.797 700.446 640.743 610.598 490.437 900.942 720.403 850.150 990.626 480.800 490.649 640.697 750.557 730.846 810.777 810.563 80
SConv0.636 700.830 300.697 800.752 590.572 770.780 790.445 660.716 680.529 720.530 640.951 440.446 700.170 910.507 830.666 720.636 720.682 810.541 830.886 520.799 620.594 70
Supervoxel-CNN0.635 710.656 870.711 720.719 640.613 620.757 880.444 690.765 530.534 710.566 530.928 900.478 530.272 480.636 430.531 860.664 580.645 920.508 900.864 750.792 720.611 59
joint point-basedpermissive0.634 720.614 950.778 410.667 810.633 590.825 450.420 770.804 430.467 910.561 540.951 440.494 450.291 350.566 640.458 930.579 890.764 450.559 720.838 820.814 540.598 68
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 730.731 710.688 870.675 760.591 680.784 760.444 690.565 960.610 400.492 760.949 520.456 620.254 590.587 560.706 630.599 820.665 870.612 480.868 730.791 750.579 73
3DSM_DMMF0.631 740.626 920.745 620.801 410.607 630.751 890.506 330.729 660.565 640.491 770.866 1070.434 710.197 830.595 540.630 760.709 390.705 730.560 700.875 620.740 920.491 97
PointNet2-SFPN0.631 740.771 520.692 840.672 770.524 860.837 330.440 710.706 730.538 700.446 870.944 680.421 810.219 710.552 700.751 550.591 850.737 600.543 820.901 420.768 840.557 83
APCF-Net0.631 740.742 660.687 890.672 770.557 810.792 740.408 790.665 810.545 690.508 720.952 420.428 760.186 860.634 450.702 650.620 750.706 720.555 740.873 660.798 640.581 72
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 770.604 970.741 660.766 550.590 690.747 900.501 360.734 640.503 810.527 650.919 960.454 630.323 220.550 720.420 970.678 530.688 790.544 800.896 450.795 660.627 57
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 780.800 380.625 1000.719 640.545 830.806 640.445 660.597 890.448 960.519 700.938 780.481 510.328 200.489 870.499 910.657 620.759 500.592 570.881 560.797 650.634 54
SegGroup_sempermissive0.627 790.818 340.747 610.701 680.602 650.764 850.385 900.629 860.490 840.508 720.931 890.409 840.201 800.564 650.725 590.618 760.692 770.539 840.873 660.794 670.548 86
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 800.830 300.694 820.757 570.563 790.772 830.448 630.647 840.520 750.509 710.949 520.431 740.191 840.496 850.614 780.647 670.672 850.535 860.876 610.783 780.571 75
dtc_net0.625 800.703 800.751 570.794 440.535 840.848 230.480 470.676 790.528 730.469 820.944 680.454 630.004 1130.464 900.636 750.704 420.758 510.548 790.924 260.787 760.492 96
HPEIN0.618 820.729 720.668 900.647 880.597 670.766 840.414 780.680 760.520 750.525 660.946 590.432 720.215 730.493 860.599 790.638 710.617 970.570 640.897 440.806 580.605 65
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-GCNpermissive0.610 830.858 230.772 430.489 1050.532 850.792 740.404 820.643 850.570 630.507 740.935 820.414 830.046 1100.510 800.702 650.602 810.705 730.549 780.859 770.773 830.534 89
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 840.760 570.667 910.649 870.521 870.793 720.457 570.648 830.528 730.434 920.947 560.401 860.153 980.454 910.721 610.648 660.717 670.536 850.904 370.765 850.485 98
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 850.634 910.743 640.697 710.601 660.781 770.437 730.585 920.493 830.446 870.933 870.394 870.011 1120.654 390.661 740.603 800.733 620.526 870.832 830.761 870.480 99
LAP-D0.594 860.720 750.692 840.637 930.456 970.773 820.391 880.730 650.587 530.445 890.940 760.381 900.288 360.434 940.453 950.591 850.649 900.581 620.777 910.749 910.610 61
DPC0.592 870.720 750.700 780.602 970.480 930.762 870.380 910.713 710.585 560.437 900.940 760.369 920.288 360.434 940.509 900.590 870.639 950.567 680.772 930.755 890.592 71
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 880.766 560.659 950.683 740.470 960.740 920.387 890.620 880.490 840.476 800.922 940.355 950.245 630.511 790.511 890.571 900.643 930.493 940.872 680.762 860.600 67
ROSMRF0.580 890.772 510.707 740.681 750.563 790.764 850.362 930.515 1010.465 920.465 840.936 810.427 780.207 750.438 920.577 810.536 930.675 840.486 950.723 990.779 790.524 92
SD-DETR0.576 900.746 630.609 1040.445 1090.517 880.643 1040.366 920.714 700.456 940.468 830.870 1060.432 720.264 550.558 680.674 690.586 880.688 790.482 960.739 970.733 940.537 88
SQN_0.1%0.569 910.676 840.696 810.657 830.497 890.779 800.424 750.548 970.515 770.376 970.902 1030.422 800.357 80.379 990.456 940.596 840.659 880.544 800.685 1020.665 1050.556 84
TextureNetpermissive0.566 920.672 860.664 920.671 790.494 910.719 940.445 660.678 780.411 1020.396 950.935 820.356 940.225 680.412 960.535 850.565 910.636 960.464 980.794 900.680 1020.568 78
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
DVVNet0.562 930.648 880.700 780.770 520.586 720.687 980.333 970.650 820.514 780.475 810.906 1000.359 930.223 700.340 1010.442 960.422 1040.668 860.501 910.708 1000.779 790.534 89
Pointnet++ & Featurepermissive0.557 940.735 680.661 940.686 730.491 920.744 910.392 860.539 980.451 950.375 980.946 590.376 910.205 770.403 970.356 1010.553 920.643 930.497 920.824 860.756 880.515 93
GMLPs0.538 950.495 1060.693 830.647 880.471 950.793 720.300 1000.477 1020.505 800.358 1000.903 1020.327 980.081 1070.472 890.529 870.448 1020.710 680.509 880.746 950.737 930.554 85
PanopticFusion-label0.529 960.491 1070.688 870.604 960.386 1020.632 1050.225 1100.705 740.434 990.293 1060.815 1080.348 960.241 640.499 840.669 700.507 950.649 900.442 1040.796 890.602 1090.561 81
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_weak0.516 970.676 840.591 1070.609 940.442 980.774 810.335 960.597 890.422 1010.357 1010.932 880.341 970.094 1060.298 1030.528 880.473 1000.676 830.495 930.602 1080.721 970.349 109
Online SegFusion0.515 980.607 960.644 980.579 990.434 990.630 1060.353 940.628 870.440 970.410 930.762 1120.307 1000.167 930.520 770.403 990.516 940.565 1000.447 1020.678 1030.701 990.514 94
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, FTSDF0.501 990.558 1010.608 1050.424 1110.478 940.690 970.246 1060.586 910.468 900.450 860.911 980.394 870.160 960.438 920.212 1080.432 1030.541 1060.475 970.742 960.727 950.477 100
PCNN0.498 1000.559 1000.644 980.560 1010.420 1010.711 960.229 1080.414 1030.436 980.352 1020.941 740.324 990.155 970.238 1080.387 1000.493 960.529 1070.509 880.813 880.751 900.504 95
Weakly-Openseg v30.489 1010.749 620.664 920.646 900.496 900.559 1100.122 1130.577 930.257 1130.364 990.805 1090.198 1110.096 1050.510 800.496 920.361 1080.563 1010.359 1110.777 910.644 1060.532 91
3DMV0.484 1020.484 1080.538 1090.643 910.424 1000.606 1090.310 980.574 940.433 1000.378 960.796 1100.301 1010.214 740.537 750.208 1090.472 1010.507 1100.413 1070.693 1010.602 1090.539 87
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1030.577 990.611 1030.356 1130.321 1100.715 950.299 1020.376 1070.328 1090.319 1040.944 680.285 1030.164 940.216 1110.229 1060.484 980.545 1050.456 1000.755 940.709 980.475 101
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1040.679 830.604 1060.578 1000.380 1030.682 990.291 1030.106 1130.483 870.258 1110.920 950.258 1070.025 1110.231 1100.325 1020.480 990.560 1030.463 990.725 980.666 1040.231 113
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1050.474 1090.623 1010.463 1070.366 1050.651 1020.310 980.389 1060.349 1070.330 1030.937 790.271 1050.126 1020.285 1040.224 1070.350 1100.577 990.445 1030.625 1060.723 960.394 105
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 1060.548 1030.548 1080.597 980.363 1060.628 1070.300 1000.292 1080.374 1040.307 1050.881 1050.268 1060.186 860.238 1080.204 1100.407 1050.506 1110.449 1010.667 1040.620 1080.462 103
SurfaceConvPF0.442 1060.505 1050.622 1020.380 1120.342 1080.654 1010.227 1090.397 1050.367 1050.276 1080.924 920.240 1080.198 820.359 1000.262 1040.366 1060.581 980.435 1050.640 1050.668 1030.398 104
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1080.437 1110.646 970.474 1060.369 1040.645 1030.353 940.258 1100.282 1110.279 1070.918 970.298 1020.147 1010.283 1050.294 1030.487 970.562 1020.427 1060.619 1070.633 1070.352 108
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1090.525 1040.647 960.522 1020.324 1090.488 1130.077 1140.712 720.353 1060.401 940.636 1140.281 1040.176 890.340 1010.565 830.175 1140.551 1040.398 1080.370 1140.602 1090.361 107
SPLAT Netcopyleft0.393 1100.472 1100.511 1100.606 950.311 1110.656 1000.245 1070.405 1040.328 1090.197 1120.927 910.227 1100.000 1150.001 1150.249 1050.271 1130.510 1080.383 1100.593 1090.699 1000.267 111
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 1110.297 1130.491 1110.432 1100.358 1070.612 1080.274 1040.116 1120.411 1020.265 1090.904 1010.229 1090.079 1080.250 1060.185 1110.320 1110.510 1080.385 1090.548 1100.597 1120.394 105
PointNet++permissive0.339 1120.584 980.478 1120.458 1080.256 1130.360 1140.250 1050.247 1110.278 1120.261 1100.677 1130.183 1120.117 1030.212 1120.145 1130.364 1070.346 1140.232 1140.548 1100.523 1130.252 112
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 1130.353 1120.290 1140.278 1140.166 1140.553 1110.169 1120.286 1090.147 1140.148 1140.908 990.182 1130.064 1090.023 1140.018 1150.354 1090.363 1120.345 1120.546 1120.685 1010.278 110
ScanNetpermissive0.306 1140.203 1140.366 1130.501 1030.311 1110.524 1120.211 1110.002 1150.342 1080.189 1130.786 1110.145 1140.102 1040.245 1070.152 1120.318 1120.348 1130.300 1130.460 1130.437 1140.182 114
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 1150.000 1150.041 1150.172 1150.030 1150.062 1150.001 1150.035 1140.004 1150.051 1150.143 1150.019 1150.003 1140.041 1130.050 1140.003 1150.054 1150.018 1150.005 1150.264 1150.082 115


This table lists the benchmark results for the 3D 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
Spherical Mask(CtoF)0.616 10.946 50.654 90.555 40.434 80.769 40.271 70.604 80.447 30.505 30.549 10.698 20.716 20.775 100.480 50.747 40.575 60.925 80.436 4
SIM3D0.614 20.952 40.654 80.539 60.422 110.768 60.302 40.688 20.419 50.476 90.511 100.703 10.717 10.743 180.460 120.770 10.565 90.914 110.446 2
ExtMask3D0.598 30.852 130.692 40.433 240.461 50.791 10.264 80.488 290.493 10.508 20.528 90.594 70.706 40.791 50.483 30.734 70.595 20.911 130.437 3
MAFT0.596 40.889 110.721 10.448 170.460 60.768 50.251 90.558 170.408 60.504 40.539 50.616 50.618 80.858 20.482 40.684 150.551 110.931 70.450 1
UniPerception0.588 50.963 30.667 60.493 100.472 40.750 80.229 120.528 220.468 20.498 60.542 40.643 30.530 170.661 310.463 90.695 140.599 10.972 10.420 5
InsSSM0.586 61.000 10.593 140.440 200.480 20.771 20.345 10.437 330.444 40.495 70.548 30.579 100.621 70.720 220.409 160.712 90.593 30.960 30.395 7
Queryformer0.583 70.926 70.702 20.393 300.504 10.733 140.276 60.527 230.373 110.479 80.534 70.533 160.697 50.720 230.436 140.745 50.592 40.958 40.363 15
PBNetpermissive0.573 80.926 70.575 190.619 10.472 30.736 120.239 110.487 300.383 100.459 120.506 130.533 150.585 100.767 110.404 170.717 80.559 100.969 20.381 11
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
TST3D0.569 90.778 200.675 50.598 20.451 70.727 150.280 50.476 320.395 70.472 100.457 210.583 80.580 120.777 70.462 110.735 60.547 130.919 100.333 21
OneFormer3Dcopyleft0.566 100.781 190.697 30.562 30.431 90.770 30.331 20.400 390.373 120.529 10.504 140.568 120.475 220.732 200.470 70.762 20.550 120.871 280.379 12
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
Mask3D0.566 100.926 70.597 130.408 270.420 120.737 110.239 100.598 100.386 90.458 130.549 10.568 130.716 20.601 370.480 50.646 190.575 60.922 90.364 14
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ISBNetpermissive0.559 120.939 60.655 70.383 330.426 100.763 70.180 140.534 210.386 80.499 50.509 120.621 40.427 320.704 260.467 80.649 180.571 80.948 50.401 6
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
GraphCut0.552 131.000 10.611 120.438 210.392 150.714 160.139 170.598 110.327 150.389 160.510 110.598 60.427 330.754 140.463 100.761 30.588 50.903 160.329 22
SPFormerpermissive0.549 140.745 230.640 100.484 110.395 140.739 100.311 30.566 150.335 140.468 110.492 150.555 140.478 210.747 160.436 130.712 100.540 140.893 200.343 20
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet0.532 150.815 160.624 110.517 70.377 170.749 90.107 190.509 260.304 170.437 140.475 160.581 90.539 150.775 90.339 220.640 210.506 170.901 170.385 10
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst0.520 160.889 110.551 230.548 50.418 130.665 260.064 280.585 120.260 250.277 300.471 180.500 170.644 60.785 60.369 180.591 270.511 150.878 250.362 16
SoftGroup++0.513 170.704 290.578 180.398 290.363 230.704 170.061 290.647 50.297 220.378 190.537 60.343 200.614 90.828 40.295 270.710 120.505 190.875 270.394 8
SSTNetpermissive0.506 180.738 260.549 240.497 90.316 280.693 200.178 150.377 420.198 310.330 210.463 200.576 110.515 180.857 30.494 10.637 220.457 230.943 60.290 31
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGrouppermissive0.504 190.667 360.579 160.372 350.381 160.694 190.072 250.677 30.303 180.387 170.531 80.319 240.582 110.754 130.318 230.643 200.492 200.907 150.388 9
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
DANCENET0.504 190.926 70.579 150.472 130.367 200.626 360.165 160.432 340.221 270.408 150.449 230.411 180.564 130.746 170.421 150.707 130.438 260.846 360.288 32
TD3Dpermissive0.489 210.852 130.511 330.434 220.322 270.735 130.101 220.512 250.355 130.349 200.468 190.283 280.514 190.676 300.268 320.671 160.510 160.908 140.329 23
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
OccuSeg+instance0.486 220.802 180.536 260.428 250.369 190.702 180.205 130.331 470.301 190.379 180.474 170.327 210.437 270.862 10.485 20.601 250.394 340.846 380.273 35
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg0.479 230.704 290.564 200.467 150.366 210.633 340.068 260.554 180.262 240.328 220.447 240.323 220.534 160.722 210.288 290.614 230.482 210.912 120.358 18
DualGroup0.469 240.815 160.552 220.398 280.374 180.683 220.130 180.539 200.310 160.327 230.407 270.276 290.447 260.535 410.342 210.659 170.455 240.900 190.301 27
SSEC0.465 250.667 360.578 170.502 80.362 240.641 330.035 380.605 70.291 230.323 240.451 220.296 260.417 360.677 290.245 360.501 450.506 180.900 180.366 13
HAISpermissive0.457 260.704 290.561 210.457 160.364 220.673 230.046 370.547 190.194 320.308 250.426 250.288 270.454 250.711 240.262 330.563 350.434 280.889 220.344 19
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group0.436 270.630 440.508 360.480 120.310 300.624 380.065 270.638 60.174 330.256 340.384 310.194 410.428 300.759 120.289 280.574 320.400 320.849 350.291 30
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance0.435 280.716 280.495 380.355 370.331 250.689 210.102 210.394 410.208 300.280 280.395 290.250 320.544 140.741 190.309 250.536 410.391 350.842 410.258 39
Mask-Group0.434 290.778 200.516 310.471 140.330 260.658 270.029 400.526 240.249 260.256 330.400 280.309 250.384 400.296 570.368 190.575 310.425 290.877 260.362 17
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask0.433 300.741 240.463 430.433 230.283 330.625 370.103 200.298 520.125 420.260 320.424 260.322 230.472 230.701 270.363 200.711 110.309 510.882 230.272 37
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN0.428 310.630 440.508 350.367 360.249 400.658 280.016 480.673 40.131 400.234 370.383 320.270 300.434 280.748 150.274 310.609 240.406 310.842 400.267 38
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet0.413 320.741 240.520 280.237 480.284 320.523 470.097 230.691 10.138 370.209 470.229 490.238 350.390 380.707 250.310 240.448 520.470 220.892 210.310 25
PointGroup0.407 330.639 430.496 370.415 260.243 420.645 320.021 450.570 140.114 430.211 450.359 340.217 390.428 310.660 320.256 340.562 360.341 430.860 310.291 29
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]
CSC-Pretrained0.405 340.738 260.465 420.331 410.205 460.655 290.051 330.601 90.092 470.211 460.329 370.198 400.459 240.775 80.195 430.524 430.400 330.878 240.184 48
PE0.396 350.667 360.467 410.446 190.243 410.624 390.022 440.577 130.106 440.219 400.340 350.239 340.487 200.475 480.225 380.541 400.350 410.818 430.273 36
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3Dcopyleft0.395 360.642 420.518 300.447 180.259 390.666 250.050 340.251 570.166 340.231 380.362 330.232 360.331 430.535 400.229 370.587 280.438 270.850 330.317 24
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS0.392 370.778 200.530 270.220 500.278 340.567 440.083 240.330 480.299 200.270 310.310 400.143 470.260 470.624 350.277 300.568 340.361 390.865 300.301 26
AOIA0.387 380.704 290.515 320.385 320.225 450.669 240.005 550.482 310.126 410.181 500.269 460.221 380.426 340.478 470.218 390.592 260.371 370.851 320.242 41
SSEN0.384 390.852 130.494 390.192 510.226 440.648 310.022 430.398 400.299 210.277 290.317 390.231 370.194 540.514 440.196 410.586 290.444 250.843 390.184 47
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Mask3D_evaluation0.382 400.593 460.520 290.390 310.314 290.600 400.018 470.287 550.151 360.281 270.387 300.169 450.429 290.654 330.172 470.578 300.384 360.670 540.278 34
PCJC0.375 410.704 290.542 250.284 450.197 480.649 300.006 520.426 350.138 380.242 350.304 410.183 440.388 390.629 340.141 540.546 390.344 420.738 490.283 33
ClickSeg_Instance0.366 420.654 400.375 470.184 520.302 310.592 420.050 350.300 510.093 460.283 260.277 430.249 330.426 350.615 360.299 260.504 440.367 380.832 420.191 46
SphereSeg0.357 430.651 410.411 450.345 380.264 380.630 350.059 300.289 540.212 280.240 360.336 360.158 460.305 440.557 380.159 500.455 510.341 440.726 510.294 28
3D-MPA0.355 440.457 560.484 400.299 430.277 350.591 430.047 360.332 450.212 290.217 410.278 420.193 420.413 370.410 510.195 420.574 330.352 400.849 340.213 44
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF0.353 450.593 460.511 340.375 340.264 370.597 410.008 500.332 460.160 350.229 390.274 450.000 680.206 510.678 280.155 510.485 470.422 300.816 440.254 40
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
RWSeg0.348 460.475 530.456 440.320 420.275 360.476 490.020 460.491 280.056 540.212 440.320 380.261 310.302 450.520 420.182 450.557 370.285 530.867 290.197 45
GICN0.341 470.580 480.371 480.344 390.198 470.469 500.052 320.564 160.093 450.212 430.212 510.127 490.347 420.537 390.206 400.525 420.329 460.729 500.241 42
One_Thing_One_Clickpermissive0.326 480.472 540.361 490.232 490.183 490.555 450.000 610.498 270.038 560.195 480.226 500.362 190.168 550.469 490.251 350.553 380.335 450.846 370.117 56
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS0.320 490.679 350.352 500.334 400.229 430.436 510.025 410.412 380.058 520.161 550.240 480.085 510.262 460.496 460.187 440.467 490.328 470.775 450.231 43
Sparse R-CNN0.292 500.704 290.213 600.153 540.154 510.551 460.053 310.212 580.132 390.174 520.274 440.070 530.363 410.441 500.176 460.424 540.234 550.758 470.161 52
MTML0.282 510.577 490.380 460.182 530.107 570.430 520.001 580.422 360.057 530.179 510.162 540.070 540.229 490.511 450.161 480.491 460.313 480.650 570.162 50
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet0.262 520.667 360.335 510.067 610.123 550.427 530.022 420.280 560.058 510.216 420.211 520.039 570.142 570.519 430.106 580.338 580.310 500.721 520.138 53
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)
MASCpermissive0.254 530.463 550.249 590.113 550.167 500.412 550.000 600.374 430.073 480.173 530.243 470.130 480.228 500.368 530.160 490.356 560.208 560.711 530.136 54
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet0.253 540.519 510.324 540.251 470.137 540.345 600.031 390.419 370.069 490.162 540.131 560.052 550.202 530.338 550.147 530.301 610.303 520.651 560.178 49
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
SPG_WSIS0.251 550.380 580.274 570.289 440.144 520.413 540.000 610.311 490.065 500.113 570.130 570.029 600.204 520.388 520.108 570.459 500.311 490.769 460.127 55
SegGroup_inspermissive0.246 560.556 500.335 520.062 630.115 560.490 480.000 610.297 530.018 600.186 490.142 550.083 520.233 480.216 590.153 520.469 480.251 540.744 480.083 59
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst0.214 570.250 630.330 530.275 460.103 580.228 660.000 610.345 440.024 580.088 590.203 530.186 430.167 560.367 540.125 550.221 640.112 660.666 550.162 51
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone0.161 580.519 510.259 580.084 570.059 600.325 620.002 560.093 630.009 620.077 610.064 600.045 560.044 640.161 610.045 600.331 590.180 580.566 580.033 68
3D-SISpermissive0.161 580.407 570.155 650.068 600.043 640.346 590.001 570.134 600.005 630.088 580.106 590.037 580.135 590.321 560.028 640.339 570.116 650.466 610.093 58
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.158 600.356 590.173 630.113 560.140 530.359 560.012 490.023 660.039 550.134 560.123 580.008 640.089 600.149 620.117 560.221 630.128 630.563 590.094 57
Region-18class0.146 610.175 670.321 550.080 580.062 590.357 570.000 610.307 500.002 650.066 620.044 620.000 680.018 660.036 670.054 590.447 530.133 610.472 600.060 63
SemRegionNet-20cls0.121 620.296 610.203 610.071 590.058 610.349 580.000 610.150 590.019 590.054 640.034 650.017 630.052 620.042 660.013 670.209 650.183 570.371 620.057 64
3D-BEVIS0.117 630.250 630.308 560.020 670.009 690.269 650.006 530.008 670.029 570.037 670.014 680.003 660.036 650.147 630.042 620.381 550.118 640.362 630.069 62
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3Dcopyleft0.117 630.222 650.161 640.054 650.027 660.289 630.000 610.124 610.001 670.079 600.061 610.027 610.141 580.240 580.005 680.310 600.129 620.153 680.081 60
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp0.113 650.333 600.151 660.056 640.053 620.344 610.000 610.105 620.016 610.049 650.035 640.020 620.053 610.048 650.013 660.183 670.173 590.344 650.054 65
Sem_Recon_ins0.098 660.295 620.187 620.015 680.036 650.213 670.005 540.038 650.003 640.056 630.037 630.036 590.015 670.051 640.044 610.209 660.098 670.354 640.071 61
ASIS0.085 670.037 680.080 680.066 620.047 630.282 640.000 610.052 640.002 660.047 660.026 660.001 670.046 630.194 600.031 630.264 620.140 600.167 670.047 67
Sgpn_scannet0.049 680.023 690.134 670.031 660.013 680.144 680.006 510.008 680.000 680.028 680.017 670.003 650.009 690.000 680.021 650.122 680.095 680.175 660.054 66