Presenting the ScanNet200 Benchmark

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

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

ScanNet200 Benchmark

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




Method Infoavg iouhead ioucommon ioutail ioualarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefloorfolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwallwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 by
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
PonderV2 ScanNet2000.346 20.552 40.270 40.175 30.497 50.070 80.239 40.000 10.000 30.000 10.232 110.412 50.584 10.842 30.804 30.212 50.540 40.000 30.433 110.106 60.000 60.590 30.290 60.548 20.243 40.000 50.356 70.000 10.000 30.062 70.398 70.441 50.000 10.104 60.000 20.888 20.076 80.682 40.030 10.094 40.491 60.351 70.869 70.000 10.063 10.403 60.700 20.000 70.660 90.881 30.761 10.050 60.186 50.852 70.000 10.007 50.570 50.100 20.565 20.326 30.641 60.431 30.290 80.621 30.259 20.408 50.622 60.125 10.082 70.950 20.179 30.000 10.263 20.424 20.193 50.558 30.880 10.545 70.375 40.727 20.445 60.499 60.000 30.000 10.475 40.002 40.034 40.083 40.000 30.924 10.290 30.636 30.115 80.400 30.874 30.186 40.000 10.611 50.128 20.113 20.000 40.000 10.000 50.584 60.636 50.103 80.385 50.843 40.283 20.603 30.080 50.825 40.000 30.377 60.000 10.000 40.000 20.457 70.000 10.000 50.000 10.574 80.608 60.481 20.792 30.394 20.000 10.357 60.503 70.261 60.817 70.504 80.304 40.472 30.115 50.000 10.750 30.677 30.202 10.000 70.509 30.729 10.000 10.519 80.000 100.000 50.000 10.620 80.000 20.000 10.660 30.560 40.486 20.384 60.346 40.952 20.247 80.667 20.436 60.269 30.691 30.000 10.010 30.787 50.889 20.880 40.000 10.810 40.336 30.860 60.000 10.606 40.009 50.248 50.681 40.392 6
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
CeCo0.340 30.551 50.247 70.181 20.475 70.057 110.142 80.000 10.000 30.000 10.387 30.463 30.499 60.924 20.774 60.213 40.257 70.000 30.546 100.100 70.006 50.615 10.177 110.534 40.246 30.000 50.400 20.000 10.338 10.006 100.484 30.609 20.000 10.083 70.000 20.873 60.089 40.661 80.000 40.048 100.560 10.408 40.892 50.000 10.000 20.586 10.616 50.000 70.692 60.900 20.721 60.162 10.228 30.860 50.000 10.000 70.575 20.083 30.550 30.347 20.624 70.410 70.360 30.740 20.109 80.321 90.660 40.000 40.121 40.939 70.143 60.000 10.400 10.003 70.190 60.564 20.652 60.615 50.421 20.304 90.579 10.547 30.000 30.000 10.296 80.000 60.030 50.096 30.000 30.916 30.037 70.551 60.171 40.376 40.865 50.286 20.000 10.633 20.102 90.027 50.011 30.000 10.000 50.474 80.742 20.133 40.311 70.824 60.242 70.503 80.068 60.828 30.000 30.429 30.000 10.063 30.000 20.781 10.000 10.000 50.000 10.665 10.633 40.450 30.818 20.000 60.000 10.429 20.532 40.226 70.825 50.510 70.377 30.709 10.079 80.000 10.753 20.683 20.102 100.063 30.401 100.620 80.000 10.619 20.000 100.000 50.000 10.595 90.000 20.000 10.345 80.564 30.411 40.603 10.384 30.945 40.266 60.643 30.367 80.304 10.663 60.000 10.010 30.726 90.767 60.898 30.000 10.784 70.435 10.861 50.000 10.447 60.000 90.257 40.656 70.377 7
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
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
AWCS0.305 80.508 80.225 80.142 50.463 80.063 90.195 60.000 10.000 30.000 10.467 20.551 10.504 50.773 50.764 80.142 80.029 110.000 30.626 80.100 70.000 60.360 80.179 90.507 90.137 90.006 40.300 80.000 10.000 30.172 50.364 90.512 40.000 10.056 80.000 20.865 80.093 30.634 110.000 40.071 80.396 90.296 100.876 60.000 10.000 20.373 80.436 100.063 60.749 10.877 40.721 60.131 30.124 80.804 90.000 10.000 70.515 70.010 60.452 70.252 60.578 80.417 50.179 110.484 60.171 40.337 80.606 80.000 40.115 50.937 80.142 70.000 10.008 60.000 90.157 100.484 80.402 110.501 90.339 60.553 30.529 20.478 80.000 30.000 10.404 60.001 50.022 70.077 50.000 30.894 80.219 40.628 40.093 90.305 80.886 10.233 30.000 10.603 60.112 40.023 60.000 40.000 10.000 50.741 20.664 40.097 90.253 80.782 80.264 50.523 70.154 10.707 100.000 30.411 40.000 10.000 40.000 20.332 100.000 10.000 50.000 10.602 30.595 70.185 90.656 100.159 30.000 10.355 70.424 90.154 90.729 90.516 60.220 60.620 20.084 70.000 10.707 80.651 70.173 20.014 60.381 110.582 90.000 10.619 20.049 80.000 50.000 10.702 20.000 20.000 10.302 100.489 90.317 70.334 70.392 20.922 80.254 70.533 80.394 70.129 110.613 90.000 10.000 80.820 20.649 80.749 80.000 10.782 80.282 50.863 40.000 10.288 100.006 60.220 70.633 80.542 2
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.520 10.109 20.108 100.000 10.337 10.000 10.310 90.394 60.494 80.753 70.848 10.256 20.717 20.000 30.842 10.192 20.065 20.449 50.346 10.546 30.190 70.000 50.384 40.000 10.000 30.218 10.505 10.791 10.000 10.136 10.000 20.903 10.073 90.687 30.000 40.168 10.551 20.387 50.941 10.000 10.000 20.397 70.654 30.000 70.714 30.759 90.752 40.118 40.264 20.926 10.000 10.048 20.575 20.000 70.597 10.366 10.755 10.469 10.474 10.798 10.140 60.617 10.692 30.000 40.592 20.971 10.188 20.000 10.133 40.593 10.349 10.650 10.717 40.699 10.455 10.790 10.523 30.636 10.301 10.000 10.622 20.000 60.017 90.259 10.000 30.921 20.337 10.733 10.210 10.514 10.860 60.407 10.000 10.688 10.109 60.000 90.000 40.000 10.151 10.671 40.782 10.115 70.641 10.903 10.349 10.616 10.088 40.832 20.000 30.480 10.000 10.428 10.000 20.497 60.000 10.000 50.000 10.662 20.690 10.612 10.828 10.575 10.000 10.404 40.644 10.325 30.887 20.728 10.009 100.134 50.026 110.000 10.761 10.731 10.172 30.077 20.528 20.727 20.000 10.603 40.220 20.022 20.000 10.740 10.000 20.000 10.661 10.586 10.566 10.436 40.531 10.978 10.457 10.708 10.583 30.141 70.748 10.000 10.026 10.822 10.871 30.879 50.000 10.851 10.405 20.914 10.000 10.682 20.000 90.281 10.738 10.463 4
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
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
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


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




Method Infoavg ap 25%head ap 25%common ap 25%tail ap 25%alarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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Mask3D Scannet2000.445 10.653 10.392 10.254 10.648 10.097 10.125 50.000 10.000 10.000 10.657 10.971 10.451 21.000 11.000 10.640 10.500 10.045 11.000 10.241 20.409 10.363 10.440 10.686 30.300 10.000 10.201 10.000 10.009 10.290 10.556 11.000 10.000 10.063 30.000 10.830 10.573 10.844 20.333 10.204 10.058 50.158 50.552 20.056 10.000 11.000 10.725 40.750 10.927 11.000 10.888 40.042 30.120 20.615 40.226 10.250 10.890 10.792 10.677 20.510 20.818 10.699 10.512 20.167 50.125 10.315 20.943 10.309 10.017 30.200 30.000 10.188 10.000 10.183 30.815 11.000 10.827 10.741 10.442 30.414 40.600 10.000 10.000 10.458 10.049 30.321 10.381 10.000 10.908 20.400 10.841 10.260 10.710 10.966 10.265 10.000 10.924 10.152 10.025 20.500 10.027 10.028 11.000 10.556 50.016 10.080 50.500 10.694 30.608 10.084 10.604 30.194 10.538 30.000 10.500 10.000 20.354 40.000 11.000 10.000 10.761 20.930 10.053 40.890 31.000 10.008 20.262 10.358 21.000 11.000 10.792 40.966 11.000 10.765 20.004 20.930 10.780 20.330 20.027 20.625 10.974 40.050 10.412 50.021 20.000 30.000 20.778 10.000 10.000 10.493 20.746 20.454 10.335 20.396 10.930 50.551 21.000 10.552 10.606 10.853 10.000 10.004 10.806 11.000 10.727 20.000 10.042 30.745 20.000 10.399 40.391 10.630 10.721 10.619 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
TD3D Scannet200permissive0.379 20.603 20.306 20.190 20.635 20.073 20.500 10.000 10.000 10.000 10.495 30.735 20.275 51.000 10.979 20.590 20.000 40.021 20.000 30.146 30.000 20.356 20.173 50.795 10.226 20.000 10.173 20.000 10.000 20.226 20.390 20.000 20.000 10.250 10.000 10.706 20.061 30.885 10.093 20.186 20.259 40.200 10.667 10.000 20.000 10.667 20.825 10.250 40.834 41.000 10.958 10.553 10.111 30.748 10.220 20.051 20.866 20.792 10.390 50.045 50.800 20.302 50.517 10.533 30.113 20.427 10.843 20.000 20.458 10.600 10.000 10.101 20.000 10.259 10.717 20.500 20.615 20.520 20.526 20.457 10.270 40.000 10.000 10.400 20.088 20.294 20.181 20.000 11.000 10.400 10.710 50.103 30.477 50.905 20.061 20.000 10.906 20.102 20.232 10.125 20.000 20.003 20.792 31.000 10.000 20.102 30.125 40.559 50.523 30.075 20.715 10.000 20.424 50.000 10.396 20.250 10.638 10.000 10.000 20.000 10.622 50.833 20.221 10.970 10.250 20.038 10.260 20.415 10.125 21.000 11.000 10.857 20.000 20.908 10.012 10.869 30.836 10.635 10.111 10.625 11.000 10.020 20.510 10.003 30.009 21.000 10.778 10.000 10.000 10.370 30.755 10.288 20.333 30.274 21.000 10.557 10.731 20.456 20.433 30.769 50.000 10.000 20.621 41.000 10.458 40.000 10.196 20.817 10.000 10.472 10.222 30.205 50.689 20.274 3
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Minkowski 34D Inst.permissive0.280 40.488 40.192 50.124 40.593 40.010 40.500 10.000 10.000 10.000 10.447 40.535 40.445 31.000 10.861 40.400 30.225 20.000 30.000 30.142 40.000 20.074 40.342 30.467 50.067 30.000 10.119 50.000 10.000 20.000 40.337 50.000 20.000 10.000 40.000 10.506 50.070 20.804 40.000 30.000 40.333 30.172 30.150 50.000 20.000 10.479 50.745 30.000 50.830 51.000 10.904 30.167 20.090 40.732 20.000 30.000 30.443 40.000 30.500 30.542 10.772 50.396 40.077 50.385 40.044 40.118 50.777 40.000 20.000 40.200 30.000 10.000 30.000 10.148 40.502 40.500 20.419 40.159 50.281 40.404 50.317 30.000 10.000 10.200 30.000 40.077 30.000 30.000 10.750 30.200 30.715 40.021 40.551 20.828 50.000 30.000 10.743 40.059 50.000 30.000 30.000 20.000 30.125 50.648 30.000 20.191 20.500 10.669 40.502 40.000 50.568 40.000 20.516 40.000 10.000 30.000 20.305 50.000 10.000 20.000 10.825 10.833 20.021 50.918 20.000 30.000 30.191 40.346 40.100 40.981 31.000 10.286 40.000 20.000 50.000 30.868 40.648 50.292 30.000 30.375 31.000 10.000 30.500 20.000 40.333 10.000 20.538 50.000 10.000 10.213 50.518 40.098 40.528 10.250 30.997 30.284 50.677 30.398 30.167 40.790 40.000 10.000 20.618 50.903 50.200 50.000 10.333 10.333 40.000 10.442 30.083 40.213 40.587 40.131 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
LGround Inst.permissive0.314 30.529 30.225 30.155 30.578 50.010 30.500 10.000 10.000 10.000 10.515 20.556 30.696 11.000 10.927 30.400 30.083 30.000 31.000 10.252 10.000 20.167 30.350 20.731 20.067 30.000 10.123 40.000 10.000 20.036 30.372 30.000 20.000 10.250 10.000 10.569 40.031 50.810 30.000 30.000 40.630 10.183 20.278 30.000 20.000 10.582 40.589 50.500 20.863 31.000 10.940 20.000 40.144 10.716 30.000 30.000 30.484 30.000 30.500 30.400 30.798 30.500 20.278 40.750 10.093 30.166 40.783 30.000 20.200 20.400 20.000 10.000 30.000 10.219 20.539 30.500 20.578 30.413 30.181 50.457 20.375 20.000 10.000 10.050 50.000 40.077 40.000 30.000 10.500 50.000 50.743 30.250 20.488 40.846 30.000 30.000 10.800 30.069 30.000 30.000 30.000 20.000 31.000 10.607 40.000 20.200 10.500 10.694 20.528 20.063 30.659 20.000 20.594 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.716 40.647 50.221 20.857 40.000 30.000 30.217 30.346 30.071 50.530 51.000 10.429 30.000 20.286 30.000 30.826 50.706 30.208 40.000 30.250 40.744 50.000 30.500 20.042 10.000 30.000 20.746 30.000 10.000 10.517 10.625 30.085 50.333 30.000 41.000 10.378 40.533 50.376 40.042 50.814 30.000 10.000 20.765 31.000 10.600 30.000 10.000 40.667 30.000 10.472 10.333 20.337 30.605 30.305 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.275 50.466 50.218 40.110 50.625 30.007 50.500 10.000 10.000 10.000 10.000 50.222 50.377 41.000 10.661 50.400 30.000 40.000 30.000 30.119 50.000 20.000 50.277 40.685 40.067 30.000 10.132 30.000 10.000 20.000 40.367 40.000 20.000 10.000 40.000 10.591 30.055 40.783 50.000 30.014 30.500 20.161 40.278 30.000 20.000 10.667 20.768 20.500 20.866 21.000 10.829 50.000 40.019 50.555 50.000 30.000 30.305 50.000 30.750 10.200 40.783 40.429 30.395 30.677 20.020 50.286 30.584 50.000 20.000 40.115 50.000 10.000 30.000 10.145 50.423 50.500 20.364 50.369 40.571 10.448 30.206 50.000 10.000 10.200 30.106 10.065 50.000 30.000 10.750 30.200 30.774 20.000 50.501 30.841 40.000 30.000 10.692 50.063 40.000 30.000 30.000 20.000 30.500 40.649 20.000 20.084 40.125 40.719 10.413 50.004 40.450 50.000 20.638 10.000 10.000 30.000 20.505 30.000 10.000 20.000 10.727 30.833 20.221 20.779 50.000 30.000 30.168 50.311 50.125 20.571 40.500 50.143 50.000 20.250 40.000 30.869 20.667 40.162 50.000 30.250 41.000 10.000 30.500 20.000 40.000 30.000 20.689 40.000 10.000 10.312 40.383 50.114 30.333 30.000 40.997 30.420 30.613 40.212 50.500 20.819 20.000 10.000 20.768 21.000 10.918 10.000 10.000 40.278 50.000 10.333 50.000 50.353 20.546 50.258 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


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 30.813 170.851 70.782 50.890 20.597 10.916 20.696 70.713 30.979 10.635 10.384 20.793 20.907 70.821 40.790 300.696 100.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
PonderV20.785 20.978 10.800 250.833 210.788 30.853 150.545 160.910 50.713 10.705 40.979 10.596 60.390 10.769 110.832 400.821 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 10.843 150.781 60.858 110.575 60.831 310.685 130.714 20.979 10.594 70.310 260.801 10.892 150.841 20.819 40.723 40.940 130.887 60.725 22
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 130.836 180.790 20.875 40.576 50.905 60.704 40.739 10.969 100.611 20.349 100.756 200.958 10.702 430.805 140.708 70.916 310.898 30.801 2
TTT-KD0.773 50.646 890.818 130.809 330.774 80.878 30.581 20.943 10.687 110.704 50.978 40.607 50.336 150.775 80.912 50.838 30.823 20.694 110.967 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 90.840 290.564 100.900 80.686 120.677 110.961 160.537 290.348 110.769 110.903 90.785 100.815 60.676 200.939 140.880 110.772 8
OctFormerpermissive0.766 70.925 70.808 210.849 90.786 40.846 250.566 90.876 140.690 90.674 130.960 170.576 160.226 650.753 220.904 80.777 120.815 60.722 50.923 270.877 130.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint0.766 70.932 50.794 310.829 230.751 210.854 130.540 200.903 70.630 320.672 140.963 140.565 200.357 80.788 30.900 110.737 250.802 150.685 150.950 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
OccuSeg+Semantic0.764 90.758 570.796 290.839 170.746 230.907 10.562 110.850 230.680 150.672 140.978 40.610 30.335 170.777 60.819 430.847 10.830 10.691 130.972 20.885 80.727 20
CU-Hybrid Net0.764 90.924 80.819 110.840 160.757 160.853 150.580 30.848 240.709 30.643 220.958 200.587 110.295 320.753 220.884 190.758 190.815 60.725 30.927 240.867 200.743 14
O-CNNpermissive0.762 110.924 80.823 70.844 140.770 100.852 170.577 40.847 260.711 20.640 260.958 200.592 80.217 710.762 160.888 160.758 190.813 100.726 20.932 220.868 190.744 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
OA-CNN-L_ScanNet200.756 120.783 430.826 50.858 40.776 70.837 320.548 150.896 110.649 240.675 120.962 150.586 120.335 170.771 100.802 470.770 150.787 320.691 130.936 170.880 110.761 10
ConDaFormer0.755 130.927 60.822 80.836 180.801 10.849 200.516 300.864 200.651 230.680 100.958 200.584 140.282 400.759 180.855 300.728 270.802 150.678 170.880 570.873 180.756 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
PNE0.755 130.786 410.835 40.834 200.758 140.849 200.570 80.836 300.648 250.668 160.978 40.581 150.367 60.683 330.856 280.804 60.801 190.678 170.961 50.889 50.716 27
P. Hermosilla: Point Neighborhood Embeddings.
PointTransformerV20.752 150.742 650.809 200.872 10.758 140.860 100.552 130.891 120.610 390.687 60.960 170.559 230.304 290.766 140.926 30.767 160.797 220.644 310.942 110.876 160.722 24
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 150.906 120.793 330.802 390.689 380.825 440.556 120.867 160.681 140.602 420.960 170.555 250.365 70.779 50.859 250.747 220.795 260.717 60.917 300.856 280.764 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
PointConvFormer0.749 170.793 390.790 340.807 350.750 220.856 120.524 260.881 130.588 510.642 250.977 80.591 90.274 450.781 40.929 20.804 60.796 230.642 320.947 90.885 80.715 28
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 170.909 100.818 130.811 310.752 190.839 310.485 450.842 270.673 160.644 210.957 240.528 350.305 280.773 90.859 250.788 80.818 50.693 120.916 310.856 280.723 23
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 190.623 920.804 230.859 30.745 240.824 460.501 350.912 40.690 90.685 80.956 250.567 190.320 230.768 130.918 40.720 320.802 150.676 200.921 280.881 100.779 6
StratifiedFormerpermissive0.747 200.901 130.803 240.845 130.757 160.846 250.512 310.825 340.696 70.645 200.956 250.576 160.262 560.744 270.861 240.742 230.770 410.705 80.899 430.860 250.734 15
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 210.771 510.819 110.848 110.702 350.865 90.397 830.899 90.699 50.664 170.948 530.588 100.330 190.746 260.851 340.764 170.796 230.704 90.935 180.866 210.728 18
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 210.870 180.838 20.858 40.729 290.850 190.501 350.874 150.587 520.658 180.956 250.564 210.299 300.765 150.900 110.716 350.812 110.631 370.939 140.858 260.709 29
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)
Retro-FPN0.744 230.842 260.800 250.767 530.740 250.836 340.541 180.914 30.672 170.626 300.958 200.552 260.272 470.777 60.886 180.696 440.801 190.674 230.941 120.858 260.717 25
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-Net0.743 240.620 930.799 280.849 90.730 280.822 480.493 420.897 100.664 180.681 90.955 280.562 220.378 30.760 170.903 90.738 240.801 190.673 240.907 350.877 130.745 12
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 250.816 340.806 220.807 350.752 190.828 420.575 60.839 290.699 50.637 270.954 340.520 380.320 230.755 210.834 380.760 180.772 380.676 200.915 330.862 230.717 25
SAT0.742 250.860 210.765 470.819 260.769 110.848 220.533 220.829 320.663 190.631 290.955 280.586 120.274 450.753 220.896 130.729 260.760 480.666 260.921 280.855 300.733 16
LargeKernel3D0.739 270.909 100.820 100.806 370.740 250.852 170.545 160.826 330.594 500.643 220.955 280.541 280.263 550.723 310.858 270.775 140.767 420.678 170.933 200.848 350.694 34
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 280.776 470.790 340.851 70.754 180.854 130.491 440.866 180.596 490.686 70.955 280.536 300.342 130.624 480.869 210.787 90.802 150.628 380.927 240.875 170.704 31
MinkowskiNetpermissive0.736 280.859 220.818 130.832 220.709 330.840 290.521 280.853 220.660 210.643 220.951 430.544 270.286 380.731 290.893 140.675 530.772 380.683 160.874 640.852 330.727 20
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 300.890 140.837 30.864 20.726 300.873 50.530 250.824 350.489 850.647 190.978 40.609 40.336 150.624 480.733 560.758 190.776 360.570 630.949 80.877 130.728 18
SparseConvNet0.725 310.647 880.821 90.846 120.721 310.869 60.533 220.754 560.603 450.614 340.955 280.572 180.325 210.710 320.870 200.724 300.823 20.628 380.934 190.865 220.683 37
PointTransformer++0.725 310.727 730.811 190.819 260.765 120.841 280.502 340.814 400.621 350.623 320.955 280.556 240.284 390.620 500.866 220.781 110.757 520.648 290.932 220.862 230.709 29
MatchingNet0.724 330.812 360.812 180.810 320.735 270.834 360.495 410.860 210.572 590.602 420.954 340.512 400.280 420.757 190.845 360.725 290.780 340.606 480.937 160.851 340.700 33
INS-Conv-semantic0.717 340.751 600.759 500.812 300.704 340.868 70.537 210.842 270.609 410.608 380.953 370.534 320.293 330.616 510.864 230.719 340.793 270.640 330.933 200.845 390.663 43
PointMetaBase0.714 350.835 270.785 360.821 240.684 400.846 250.531 240.865 190.614 360.596 460.953 370.500 430.246 610.674 340.888 160.692 450.764 440.624 400.849 790.844 400.675 39
contrastBoundarypermissive0.705 360.769 540.775 410.809 330.687 390.820 510.439 710.812 410.661 200.591 480.945 610.515 390.171 890.633 450.856 280.720 320.796 230.668 250.889 500.847 360.689 35
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 370.774 490.800 250.793 440.760 130.847 240.471 490.802 440.463 920.634 280.968 120.491 460.271 490.726 300.910 60.706 390.815 60.551 750.878 580.833 410.570 75
RFCR0.702 380.889 150.745 610.813 290.672 430.818 550.493 420.815 390.623 330.610 360.947 550.470 550.249 600.594 540.848 350.705 400.779 350.646 300.892 480.823 470.611 58
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 Click0.701 390.825 310.796 290.723 600.716 320.832 380.433 730.816 370.634 300.609 370.969 100.418 810.344 120.559 660.833 390.715 360.808 130.560 690.902 400.847 360.680 38
JSENetpermissive0.699 400.881 170.762 480.821 240.667 440.800 670.522 270.792 470.613 370.607 390.935 810.492 450.205 760.576 590.853 320.691 470.758 500.652 280.872 670.828 440.649 47
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-Click0.693 410.743 640.794 310.655 830.684 400.822 480.497 400.719 660.622 340.617 330.977 80.447 680.339 140.750 250.664 720.703 420.790 300.596 530.946 100.855 300.647 48
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 420.732 690.772 420.786 450.677 420.866 80.517 290.848 240.509 780.626 300.952 410.536 300.225 670.545 720.704 630.689 500.810 120.564 680.903 390.854 320.729 17
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 430.884 160.754 540.795 420.647 510.818 550.422 750.802 440.612 380.604 400.945 610.462 580.189 840.563 650.853 320.726 280.765 430.632 360.904 370.821 500.606 62
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 440.704 780.741 650.754 570.656 460.829 400.501 350.741 610.609 410.548 560.950 470.522 370.371 40.633 450.756 510.715 360.771 400.623 410.861 750.814 530.658 44
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 450.866 190.748 580.819 260.645 530.794 700.450 610.802 440.587 520.604 400.945 610.464 570.201 790.554 680.840 370.723 310.732 620.602 510.907 350.822 490.603 65
KP-FCNN0.684 460.847 250.758 520.784 470.647 510.814 580.473 480.772 500.605 430.594 470.935 810.450 660.181 870.587 550.805 460.690 480.785 330.614 440.882 540.819 510.632 54
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 460.728 720.757 530.776 500.690 360.804 650.464 540.816 370.577 580.587 490.945 610.508 420.276 440.671 350.710 610.663 580.750 560.589 580.881 550.832 430.653 46
DGNet0.684 460.712 770.784 370.782 490.658 450.835 350.499 390.823 360.641 270.597 450.950 470.487 480.281 410.575 600.619 760.647 660.764 440.620 430.871 700.846 380.688 36
Superpoint Network0.683 490.851 240.728 690.800 410.653 480.806 630.468 510.804 420.572 590.602 420.946 580.453 650.239 640.519 770.822 410.689 500.762 470.595 550.895 460.827 450.630 55
PointContrast_LA_SEM0.683 490.757 580.784 370.786 450.639 550.824 460.408 780.775 490.604 440.541 580.934 850.532 330.269 510.552 690.777 490.645 690.793 270.640 330.913 340.824 460.671 40
VI-PointConv0.676 510.770 530.754 540.783 480.621 590.814 580.552 130.758 540.571 610.557 540.954 340.529 340.268 530.530 750.682 670.675 530.719 650.603 500.888 510.833 410.665 42
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 520.789 400.748 580.763 550.635 570.814 580.407 800.747 580.581 560.573 510.950 470.484 490.271 490.607 520.754 520.649 630.774 370.596 530.883 530.823 470.606 62
SALANet0.670 530.816 340.770 450.768 520.652 490.807 620.451 580.747 580.659 220.545 570.924 910.473 540.149 990.571 620.811 450.635 720.746 570.623 410.892 480.794 660.570 75
O3DSeg0.668 540.822 320.771 440.496 1030.651 500.833 370.541 180.761 530.555 670.611 350.966 130.489 470.370 50.388 970.580 790.776 130.751 540.570 630.956 60.817 520.646 49
PointConvpermissive0.666 550.781 440.759 500.699 680.644 540.822 480.475 470.779 480.564 640.504 740.953 370.428 750.203 780.586 570.754 520.661 590.753 530.588 590.902 400.813 550.642 50
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 550.703 790.781 390.751 590.655 470.830 390.471 490.769 510.474 880.537 600.951 430.475 530.279 430.635 430.698 660.675 530.751 540.553 740.816 860.806 570.703 32
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++permissive0.663 570.746 620.708 720.722 610.638 560.820 510.451 580.566 940.599 470.541 580.950 470.510 410.313 250.648 400.819 430.616 770.682 800.590 570.869 710.810 560.656 45
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 580.558 1000.751 560.655 830.690 360.722 920.453 570.867 160.579 570.576 500.893 1030.523 360.293 330.733 280.571 810.692 450.659 870.606 480.875 610.804 590.668 41
DCM-Net0.658 580.778 450.702 750.806 370.619 600.813 610.468 510.693 740.494 810.524 660.941 730.449 670.298 310.510 790.821 420.675 530.727 640.568 660.826 840.803 600.637 52
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 600.698 810.743 630.650 850.564 770.820 510.505 330.758 540.631 310.479 780.945 610.480 510.226 650.572 610.774 500.690 480.735 600.614 440.853 780.776 810.597 68
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 610.752 590.734 670.664 810.583 720.815 570.399 820.754 560.639 280.535 620.942 710.470 550.309 270.665 360.539 830.650 620.708 700.635 350.857 770.793 680.642 50
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 620.778 450.731 680.699 680.577 730.829 400.446 630.736 620.477 870.523 680.945 610.454 620.269 510.484 870.749 550.618 750.738 580.599 520.827 830.792 710.621 57
PointConv-SFPN0.641 630.776 470.703 740.721 620.557 800.826 430.451 580.672 790.563 650.483 770.943 700.425 780.162 940.644 410.726 570.659 600.709 690.572 620.875 610.786 760.559 81
MVPNetpermissive0.641 630.831 280.715 700.671 780.590 680.781 760.394 840.679 760.642 260.553 550.937 780.462 580.256 570.649 390.406 970.626 730.691 770.666 260.877 590.792 710.608 61
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 650.717 760.701 760.692 710.576 740.801 660.467 530.716 670.563 650.459 840.953 370.429 740.169 910.581 580.854 310.605 780.710 670.550 760.894 470.793 680.575 73
FPConvpermissive0.639 660.785 420.760 490.713 660.603 630.798 680.392 850.534 990.603 450.524 660.948 530.457 600.250 590.538 730.723 590.598 820.696 750.614 440.872 670.799 610.567 78
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 670.797 380.769 460.641 910.590 680.820 510.461 550.537 980.637 290.536 610.947 550.388 880.206 750.656 370.668 700.647 660.732 620.585 600.868 720.793 680.473 101
PointSPNet0.637 680.734 680.692 830.714 650.576 740.797 690.446 630.743 600.598 480.437 890.942 710.403 840.150 980.626 470.800 480.649 630.697 740.557 720.846 800.777 800.563 79
SConv0.636 690.830 290.697 790.752 580.572 760.780 780.445 650.716 670.529 710.530 630.951 430.446 690.170 900.507 820.666 710.636 710.682 800.541 820.886 520.799 610.594 69
Supervoxel-CNN0.635 700.656 860.711 710.719 630.613 610.757 870.444 680.765 520.534 700.566 520.928 890.478 520.272 470.636 420.531 850.664 570.645 910.508 890.864 740.792 710.611 58
joint point-basedpermissive0.634 710.614 940.778 400.667 800.633 580.825 440.420 760.804 420.467 900.561 530.951 430.494 440.291 350.566 630.458 920.579 880.764 440.559 710.838 810.814 530.598 67
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 720.731 700.688 860.675 750.591 670.784 750.444 680.565 950.610 390.492 750.949 510.456 610.254 580.587 550.706 620.599 810.665 860.612 470.868 720.791 740.579 72
3DSM_DMMF0.631 730.626 910.745 610.801 400.607 620.751 880.506 320.729 650.565 630.491 760.866 1060.434 700.197 820.595 530.630 750.709 380.705 720.560 690.875 610.740 910.491 96
PointNet2-SFPN0.631 730.771 510.692 830.672 760.524 850.837 320.440 700.706 720.538 690.446 860.944 670.421 800.219 700.552 690.751 540.591 840.737 590.543 810.901 420.768 830.557 82
APCF-Net0.631 730.742 650.687 880.672 760.557 800.792 730.408 780.665 800.545 680.508 710.952 410.428 750.186 850.634 440.702 640.620 740.706 710.555 730.873 650.798 630.581 71
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 760.604 960.741 650.766 540.590 680.747 890.501 350.734 630.503 800.527 640.919 950.454 620.323 220.550 710.420 960.678 520.688 780.544 790.896 450.795 650.627 56
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 770.800 370.625 990.719 630.545 820.806 630.445 650.597 880.448 950.519 690.938 770.481 500.328 200.489 860.499 900.657 610.759 490.592 560.881 550.797 640.634 53
SegGroup_sempermissive0.627 780.818 330.747 600.701 670.602 640.764 840.385 890.629 850.490 830.508 710.931 880.409 830.201 790.564 640.725 580.618 750.692 760.539 830.873 650.794 660.548 85
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 790.830 290.694 810.757 560.563 780.772 820.448 620.647 830.520 740.509 700.949 510.431 730.191 830.496 840.614 770.647 660.672 840.535 850.876 600.783 770.571 74
dtc_net0.625 790.703 790.751 560.794 430.535 830.848 220.480 460.676 780.528 720.469 810.944 670.454 620.004 1120.464 890.636 740.704 410.758 500.548 780.924 260.787 750.492 95
HPEIN0.618 810.729 710.668 890.647 870.597 660.766 830.414 770.680 750.520 740.525 650.946 580.432 710.215 720.493 850.599 780.638 700.617 960.570 630.897 440.806 570.605 64
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-GCNpermissive0.610 820.858 230.772 420.489 1040.532 840.792 730.404 810.643 840.570 620.507 730.935 810.414 820.046 1090.510 790.702 640.602 800.705 720.549 770.859 760.773 820.534 88
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 830.760 560.667 900.649 860.521 860.793 710.457 560.648 820.528 720.434 910.947 550.401 850.153 970.454 900.721 600.648 650.717 660.536 840.904 370.765 840.485 97
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 840.634 900.743 630.697 700.601 650.781 760.437 720.585 910.493 820.446 860.933 860.394 860.011 1110.654 380.661 730.603 790.733 610.526 860.832 820.761 860.480 98
LAP-D0.594 850.720 740.692 830.637 920.456 960.773 810.391 870.730 640.587 520.445 880.940 750.381 890.288 360.434 930.453 940.591 840.649 890.581 610.777 900.749 900.610 60
DPC0.592 860.720 740.700 770.602 960.480 920.762 860.380 900.713 700.585 550.437 890.940 750.369 910.288 360.434 930.509 890.590 860.639 940.567 670.772 920.755 880.592 70
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 870.766 550.659 940.683 730.470 950.740 910.387 880.620 870.490 830.476 790.922 930.355 940.245 620.511 780.511 880.571 890.643 920.493 930.872 670.762 850.600 66
ROSMRF0.580 880.772 500.707 730.681 740.563 780.764 840.362 920.515 1000.465 910.465 830.936 800.427 770.207 740.438 910.577 800.536 920.675 830.486 940.723 980.779 780.524 91
SD-DETR0.576 890.746 620.609 1030.445 1080.517 870.643 1030.366 910.714 690.456 930.468 820.870 1050.432 710.264 540.558 670.674 680.586 870.688 780.482 950.739 960.733 930.537 87
SQN_0.1%0.569 900.676 830.696 800.657 820.497 880.779 790.424 740.548 960.515 760.376 960.902 1020.422 790.357 80.379 980.456 930.596 830.659 870.544 790.685 1010.665 1040.556 83
TextureNetpermissive0.566 910.672 850.664 910.671 780.494 900.719 930.445 650.678 770.411 1010.396 940.935 810.356 930.225 670.412 950.535 840.565 900.636 950.464 970.794 890.680 1010.568 77
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
DVVNet0.562 920.648 870.700 770.770 510.586 710.687 970.333 960.650 810.514 770.475 800.906 990.359 920.223 690.340 1000.442 950.422 1030.668 850.501 900.708 990.779 780.534 88
Pointnet++ & Featurepermissive0.557 930.735 670.661 930.686 720.491 910.744 900.392 850.539 970.451 940.375 970.946 580.376 900.205 760.403 960.356 1000.553 910.643 920.497 910.824 850.756 870.515 92
GMLPs0.538 940.495 1050.693 820.647 870.471 940.793 710.300 990.477 1010.505 790.358 990.903 1010.327 970.081 1060.472 880.529 860.448 1010.710 670.509 870.746 940.737 920.554 84
PanopticFusion-label0.529 950.491 1060.688 860.604 950.386 1010.632 1040.225 1090.705 730.434 980.293 1050.815 1070.348 950.241 630.499 830.669 690.507 940.649 890.442 1030.796 880.602 1080.561 80
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_weak0.516 960.676 830.591 1060.609 930.442 970.774 800.335 950.597 880.422 1000.357 1000.932 870.341 960.094 1050.298 1020.528 870.473 990.676 820.495 920.602 1070.721 960.349 108
Online SegFusion0.515 970.607 950.644 970.579 980.434 980.630 1050.353 930.628 860.440 960.410 920.762 1110.307 990.167 920.520 760.403 980.516 930.565 990.447 1010.678 1020.701 980.514 93
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, FTSDF0.501 980.558 1000.608 1040.424 1100.478 930.690 960.246 1050.586 900.468 890.450 850.911 970.394 860.160 950.438 910.212 1070.432 1020.541 1050.475 960.742 950.727 940.477 99
PCNN0.498 990.559 990.644 970.560 1000.420 1000.711 950.229 1070.414 1020.436 970.352 1010.941 730.324 980.155 960.238 1070.387 990.493 950.529 1060.509 870.813 870.751 890.504 94
Weakly-Openseg v30.489 1000.749 610.664 910.646 890.496 890.559 1090.122 1120.577 920.257 1120.364 980.805 1080.198 1100.096 1040.510 790.496 910.361 1070.563 1000.359 1100.777 900.644 1050.532 90
3DMV0.484 1010.484 1070.538 1080.643 900.424 990.606 1080.310 970.574 930.433 990.378 950.796 1090.301 1000.214 730.537 740.208 1080.472 1000.507 1090.413 1060.693 1000.602 1080.539 86
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1020.577 980.611 1020.356 1120.321 1090.715 940.299 1010.376 1060.328 1080.319 1030.944 670.285 1020.164 930.216 1100.229 1050.484 970.545 1040.456 990.755 930.709 970.475 100
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1030.679 820.604 1050.578 990.380 1020.682 980.291 1020.106 1120.483 860.258 1100.920 940.258 1060.025 1100.231 1090.325 1010.480 980.560 1020.463 980.725 970.666 1030.231 112
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1040.474 1080.623 1000.463 1060.366 1040.651 1010.310 970.389 1050.349 1060.330 1020.937 780.271 1040.126 1010.285 1030.224 1060.350 1090.577 980.445 1020.625 1050.723 950.394 104
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 1050.548 1020.548 1070.597 970.363 1050.628 1060.300 990.292 1070.374 1030.307 1040.881 1040.268 1050.186 850.238 1070.204 1090.407 1040.506 1100.449 1000.667 1030.620 1070.462 102
SurfaceConvPF0.442 1050.505 1040.622 1010.380 1110.342 1070.654 1000.227 1080.397 1040.367 1040.276 1070.924 910.240 1070.198 810.359 990.262 1030.366 1050.581 970.435 1040.640 1040.668 1020.398 103
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1070.437 1100.646 960.474 1050.369 1030.645 1020.353 930.258 1090.282 1100.279 1060.918 960.298 1010.147 1000.283 1040.294 1020.487 960.562 1010.427 1050.619 1060.633 1060.352 107
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1080.525 1030.647 950.522 1010.324 1080.488 1120.077 1130.712 710.353 1050.401 930.636 1130.281 1030.176 880.340 1000.565 820.175 1130.551 1030.398 1070.370 1130.602 1080.361 106
SPLAT Netcopyleft0.393 1090.472 1090.511 1090.606 940.311 1100.656 990.245 1060.405 1030.328 1080.197 1110.927 900.227 1090.000 1140.001 1140.249 1040.271 1120.510 1070.383 1090.593 1080.699 990.267 110
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 1100.297 1120.491 1100.432 1090.358 1060.612 1070.274 1030.116 1110.411 1010.265 1080.904 1000.229 1080.079 1070.250 1050.185 1100.320 1100.510 1070.385 1080.548 1090.597 1110.394 104
PointNet++permissive0.339 1110.584 970.478 1110.458 1070.256 1120.360 1130.250 1040.247 1100.278 1110.261 1090.677 1120.183 1110.117 1020.212 1110.145 1120.364 1060.346 1130.232 1130.548 1090.523 1120.252 111
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 1120.353 1110.290 1130.278 1130.166 1130.553 1100.169 1110.286 1080.147 1130.148 1130.908 980.182 1120.064 1080.023 1130.018 1140.354 1080.363 1110.345 1110.546 1110.685 1000.278 109
ScanNetpermissive0.306 1130.203 1130.366 1120.501 1020.311 1100.524 1110.211 1100.002 1140.342 1070.189 1120.786 1100.145 1130.102 1030.245 1060.152 1110.318 1110.348 1120.300 1120.460 1120.437 1130.182 113
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 1140.000 1140.041 1140.172 1140.030 1140.062 1140.001 1140.035 1130.004 1140.051 1140.143 1140.019 1140.003 1130.041 1120.050 1130.003 1140.054 1140.018 1140.005 1140.264 1140.082 114


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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
OneFormer3Dcopyleft0.896 11.000 11.000 10.913 50.858 40.951 30.786 90.837 140.916 80.908 20.778 40.803 20.750 111.000 10.976 20.926 40.882 50.995 400.849 1
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception0.884 21.000 10.979 150.872 140.869 20.892 200.806 60.890 50.835 220.892 40.755 100.811 10.779 80.955 400.951 30.876 190.914 10.997 340.840 2
TST3D0.879 31.000 10.994 50.921 40.807 150.939 80.771 100.887 60.923 60.862 100.722 150.768 70.756 101.000 10.910 220.904 60.836 190.999 330.824 5
Spherical Mask(CtoF)0.875 41.000 10.991 100.873 130.850 50.946 50.691 190.752 280.926 40.889 60.759 80.794 40.820 21.000 10.912 130.900 80.878 91.000 10.769 15
TD3Dpermissive0.875 41.000 10.976 180.877 110.783 210.970 10.889 10.828 150.945 30.803 150.713 170.720 170.709 141.000 10.936 90.934 30.873 121.000 10.791 12
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Queryformer0.874 61.000 10.978 170.809 300.876 10.936 100.702 160.716 330.920 70.875 90.766 50.772 60.818 41.000 10.995 10.916 50.892 21.000 10.767 16
SoftGroup++0.874 61.000 10.972 190.947 10.839 80.898 190.556 330.913 20.881 140.756 170.828 20.748 110.821 11.000 10.937 80.937 10.887 31.000 10.821 6
Mask3D0.870 81.000 10.985 120.782 380.818 130.938 90.760 110.749 290.923 50.877 80.760 70.785 50.820 21.000 10.912 130.864 300.878 90.983 460.825 4
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ExtMask3D0.867 91.000 11.000 10.756 450.816 140.940 70.795 70.760 270.862 160.888 70.739 120.763 80.774 91.000 10.929 110.878 180.879 71.000 10.819 8
SoftGrouppermissive0.865 101.000 10.969 200.860 160.860 30.913 140.558 300.899 30.911 90.760 160.828 10.736 130.802 60.981 370.919 120.875 200.877 111.000 10.820 7
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
MAFT0.860 111.000 10.990 110.810 290.829 90.949 40.809 50.688 400.836 210.904 30.751 110.796 30.741 121.000 10.864 320.848 370.837 171.000 10.828 3
IPCA-Inst0.851 121.000 10.968 210.884 100.842 70.862 320.693 180.812 200.888 130.677 290.783 30.698 180.807 51.000 10.911 190.865 290.865 141.000 10.757 19
SPFormerpermissive0.851 121.000 10.994 60.806 310.774 230.942 60.637 220.849 120.859 180.889 50.720 160.730 150.665 201.000 10.911 190.868 280.873 131.000 10.796 10
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
Mask3D_evaluation0.843 141.000 10.955 260.847 180.795 170.932 110.750 130.780 250.891 110.818 120.737 130.633 270.703 151.000 10.902 240.870 240.820 200.941 540.805 9
SIM3D0.842 151.000 10.998 30.608 580.717 420.908 150.818 40.699 370.798 290.908 10.760 60.733 140.793 71.000 10.912 130.831 420.883 41.000 10.792 11
ISBNetpermissive0.835 161.000 10.950 270.731 470.819 110.918 120.790 80.740 300.851 200.831 110.661 250.742 120.650 231.000 10.937 70.814 500.836 181.000 10.765 17
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
SphereSeg0.835 161.000 10.963 240.891 80.794 180.954 20.822 30.710 340.961 20.721 210.693 230.530 400.653 221.000 10.867 310.857 330.859 150.991 430.771 14
GraphCut0.832 181.000 10.922 410.724 490.798 160.902 180.701 170.856 100.859 170.715 220.706 180.748 100.640 341.000 10.934 100.862 310.880 61.000 10.729 22
TopoSeg0.832 181.000 10.981 140.933 20.819 120.826 410.524 390.841 130.811 260.681 280.759 90.687 190.727 130.981 370.911 190.883 140.853 161.000 10.756 20
PBNetpermissive0.825 201.000 10.963 230.837 210.843 60.865 270.822 20.647 430.878 150.733 190.639 320.683 200.650 231.000 10.853 330.870 250.820 211.000 10.744 21
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
SSEC0.820 211.000 10.983 130.924 30.826 100.817 440.415 480.899 40.793 310.673 300.731 140.636 250.653 211.000 10.939 60.804 520.878 81.000 10.780 13
DKNet0.815 221.000 10.930 330.844 190.765 270.915 130.534 370.805 220.805 280.807 140.654 260.763 90.650 231.000 10.794 450.881 150.766 251.000 10.758 18
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN0.806 231.000 10.992 80.789 330.723 400.891 210.650 210.810 210.832 230.665 320.699 210.658 210.700 161.000 10.881 260.832 410.774 230.997 340.613 42
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
HAISpermissive0.803 241.000 10.994 60.820 250.759 280.855 330.554 340.882 70.827 250.615 380.676 240.638 240.646 321.000 10.912 130.797 550.767 240.994 410.726 23
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask0.803 241.000 10.962 250.874 120.707 450.887 240.686 200.598 480.961 10.715 230.694 220.469 450.700 161.000 10.912 130.902 70.753 300.997 340.637 36
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask-Group0.792 261.000 10.968 220.812 260.766 260.864 280.460 420.815 190.888 120.598 420.651 290.639 230.600 400.918 430.941 40.896 100.721 371.000 10.723 24
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.791 271.000 10.996 40.829 240.767 250.889 230.600 250.819 180.770 360.594 430.620 360.541 370.700 161.000 10.941 40.889 120.763 261.000 10.526 52
SSTNetpermissive0.789 281.000 10.840 550.888 90.717 410.835 370.717 150.684 410.627 510.724 200.652 280.727 160.600 401.000 10.912 130.822 450.757 291.000 10.691 30
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN0.788 291.000 10.978 160.867 150.781 220.833 380.527 380.824 160.806 270.549 510.596 390.551 330.700 161.000 10.853 330.935 20.733 341.000 10.651 33
DANCENET0.786 301.000 10.936 300.783 360.737 370.852 350.742 140.647 430.765 380.811 130.624 350.579 300.632 371.000 10.909 230.898 90.696 420.944 500.601 45
DENet0.786 301.000 10.929 340.736 460.750 340.720 570.755 120.934 10.794 300.590 440.561 450.537 380.650 231.000 10.882 250.804 530.789 221.000 10.719 25
DualGroup0.782 321.000 10.927 350.811 270.772 240.853 340.631 240.805 220.773 330.613 390.611 370.610 280.650 230.835 540.881 260.879 170.750 321.000 10.675 31
PointGroup0.778 331.000 10.900 450.798 320.715 430.863 290.493 400.706 350.895 100.569 490.701 190.576 310.639 351.000 10.880 280.851 350.719 380.997 340.709 27
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
PE0.776 341.000 10.900 460.860 160.728 390.869 250.400 490.857 90.774 320.568 500.701 200.602 290.646 320.933 420.843 360.890 110.691 460.997 340.709 26
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA0.767 351.000 10.937 290.810 280.740 360.906 160.550 350.800 240.706 430.577 480.624 340.544 360.596 450.857 460.879 300.880 160.750 310.992 420.658 32
DD-UNet+Group0.764 361.000 10.897 480.837 200.753 310.830 400.459 440.824 160.699 450.629 360.653 270.438 480.650 231.000 10.880 280.858 320.690 471.000 10.650 34
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance0.762 371.000 10.923 380.765 410.785 200.905 170.600 250.655 420.646 500.683 270.647 300.530 390.650 231.000 10.824 380.830 430.693 450.944 500.644 35
Dyco3Dcopyleft0.761 381.000 10.935 310.893 70.752 330.863 300.600 250.588 490.742 400.641 340.633 330.546 350.550 470.857 460.789 470.853 340.762 270.987 440.699 28
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance0.742 391.000 10.923 380.785 340.745 350.867 260.557 310.578 520.729 410.670 310.644 310.488 430.577 461.000 10.794 450.830 430.620 551.000 10.550 48
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg0.739 401.000 10.899 470.759 430.753 320.823 420.282 540.691 390.658 480.582 470.594 400.547 340.628 381.000 10.795 440.868 270.728 361.000 10.692 29
3D-MPA0.737 411.000 10.933 320.785 340.794 190.831 390.279 560.588 490.695 460.616 370.559 460.556 320.650 231.000 10.809 420.875 210.696 431.000 10.608 44
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.731 421.000 10.992 80.779 400.609 540.746 520.308 530.867 80.601 540.607 400.539 490.519 410.550 471.000 10.824 380.869 260.729 351.000 10.616 40
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS0.725 431.000 10.885 510.653 550.657 510.801 450.576 290.695 380.828 240.698 250.534 500.457 470.500 540.857 460.831 370.841 390.627 531.000 10.619 39
SSEN0.724 441.000 10.926 360.781 390.661 490.845 360.596 280.529 550.764 390.653 330.489 560.461 460.500 540.859 450.765 480.872 230.761 281.000 10.577 46
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF0.718 451.000 10.945 280.901 60.754 300.817 430.460 420.700 360.772 340.688 260.568 440.000 670.500 540.981 370.606 580.872 220.740 331.000 10.614 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
Sparse R-CNN0.714 461.000 10.926 370.694 500.699 470.890 220.636 230.516 560.693 470.743 180.588 410.369 520.601 390.594 600.800 430.886 130.676 480.986 450.546 49
SALoss-ResNet0.695 471.000 10.855 530.579 610.589 560.735 550.484 410.588 490.856 190.634 350.571 430.298 530.500 541.000 10.824 380.818 460.702 410.935 570.545 50
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
PanopticFusion-inst0.693 481.000 10.852 540.655 540.616 530.788 470.334 510.763 260.771 350.457 610.555 470.652 220.518 510.857 460.765 480.732 610.631 510.944 500.577 47
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS0.688 491.000 10.913 420.730 480.737 380.743 540.442 450.855 110.655 490.546 520.546 480.263 550.508 530.889 440.568 590.771 580.705 400.889 600.625 38
3D-BoNet0.687 501.000 10.887 500.836 220.587 570.643 640.550 350.620 450.724 420.522 560.501 540.243 560.512 521.000 10.751 500.807 510.661 500.909 590.612 43
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
ClickSeg_Instance0.685 511.000 10.818 570.600 590.715 440.795 460.557 310.533 540.591 560.601 410.519 520.429 500.638 360.938 410.706 530.817 480.624 540.944 500.502 54
PCJC0.684 521.000 10.895 490.757 440.659 500.862 310.189 630.739 310.606 530.712 240.581 420.515 420.650 230.857 460.357 640.785 560.631 520.889 600.635 37
SPG_WSIS0.678 531.000 10.880 520.836 220.701 460.727 560.273 580.607 470.706 440.541 540.515 530.174 590.600 400.857 460.716 520.846 380.711 391.000 10.506 53
One_Thing_One_Clickpermissive0.675 541.000 10.823 560.782 370.621 520.766 490.211 600.736 320.560 580.586 450.522 510.636 260.453 580.641 580.853 330.850 360.694 440.997 340.411 59
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_inspermissive0.637 551.000 10.923 400.593 600.561 580.746 530.143 650.504 570.766 370.485 590.442 570.372 510.530 500.714 550.815 410.775 570.673 491.000 10.431 58
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASCpermissive0.615 560.711 630.802 580.540 620.757 290.777 480.029 660.577 530.588 570.521 570.600 380.436 490.534 490.697 560.616 570.838 400.526 570.980 470.534 51
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone0.605 571.000 10.909 430.764 420.603 550.704 580.415 470.301 620.548 590.461 600.394 580.267 540.386 600.857 460.649 560.817 470.504 590.959 480.356 62
3D-SISpermissive0.558 581.000 10.773 590.614 570.503 610.691 600.200 610.412 580.498 620.546 530.311 630.103 630.600 400.857 460.382 610.799 540.445 650.938 560.371 60
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.544 590.500 660.655 650.661 530.663 480.765 500.432 460.214 650.612 520.584 460.499 550.204 580.286 640.429 630.655 550.650 660.539 560.950 490.499 55
Hier3Dcopyleft0.540 601.000 10.727 600.626 560.467 640.693 590.200 610.412 580.480 630.528 550.318 620.077 660.600 400.688 570.382 610.768 590.472 610.941 540.350 63
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class0.497 610.250 680.902 440.689 510.540 590.747 510.276 570.610 460.268 670.489 580.348 590.000 670.243 670.220 660.663 540.814 490.459 630.928 580.496 56
Sem_Recon_ins0.484 620.764 620.608 670.470 640.521 600.637 650.311 520.218 640.348 660.365 650.223 640.222 570.258 650.629 590.734 510.596 670.509 580.858 630.444 57
tmp0.474 631.000 10.727 600.433 660.481 630.673 620.022 680.380 600.517 610.436 630.338 610.128 610.343 620.429 630.291 660.728 620.473 600.833 640.300 65
SemRegionNet-20cls0.470 641.000 10.727 600.447 650.481 620.678 610.024 670.380 600.518 600.440 620.339 600.128 610.350 610.429 630.212 670.711 630.465 620.833 640.290 66
ASIS0.422 650.333 670.707 630.676 520.401 650.650 630.350 500.177 660.594 550.376 640.202 650.077 650.404 590.571 610.197 680.674 650.447 640.500 670.260 67
3D-BEVIS0.401 660.667 640.687 640.419 670.137 680.587 660.188 640.235 630.359 650.211 670.093 680.080 640.311 630.571 610.382 610.754 600.300 670.874 620.357 61
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.390 670.556 650.636 660.493 630.353 660.539 670.271 590.160 670.450 640.359 660.178 660.146 600.250 660.143 670.347 650.698 640.436 660.667 660.331 64
MaskRCNN 2d->3d Proj0.261 680.903 610.081 680.008 680.233 670.175 680.280 550.106 680.150 680.203 680.175 670.480 440.218 680.143 670.542 600.404 680.153 680.393 680.049 68


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