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 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 by
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.520 10.109 20.108 110.000 10.337 10.000 10.310 90.394 60.494 90.753 80.848 10.256 20.717 30.000 30.842 10.192 20.065 20.449 60.346 20.546 40.190 80.000 50.384 40.000 10.000 30.218 10.505 10.791 10.000 10.136 20.000 20.903 10.073 100.687 40.000 50.168 10.551 30.387 60.941 10.000 10.000 20.397 80.654 30.000 80.714 30.759 100.752 50.118 40.264 20.926 10.000 10.048 30.575 20.000 70.597 10.366 10.755 10.469 10.474 10.798 10.140 60.617 10.692 40.000 40.592 20.971 10.188 30.000 10.133 50.593 10.349 10.650 20.717 50.699 10.455 10.790 10.523 30.636 10.301 10.000 10.622 20.000 70.017 100.259 20.000 30.921 20.337 10.733 10.210 10.514 10.860 60.407 10.000 10.688 10.109 60.000 100.000 40.000 10.151 20.671 40.782 10.115 80.641 10.903 10.349 10.616 20.088 40.832 30.000 30.480 20.000 10.428 10.000 20.497 70.000 10.000 50.000 10.662 20.690 10.612 10.828 10.575 10.000 10.404 40.644 10.325 40.887 20.728 10.009 110.134 50.026 120.000 10.761 10.731 10.172 40.077 20.528 30.727 30.000 10.603 40.220 20.022 20.000 10.740 10.000 20.000 10.661 20.586 10.566 10.436 40.531 20.978 10.457 10.708 20.583 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 20.000 100.281 20.738 10.463 5
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)
CeCo0.340 40.551 60.247 80.181 30.475 80.057 120.142 90.000 10.000 30.000 10.387 30.463 30.499 70.924 20.774 70.213 40.257 80.000 30.546 100.100 80.006 50.615 10.177 120.534 50.246 40.000 50.400 20.000 10.338 10.006 110.484 30.609 20.000 10.083 80.000 20.873 70.089 50.661 90.000 50.048 110.560 20.408 50.892 50.000 10.000 20.586 10.616 50.000 80.692 60.900 30.721 70.162 10.228 30.860 60.000 10.000 80.575 20.083 30.550 30.347 20.624 80.410 80.360 40.740 20.109 80.321 100.660 50.000 40.121 40.939 80.143 70.000 10.400 10.003 80.190 70.564 30.652 70.615 60.421 20.304 100.579 10.547 40.000 30.000 10.296 90.000 70.030 60.096 40.000 30.916 30.037 80.551 70.171 40.376 50.865 50.286 30.000 10.633 20.102 90.027 60.011 30.000 10.000 60.474 90.742 20.133 50.311 80.824 70.242 80.503 90.068 60.828 40.000 30.429 40.000 10.063 30.000 20.781 10.000 10.000 50.000 10.665 10.633 40.450 40.818 20.000 70.000 10.429 20.532 50.226 80.825 60.510 80.377 30.709 10.079 90.000 10.753 30.683 30.102 110.063 30.401 110.620 90.000 10.619 20.000 110.000 50.000 10.595 100.000 20.000 10.345 90.564 30.411 50.603 10.384 40.945 50.266 60.643 40.367 90.304 10.663 70.000 10.010 30.726 100.767 60.898 30.000 10.784 80.435 10.861 50.000 10.447 70.000 100.257 50.656 80.377 8
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
BFANet ScanNet200permissive0.360 20.553 40.293 20.193 20.483 60.096 30.266 40.000 10.000 30.000 10.298 100.255 90.661 10.810 50.810 20.194 60.785 20.000 30.000 120.161 30.000 60.494 50.382 10.574 20.258 30.000 50.372 60.000 10.000 30.043 90.436 50.000 60.000 10.239 10.000 20.901 20.105 10.689 20.025 20.128 20.614 10.436 10.493 120.000 10.000 20.526 40.546 80.109 30.651 100.953 20.753 40.101 50.143 80.897 20.000 10.431 10.469 100.000 70.522 50.337 30.661 40.459 20.409 20.666 30.102 90.508 40.757 20.000 40.060 90.970 20.497 10.000 10.376 20.511 20.262 40.688 10.921 10.617 50.321 90.590 30.491 50.556 30.000 30.000 10.481 30.093 10.043 20.284 10.000 30.875 100.135 50.669 30.124 80.394 40.849 90.298 20.000 10.476 120.088 100.042 50.000 40.000 10.254 10.653 60.741 30.215 10.573 30.852 40.266 50.654 10.056 90.835 10.000 30.492 10.000 10.000 40.000 20.612 60.000 10.000 50.000 10.616 30.469 120.460 30.698 90.516 20.000 10.378 50.563 20.476 20.863 30.574 60.330 40.000 70.282 30.000 10.760 20.710 20.233 10.000 70.641 20.814 10.000 10.585 70.053 80.000 50.000 10.629 70.000 20.000 10.678 10.528 80.534 20.129 90.596 10.973 20.264 70.772 10.526 50.139 90.707 20.000 10.000 80.764 90.591 110.848 60.000 10.827 20.338 30.806 90.000 10.568 50.151 50.358 10.659 70.510 3
PonderV2 ScanNet2000.346 30.552 50.270 50.175 40.497 50.070 90.239 50.000 10.000 30.000 10.232 120.412 50.584 20.842 30.804 40.212 50.540 50.000 30.433 110.106 70.000 60.590 30.290 70.548 30.243 50.000 50.356 80.000 10.000 30.062 70.398 80.441 50.000 10.104 70.000 20.888 30.076 90.682 50.030 10.094 50.491 70.351 80.869 70.000 10.063 10.403 70.700 20.000 80.660 90.881 40.761 10.050 70.186 50.852 80.000 10.007 60.570 50.100 20.565 20.326 40.641 70.431 40.290 90.621 40.259 20.408 60.622 70.125 10.082 70.950 30.179 40.000 10.263 30.424 30.193 60.558 40.880 20.545 80.375 40.727 20.445 70.499 70.000 30.000 10.475 50.002 50.034 50.083 50.000 30.924 10.290 30.636 40.115 90.400 30.874 30.186 50.000 10.611 50.128 20.113 20.000 40.000 10.000 60.584 70.636 60.103 90.385 60.843 50.283 20.603 40.080 50.825 50.000 30.377 70.000 10.000 40.000 20.457 80.000 10.000 50.000 10.574 90.608 60.481 20.792 30.394 30.000 10.357 70.503 80.261 70.817 80.504 90.304 50.472 30.115 60.000 10.750 40.677 40.202 20.000 70.509 40.729 20.000 10.519 90.000 110.000 50.000 10.620 90.000 20.000 10.660 40.560 40.486 30.384 60.346 50.952 30.247 90.667 30.436 70.269 30.691 40.000 10.010 30.787 50.889 20.880 40.000 10.810 50.336 40.860 60.000 10.606 40.009 60.248 60.681 40.392 7
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.
LGroundpermissive0.272 100.485 100.184 100.106 100.476 70.077 70.218 60.000 10.000 30.000 10.547 10.295 80.540 40.746 90.745 100.058 110.112 110.005 10.658 60.077 120.000 60.322 100.178 110.512 90.190 80.199 10.277 100.000 10.000 30.173 40.399 70.000 60.000 10.039 110.000 20.858 100.085 60.676 70.002 30.103 40.498 60.323 90.703 90.000 10.000 20.296 100.549 70.216 10.702 40.768 90.718 90.028 80.092 110.786 110.000 10.000 80.453 110.022 50.251 120.252 70.572 100.348 100.321 60.514 50.063 100.279 110.552 100.000 40.019 110.932 100.132 110.000 10.000 80.000 100.156 120.457 100.623 80.518 90.265 110.358 80.381 100.395 100.000 30.000 10.127 120.012 40.051 10.000 70.000 30.886 90.014 90.437 120.179 30.244 100.826 100.000 70.000 10.599 70.136 10.085 30.000 40.000 10.000 60.565 80.612 90.143 30.207 100.566 100.232 100.446 100.127 20.708 100.000 30.384 60.000 10.000 40.000 20.402 90.000 10.059 30.000 10.525 120.566 80.229 90.659 100.000 70.000 10.265 100.446 90.147 110.720 120.597 50.066 90.000 70.187 40.000 10.726 80.467 120.134 80.000 70.413 100.629 80.000 10.363 110.055 70.022 20.000 10.626 80.000 20.000 10.323 100.479 120.154 110.117 100.028 110.901 100.243 100.415 110.295 120.143 60.610 110.000 10.000 80.777 70.397 120.324 110.000 10.778 100.179 80.702 110.000 10.274 120.404 10.233 70.622 100.398 6
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
OctFormer ScanNet200permissive0.326 80.539 70.265 70.131 70.499 40.110 10.522 10.000 10.000 30.000 10.318 80.427 40.455 100.743 100.765 80.175 70.842 10.000 30.828 20.204 10.033 30.429 70.335 30.601 10.312 20.000 50.357 70.000 10.000 30.047 80.423 60.000 60.000 10.105 60.000 20.873 70.079 80.670 80.000 50.117 30.471 90.432 30.829 80.000 10.000 20.584 20.417 120.089 40.684 70.837 70.705 110.021 100.178 60.892 30.000 10.028 50.505 80.000 70.457 70.200 90.662 20.412 70.244 100.496 60.000 120.451 50.626 60.000 40.102 60.943 70.138 90.000 10.000 80.149 50.291 30.534 60.722 40.632 40.331 70.253 110.453 60.487 80.000 30.000 10.479 40.000 70.022 80.000 70.000 30.900 60.128 60.684 20.164 50.413 20.854 80.000 70.000 10.512 110.074 120.003 80.000 40.000 10.000 60.469 100.613 80.132 60.529 50.871 20.227 110.582 50.026 120.787 70.000 30.339 100.000 10.000 40.000 20.626 40.000 10.029 40.000 10.587 60.612 50.411 50.724 70.000 70.000 10.407 30.552 30.513 10.849 50.655 30.408 20.000 70.296 20.000 10.686 100.645 90.145 60.022 50.414 90.633 70.000 10.637 10.224 10.000 50.000 10.650 50.000 20.000 10.622 60.535 70.343 70.483 30.230 80.943 60.289 50.618 50.596 20.140 80.679 50.000 10.022 20.783 60.620 90.906 10.000 10.806 60.137 90.865 30.000 10.378 80.000 100.168 120.680 50.227 11
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
AWCS0.305 90.508 90.225 90.142 60.463 90.063 100.195 70.000 10.000 30.000 10.467 20.551 10.504 60.773 60.764 90.142 90.029 120.000 30.626 80.100 80.000 60.360 90.179 100.507 100.137 100.006 40.300 90.000 10.000 30.172 50.364 100.512 40.000 10.056 90.000 20.865 90.093 40.634 120.000 50.071 90.396 100.296 110.876 60.000 10.000 20.373 90.436 110.063 70.749 10.877 50.721 70.131 30.124 90.804 100.000 10.000 80.515 70.010 60.452 80.252 70.578 90.417 60.179 120.484 70.171 40.337 90.606 90.000 40.115 50.937 90.142 80.000 10.008 70.000 100.157 110.484 90.402 120.501 100.339 60.553 40.529 20.478 90.000 30.000 10.404 70.001 60.022 80.077 60.000 30.894 80.219 40.628 50.093 100.305 90.886 10.233 40.000 10.603 60.112 40.023 70.000 40.000 10.000 60.741 20.664 50.097 100.253 90.782 90.264 60.523 80.154 10.707 110.000 30.411 50.000 10.000 40.000 20.332 110.000 10.000 50.000 10.602 40.595 70.185 100.656 110.159 40.000 10.355 80.424 100.154 100.729 100.516 70.220 70.620 20.084 80.000 10.707 90.651 80.173 30.014 60.381 120.582 100.000 10.619 20.049 90.000 50.000 10.702 20.000 20.000 10.302 110.489 100.317 80.334 70.392 30.922 90.254 80.533 90.394 80.129 120.613 100.000 10.000 80.820 20.649 80.749 90.000 10.782 90.282 60.863 40.000 10.288 110.006 70.220 80.633 90.542 2
PPT-SpUNet-F.T.0.332 70.556 30.270 40.123 90.519 20.091 40.349 20.000 10.000 30.000 10.339 60.383 70.498 80.833 40.807 30.241 30.584 40.000 30.755 40.124 50.000 60.608 20.330 50.530 70.314 10.000 50.374 50.000 10.000 30.197 20.459 40.000 60.000 10.117 30.000 20.876 50.095 20.682 50.000 50.086 60.518 50.433 20.930 20.000 10.000 20.563 30.542 90.077 50.715 20.858 60.756 30.008 120.171 70.874 50.000 10.039 40.550 60.000 70.545 40.256 60.657 60.453 30.351 50.449 80.213 30.392 70.611 80.000 40.037 100.946 40.138 90.000 10.000 80.063 60.308 20.537 50.796 30.673 20.323 80.392 70.400 90.509 60.000 30.000 10.649 10.000 70.023 70.000 70.000 30.914 50.002 110.506 110.163 60.359 60.872 40.000 70.000 10.623 40.112 40.001 90.000 40.000 10.021 40.753 10.565 110.150 20.579 20.806 80.267 40.616 20.042 110.783 80.000 30.374 80.000 10.000 40.000 20.620 50.000 10.000 50.000 10.572 100.634 30.350 70.792 30.000 70.000 10.376 60.535 40.378 30.855 40.672 20.074 80.000 70.185 50.000 10.727 70.660 70.076 120.000 70.432 70.646 60.000 10.594 60.006 100.000 50.000 10.658 40.000 20.000 10.661 20.549 50.300 90.291 80.045 90.942 70.304 40.600 60.572 40.135 110.695 30.000 10.008 50.793 40.942 10.899 20.000 10.816 40.181 70.897 20.000 10.679 30.223 30.264 30.691 30.345 10
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
L3DETR-ScanNet_2000.336 50.533 80.279 30.155 50.508 30.073 80.101 120.000 10.058 20.000 10.294 110.233 110.548 30.927 10.788 60.264 10.463 60.000 30.638 70.098 100.014 40.411 80.226 80.525 80.225 70.010 30.397 30.000 10.000 30.192 30.380 90.598 30.000 10.117 30.000 20.883 40.082 70.689 20.000 50.032 120.549 40.417 40.910 30.000 10.000 20.448 60.613 60.000 80.697 50.960 10.759 20.158 20.293 10.883 40.000 10.312 20.583 10.079 40.422 90.068 120.660 50.418 50.298 70.430 90.114 70.526 30.776 10.051 20.679 10.946 40.152 60.000 10.183 40.000 100.211 50.511 70.409 110.565 70.355 50.448 50.512 40.557 20.000 30.000 10.420 60.000 70.007 120.104 30.000 30.125 120.330 20.514 100.146 70.321 80.860 60.174 60.000 10.629 30.075 110.000 100.000 40.000 10.002 50.671 40.712 40.141 40.339 70.856 30.261 70.529 70.067 70.835 10.000 30.369 90.000 10.259 20.000 20.629 30.000 10.487 10.000 10.579 80.646 20.107 120.720 80.122 50.000 10.333 90.505 70.303 60.908 10.503 100.565 10.074 60.324 10.000 10.740 50.661 60.109 90.000 70.427 80.563 120.000 10.579 80.108 50.000 50.000 10.664 30.000 20.000 10.641 50.539 60.416 40.515 20.256 60.940 80.312 30.209 120.620 10.138 100.636 80.000 10.000 80.775 80.861 40.765 80.000 10.801 70.119 100.860 60.000 10.687 10.001 90.192 110.679 60.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet2000.333 60.558 20.269 60.124 80.448 100.080 60.272 30.000 10.000 30.000 10.342 50.515 20.524 50.713 120.789 50.158 80.384 70.000 30.806 30.125 40.000 60.496 40.332 40.498 110.227 60.024 20.474 10.000 10.003 20.071 60.487 20.000 60.000 10.110 50.000 20.876 50.013 120.703 10.000 50.076 70.473 80.355 70.906 40.000 10.000 20.476 50.706 10.000 80.672 80.835 80.748 60.015 110.223 40.860 60.000 10.000 80.572 40.000 70.509 60.313 50.662 20.398 90.396 30.411 100.276 10.527 20.711 30.000 40.076 80.946 40.166 50.000 10.022 60.160 40.183 80.493 80.699 60.637 30.403 30.330 90.406 80.526 50.024 20.000 10.392 80.000 70.016 110.000 70.196 20.915 40.112 70.557 60.197 20.352 70.877 20.000 70.000 10.592 90.103 80.000 100.067 10.000 10.089 30.735 30.625 70.130 70.568 40.836 60.271 30.534 60.043 100.799 60.001 20.445 30.000 10.000 40.024 10.661 20.000 10.262 20.000 10.591 50.517 100.373 60.788 50.021 60.000 10.455 10.517 60.320 50.823 70.200 120.001 120.150 40.100 70.000 10.736 60.668 50.103 100.052 40.662 10.720 40.000 10.602 50.112 40.002 40.000 10.637 60.000 20.000 10.621 70.569 20.398 60.412 50.234 70.949 40.363 20.492 100.495 60.251 40.665 60.000 10.001 70.805 30.833 50.794 70.000 10.821 30.314 50.843 80.000 10.560 60.245 20.262 40.713 20.370 9
CSC-Pretrainpermissive0.249 120.455 120.171 110.079 120.418 110.059 110.186 80.000 10.000 30.000 10.335 70.250 100.316 110.766 70.697 120.142 90.170 90.003 20.553 90.112 60.097 10.201 120.186 90.476 120.081 110.000 50.216 120.000 10.000 30.001 120.314 120.000 60.000 10.055 100.000 20.832 120.094 30.659 100.002 30.076 70.310 120.293 120.664 110.000 10.000 20.175 120.634 40.130 20.552 120.686 120.700 120.076 60.110 100.770 120.000 10.000 80.430 120.000 70.319 100.166 100.542 120.327 110.205 110.332 110.052 110.375 80.444 120.000 40.012 120.930 120.203 20.000 10.000 80.046 70.175 90.413 110.592 90.471 110.299 100.152 120.340 110.247 120.000 30.000 10.225 100.058 30.037 30.000 70.207 10.862 110.014 90.548 80.033 110.233 110.816 110.000 70.000 10.542 100.123 30.121 10.019 20.000 10.000 60.463 110.454 120.045 120.128 120.557 110.235 90.441 110.063 80.484 120.000 30.308 120.000 10.000 40.000 20.318 120.000 10.000 50.000 10.545 110.543 90.164 110.734 60.000 70.000 10.215 120.371 110.198 90.743 90.205 110.062 100.000 70.079 90.000 10.683 110.547 110.142 70.000 70.441 60.579 110.000 10.464 100.098 60.041 10.000 10.590 110.000 20.000 10.373 80.494 90.174 100.105 110.001 120.895 110.222 110.537 80.307 110.180 50.625 90.000 10.000 80.591 120.609 100.398 100.000 10.766 120.014 120.638 120.000 10.377 90.004 80.206 100.609 120.465 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 110.463 110.154 120.102 110.381 120.084 50.134 100.000 10.000 30.000 10.386 40.141 120.279 120.737 110.703 110.014 120.164 100.000 30.663 50.092 110.000 60.224 110.291 60.531 60.056 120.000 50.242 110.000 10.000 30.013 100.331 110.000 60.000 10.035 120.001 10.858 100.059 110.650 110.000 50.056 100.353 110.299 100.670 100.000 10.000 20.284 110.484 100.071 60.594 110.720 110.710 100.027 90.068 120.813 90.000 10.005 70.492 90.164 10.274 110.111 110.571 110.307 120.293 80.307 120.150 50.163 120.531 110.002 30.545 30.932 100.093 120.000 10.000 80.002 90.159 100.368 120.581 100.440 120.228 120.406 60.282 120.294 110.000 30.000 10.189 110.060 20.036 40.000 70.000 30.897 70.000 120.525 90.025 120.205 120.771 120.000 70.000 10.593 80.108 70.044 40.000 40.000 10.000 60.282 120.589 100.094 110.169 110.466 120.227 110.419 120.125 30.757 90.002 10.334 110.000 10.000 40.000 20.357 100.000 10.000 50.000 10.582 70.513 110.337 80.612 120.000 70.000 10.250 110.352 120.136 120.724 110.655 30.280 60.000 70.046 110.000 10.606 120.559 100.159 50.102 10.445 50.655 50.000 10.310 120.117 30.000 50.000 10.581 120.026 10.000 10.265 120.483 110.084 120.097 120.044 100.865 120.142 120.588 70.351 100.272 20.596 120.000 10.003 60.622 110.720 70.096 120.000 10.771 110.016 110.772 100.000 10.302 100.194 40.214 90.621 110.197 12
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019


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




Method Infoavg ap 50%head ap 50%common ap 50%tail ap 50%alarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 by
LGround Inst.permissive0.246 30.413 30.170 30.130 30.455 50.003 50.500 10.000 10.000 10.000 10.017 40.333 40.111 51.000 10.681 40.400 30.000 20.000 31.000 10.003 50.000 20.167 30.190 20.637 20.067 30.000 10.081 30.000 10.000 20.000 30.264 40.000 20.000 10.000 30.000 10.387 40.031 50.754 30.000 10.000 20.151 20.135 20.056 40.000 20.000 10.582 40.589 50.500 20.815 21.000 10.903 10.000 30.097 20.588 40.000 30.000 30.234 30.000 30.500 30.400 10.682 40.156 30.159 40.750 10.046 30.125 40.660 30.000 20.200 20.000 50.000 10.000 30.000 10.164 30.402 30.500 20.373 30.025 30.143 50.426 30.317 20.000 10.000 10.000 30.000 30.063 30.000 30.000 10.000 50.000 40.575 30.250 20.241 20.772 30.000 30.000 10.653 40.034 30.000 30.000 30.000 20.000 31.000 10.561 40.000 20.100 20.500 10.541 40.452 30.000 30.581 30.000 20.364 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.568 40.511 40.167 30.857 30.000 30.000 30.164 20.112 30.000 50.530 51.000 10.286 30.000 20.125 30.000 30.464 50.706 30.208 40.000 30.125 20.744 40.000 30.500 20.000 10.000 20.000 20.511 30.000 10.000 10.344 20.541 30.068 30.333 20.000 31.000 10.196 40.533 30.318 30.000 40.748 30.000 10.000 20.690 21.000 10.400 30.000 10.000 30.667 30.000 10.333 40.333 20.270 30.399 30.083 4
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.209 40.361 50.157 40.085 40.506 20.007 30.500 10.000 10.000 10.000 10.000 50.093 50.221 40.667 40.524 50.400 30.000 20.000 30.000 30.004 40.000 20.000 50.109 50.589 40.000 40.000 10.059 50.000 10.000 20.000 30.322 20.000 20.000 10.000 30.000 10.405 30.055 40.700 50.000 10.000 20.028 40.091 50.083 30.000 20.000 10.667 20.768 20.000 40.807 31.000 10.776 50.000 30.000 50.340 50.000 30.000 30.103 50.000 30.750 10.200 30.634 50.053 50.246 30.677 20.006 50.198 30.432 40.000 20.000 40.050 40.000 10.000 30.000 10.111 50.356 40.500 20.188 50.000 40.220 40.448 20.050 50.000 10.000 10.000 30.000 30.032 50.000 30.000 10.396 20.000 40.573 40.000 50.228 30.747 40.000 30.000 10.573 50.021 50.000 30.000 30.000 20.000 30.500 40.573 30.000 20.000 50.125 40.592 30.364 50.000 30.450 50.000 20.364 20.000 10.000 30.000 20.340 30.000 10.000 20.000 10.610 30.833 10.221 10.702 50.000 30.000 30.135 50.094 40.125 20.571 40.500 40.143 50.000 20.125 30.000 30.618 20.667 40.115 50.000 30.125 21.000 10.000 30.500 20.000 10.000 20.000 20.502 40.000 10.000 10.312 40.248 50.050 40.000 50.000 30.997 30.420 30.500 40.149 50.451 20.748 20.000 10.000 20.636 30.667 50.600 20.000 10.000 30.278 50.000 10.333 40.000 50.294 20.381 50.110 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34D Inst.permissive0.203 50.369 40.134 50.078 50.479 40.003 40.500 10.000 10.000 10.000 10.100 20.371 30.300 20.667 40.746 20.400 30.000 20.000 30.000 30.031 30.000 20.074 40.165 30.413 50.000 40.000 10.070 40.000 10.000 20.000 30.221 50.000 20.000 10.000 30.000 10.372 50.070 20.706 40.000 10.000 20.000 50.123 40.033 50.000 20.000 10.422 50.732 30.000 40.778 51.000 10.845 30.000 30.090 40.636 20.000 30.000 30.158 40.000 30.250 50.050 40.693 30.123 40.051 50.385 30.009 40.118 50.406 50.000 20.000 40.200 20.000 10.000 30.000 10.133 40.307 50.500 20.251 40.000 40.281 30.402 40.317 20.000 10.000 10.000 30.000 30.060 40.000 30.000 10.396 20.200 30.669 20.021 40.218 40.720 50.000 30.000 10.696 30.025 40.000 30.000 30.000 20.000 30.125 50.596 20.000 20.191 10.500 10.595 20.369 40.000 30.500 40.000 20.143 50.000 10.000 30.000 20.226 50.000 10.000 20.000 10.701 20.511 40.000 50.851 40.000 30.000 30.150 40.052 50.100 30.981 30.500 40.286 30.000 20.000 50.000 30.545 40.522 50.250 30.000 30.000 50.522 50.000 30.500 20.000 10.000 20.000 20.282 50.000 10.000 10.178 50.382 40.018 50.056 40.000 30.997 30.107 50.677 20.313 40.000 40.726 50.000 10.000 20.583 40.903 40.200 50.000 10.000 30.333 40.000 10.442 20.083 40.109 50.387 40.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
TD3D Scannet200permissive0.320 20.501 20.264 20.164 20.506 30.062 20.500 10.000 10.000 10.000 10.208 10.431 20.252 31.000 10.733 30.587 20.000 20.008 20.000 30.106 10.000 20.356 10.123 40.686 10.101 20.000 10.152 20.000 10.000 20.226 10.280 30.000 20.000 10.250 10.000 10.619 20.061 30.841 10.000 10.000 20.167 10.194 10.333 20.000 20.000 10.667 20.820 10.250 30.790 41.000 10.879 20.077 10.094 30.708 10.217 20.049 20.634 10.792 10.331 40.033 50.716 20.159 20.396 20.331 40.099 20.415 10.842 10.000 20.458 10.542 10.000 10.101 20.000 10.218 10.513 20.500 20.458 20.104 20.516 10.456 10.268 40.000 10.000 10.400 10.022 10.233 20.143 20.000 10.677 10.400 10.504 50.095 30.083 50.890 20.061 20.000 10.906 10.076 20.231 10.125 20.000 20.003 20.792 30.881 10.000 20.098 30.125 40.498 50.459 20.063 10.715 10.000 20.241 40.000 10.396 20.063 10.605 10.000 10.000 20.000 10.448 50.629 30.202 20.967 10.250 20.038 10.192 10.185 20.083 41.000 11.000 10.857 20.000 20.470 20.012 10.565 30.798 10.621 10.111 10.500 11.000 10.017 20.509 10.000 10.008 11.000 10.525 20.000 10.000 10.332 30.679 10.264 20.333 20.267 11.000 10.549 10.299 50.387 20.328 30.744 40.000 10.000 20.435 51.000 10.283 40.000 10.196 10.817 10.000 10.472 10.222 30.123 40.560 20.156 2
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Mask3D Scannet2000.388 10.542 10.357 10.237 10.610 10.091 10.125 50.000 10.000 10.000 10.065 30.668 10.451 11.000 10.955 10.640 10.500 10.039 10.125 20.063 20.409 10.311 20.291 10.609 30.266 10.000 10.163 10.000 10.008 10.044 20.496 11.000 10.000 10.018 20.000 10.756 10.573 10.808 20.000 10.010 10.042 30.130 30.552 10.042 10.000 11.000 10.725 40.750 10.883 11.000 10.832 40.024 20.107 10.614 30.226 10.250 10.628 20.792 10.677 20.400 10.741 10.278 10.511 10.077 50.111 10.313 20.715 20.302 10.017 30.200 20.000 10.188 10.000 10.178 20.736 11.000 10.615 10.514 10.409 20.380 50.600 10.000 10.000 10.400 10.013 20.254 10.381 10.000 10.123 40.400 10.839 10.258 10.463 10.926 10.265 10.000 10.857 20.099 10.021 20.500 10.027 10.028 11.000 10.502 50.016 10.076 40.500 10.612 10.578 10.005 20.597 20.194 10.497 10.000 10.500 10.000 20.323 40.000 11.000 10.000 10.748 10.708 20.050 40.890 21.000 10.008 20.151 30.301 11.000 11.000 10.792 30.945 11.000 10.511 10.004 20.753 10.776 20.287 20.020 20.003 40.974 30.033 10.412 50.000 10.000 20.000 20.667 10.000 10.000 10.491 10.676 20.352 10.335 10.060 20.822 50.527 21.000 10.517 10.606 10.853 10.000 10.004 10.806 11.000 10.727 10.000 10.042 20.739 20.000 10.399 30.391 10.504 10.591 10.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023


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 30.813 190.851 70.782 60.890 20.597 10.916 20.696 80.713 30.979 10.635 10.384 20.793 20.907 80.821 40.790 310.696 110.967 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 10.800 270.833 230.788 40.853 160.545 160.910 50.713 10.705 40.979 10.596 70.390 10.769 120.832 410.821 40.792 300.730 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 60.831 330.685 140.714 20.979 10.594 80.310 260.801 10.892 160.841 20.819 40.723 40.940 130.887 60.725 24
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 140.836 200.790 30.875 40.576 50.905 60.704 50.739 10.969 100.611 20.349 100.756 220.958 10.702 450.805 140.708 70.916 320.898 30.801 2
TTT-KD0.773 50.646 910.818 140.809 350.774 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 80.686 130.677 120.961 160.537 310.348 110.769 120.903 100.785 100.815 60.676 220.939 140.880 110.772 8
OctFormerpermissive0.766 70.925 70.808 230.849 90.786 50.846 260.566 90.876 150.690 100.674 140.960 170.576 170.226 670.753 240.904 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
PPT-SpUNet-Joint0.766 70.932 50.794 330.829 250.751 220.854 140.540 200.903 70.630 340.672 150.963 140.565 210.357 80.788 30.900 120.737 250.802 150.685 170.950 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 90.758 580.796 310.839 180.746 250.907 10.562 110.850 250.680 160.672 150.978 40.610 30.335 170.777 60.819 450.847 10.830 10.691 140.972 20.885 80.727 22
CU-Hybrid Net0.764 90.924 80.819 120.840 170.757 170.853 160.580 30.848 260.709 30.643 240.958 210.587 120.295 330.753 240.884 200.758 190.815 60.725 30.927 240.867 220.743 15
O-CNNpermissive0.762 110.924 80.823 70.844 150.770 110.852 180.577 40.847 280.711 20.640 280.958 210.592 90.217 730.762 170.888 170.758 190.813 100.726 20.932 220.868 210.744 14
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 20.862 100.511 330.870 170.707 40.652 200.954 350.604 60.279 440.760 180.942 20.734 260.766 440.701 100.884 540.874 190.736 16
OA-CNN-L_ScanNet200.756 130.783 440.826 50.858 40.776 80.837 330.548 150.896 110.649 260.675 130.962 150.586 130.335 170.771 110.802 490.770 150.787 330.691 140.936 170.880 110.761 10
PNE0.755 140.786 420.835 40.834 220.758 150.849 210.570 80.836 320.648 270.668 170.978 40.581 160.367 60.683 350.856 290.804 60.801 190.678 190.961 50.889 50.716 29
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer0.755 140.927 60.822 80.836 200.801 10.849 210.516 300.864 220.651 250.680 110.958 210.584 150.282 410.759 200.855 310.728 280.802 150.678 190.880 590.873 200.756 12
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
DMF-Net0.752 160.906 120.793 350.802 410.689 400.825 460.556 120.867 180.681 150.602 440.960 170.555 270.365 70.779 50.859 260.747 220.795 270.717 60.917 310.856 300.764 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
PointTransformerV20.752 160.742 660.809 220.872 10.758 150.860 110.552 130.891 130.610 410.687 60.960 170.559 250.304 290.766 150.926 40.767 160.797 230.644 330.942 110.876 160.722 26
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 180.793 400.790 360.807 370.750 240.856 130.524 260.881 140.588 530.642 270.977 80.591 100.274 470.781 40.929 30.804 60.796 240.642 340.947 90.885 80.715 30
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 180.909 100.818 140.811 330.752 200.839 320.485 470.842 290.673 180.644 230.957 250.528 370.305 280.773 90.859 260.788 80.818 50.693 130.916 320.856 300.723 25
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 200.623 940.804 250.859 30.745 260.824 480.501 370.912 40.690 100.685 80.956 260.567 200.320 230.768 140.918 50.720 330.802 150.676 220.921 290.881 100.779 6
StratifiedFormerpermissive0.747 210.901 130.803 260.845 140.757 170.846 260.512 320.825 360.696 80.645 220.956 260.576 170.262 580.744 290.861 250.742 230.770 420.705 80.899 440.860 270.734 17
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 40.729 310.850 200.501 370.874 160.587 540.658 190.956 260.564 220.299 310.765 160.900 120.716 360.812 110.631 390.939 140.858 280.709 31
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 MVFusion0.746 220.771 520.819 120.848 110.702 370.865 90.397 850.899 90.699 60.664 180.948 550.588 110.330 190.746 280.851 350.764 170.796 240.704 90.935 180.866 230.728 20
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
DiffSeg3D20.745 240.725 750.814 180.837 190.751 220.831 400.514 310.896 110.674 170.684 90.960 170.564 220.303 300.773 90.820 440.713 390.798 220.690 160.923 270.875 170.757 11
Retro-FPN0.744 250.842 270.800 270.767 550.740 270.836 350.541 180.914 30.672 190.626 320.958 210.552 280.272 490.777 60.886 190.696 460.801 190.674 250.941 120.858 280.717 27
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-Net0.743 260.620 950.799 300.849 90.730 300.822 500.493 440.897 100.664 200.681 100.955 290.562 240.378 30.760 180.903 100.738 240.801 190.673 260.907 360.877 130.745 13
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 270.816 350.806 240.807 370.752 200.828 440.575 60.839 310.699 60.637 290.954 350.520 400.320 230.755 230.834 390.760 180.772 390.676 220.915 340.862 250.717 27
SAT0.742 270.860 210.765 490.819 280.769 120.848 230.533 220.829 340.663 210.631 310.955 290.586 130.274 470.753 240.896 140.729 270.760 500.666 280.921 290.855 320.733 18
LargeKernel3D0.739 290.909 100.820 100.806 390.740 270.852 180.545 160.826 350.594 520.643 240.955 290.541 300.263 570.723 330.858 280.775 140.767 430.678 190.933 200.848 370.694 36
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 300.776 480.790 360.851 70.754 190.854 140.491 460.866 200.596 510.686 70.955 290.536 320.342 130.624 500.869 220.787 90.802 150.628 400.927 240.875 170.704 33
MinkowskiNetpermissive0.736 300.859 220.818 140.832 240.709 350.840 300.521 280.853 240.660 230.643 240.951 450.544 290.286 390.731 310.893 150.675 550.772 390.683 180.874 660.852 350.727 22
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 320.890 140.837 30.864 20.726 320.873 50.530 250.824 370.489 870.647 210.978 40.609 40.336 150.624 500.733 580.758 190.776 370.570 650.949 80.877 130.728 20
PointTransformer++0.725 330.727 740.811 210.819 280.765 130.841 290.502 360.814 420.621 370.623 340.955 290.556 260.284 400.620 520.866 230.781 110.757 540.648 310.932 220.862 250.709 31
SparseConvNet0.725 330.647 900.821 90.846 130.721 330.869 60.533 220.754 580.603 470.614 360.955 290.572 190.325 210.710 340.870 210.724 310.823 20.628 400.934 190.865 240.683 39
MatchingNet0.724 350.812 370.812 200.810 340.735 290.834 370.495 430.860 230.572 610.602 440.954 350.512 420.280 430.757 210.845 370.725 300.780 350.606 500.937 160.851 360.700 35
INS-Conv-semantic0.717 360.751 610.759 520.812 320.704 360.868 70.537 210.842 290.609 430.608 400.953 390.534 340.293 340.616 530.864 240.719 350.793 280.640 350.933 200.845 410.663 45
PointMetaBase0.714 370.835 280.785 380.821 260.684 420.846 260.531 240.865 210.614 380.596 480.953 390.500 450.246 630.674 360.888 170.692 470.764 460.624 420.849 810.844 420.675 41
contrastBoundarypermissive0.705 380.769 550.775 430.809 350.687 410.820 530.439 730.812 430.661 220.591 500.945 630.515 410.171 910.633 470.856 290.720 330.796 240.668 270.889 510.847 380.689 37
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 390.774 500.800 270.793 460.760 140.847 250.471 510.802 460.463 940.634 300.968 120.491 480.271 510.726 320.910 70.706 410.815 60.551 770.878 600.833 430.570 77
RFCR0.702 400.889 150.745 630.813 310.672 450.818 570.493 440.815 410.623 350.610 380.947 570.470 570.249 620.594 560.848 360.705 420.779 360.646 320.892 490.823 490.611 60
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 Click0.701 410.825 320.796 310.723 620.716 340.832 390.433 750.816 390.634 320.609 390.969 100.418 830.344 120.559 680.833 400.715 370.808 130.560 710.902 410.847 380.680 40
JSENetpermissive0.699 420.881 170.762 500.821 260.667 460.800 690.522 270.792 490.613 390.607 410.935 830.492 470.205 780.576 610.853 330.691 490.758 520.652 300.872 690.828 460.649 49
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-Click0.693 430.743 650.794 330.655 850.684 420.822 500.497 420.719 680.622 360.617 350.977 80.447 700.339 140.750 270.664 740.703 440.790 310.596 550.946 100.855 320.647 50
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 440.732 700.772 440.786 470.677 440.866 80.517 290.848 260.509 800.626 320.952 430.536 320.225 690.545 740.704 650.689 520.810 120.564 700.903 400.854 340.729 19
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 450.884 160.754 560.795 440.647 530.818 570.422 770.802 460.612 400.604 420.945 630.462 600.189 860.563 670.853 330.726 290.765 450.632 380.904 380.821 520.606 64
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 460.704 800.741 670.754 590.656 480.829 420.501 370.741 630.609 430.548 580.950 490.522 390.371 40.633 470.756 530.715 370.771 410.623 430.861 770.814 550.658 46
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 470.866 190.748 600.819 280.645 550.794 720.450 630.802 460.587 540.604 420.945 630.464 590.201 810.554 700.840 380.723 320.732 640.602 530.907 360.822 510.603 67
KP-FCNN0.684 480.847 250.758 540.784 490.647 530.814 600.473 500.772 520.605 450.594 490.935 830.450 680.181 890.587 570.805 480.690 500.785 340.614 460.882 560.819 530.632 56
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 480.728 730.757 550.776 520.690 380.804 670.464 560.816 390.577 600.587 510.945 630.508 440.276 460.671 370.710 630.663 600.750 580.589 600.881 570.832 450.653 48
DGNet0.684 480.712 790.784 390.782 510.658 470.835 360.499 410.823 380.641 290.597 470.950 490.487 500.281 420.575 620.619 780.647 680.764 460.620 450.871 720.846 400.688 38
PointContrast_LA_SEM0.683 510.757 590.784 390.786 470.639 570.824 480.408 800.775 510.604 460.541 600.934 870.532 350.269 530.552 710.777 510.645 710.793 280.640 350.913 350.824 480.671 42
Superpoint Network0.683 510.851 240.728 710.800 430.653 500.806 650.468 530.804 440.572 610.602 440.946 600.453 670.239 660.519 790.822 420.689 520.762 490.595 570.895 470.827 470.630 57
VI-PointConv0.676 530.770 540.754 560.783 500.621 610.814 600.552 130.758 560.571 630.557 560.954 350.529 360.268 550.530 770.682 690.675 550.719 670.603 520.888 520.833 430.665 44
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 540.789 410.748 600.763 570.635 590.814 600.407 820.747 600.581 580.573 530.950 490.484 510.271 510.607 540.754 540.649 650.774 380.596 550.883 550.823 490.606 64
SALANet0.670 550.816 350.770 470.768 540.652 510.807 640.451 600.747 600.659 240.545 590.924 930.473 560.149 1010.571 640.811 470.635 740.746 590.623 430.892 490.794 680.570 77
O3DSeg0.668 560.822 330.771 460.496 1050.651 520.833 380.541 180.761 550.555 690.611 370.966 130.489 490.370 50.388 990.580 810.776 130.751 560.570 650.956 60.817 540.646 51
PointConvpermissive0.666 570.781 450.759 520.699 700.644 560.822 500.475 490.779 500.564 660.504 760.953 390.428 770.203 800.586 590.754 540.661 610.753 550.588 610.902 410.813 570.642 52
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 570.703 810.781 410.751 610.655 490.830 410.471 510.769 530.474 900.537 620.951 450.475 550.279 440.635 450.698 680.675 550.751 560.553 760.816 880.806 590.703 34
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++permissive0.663 590.746 630.708 740.722 630.638 580.820 530.451 600.566 960.599 490.541 600.950 490.510 430.313 250.648 420.819 450.616 790.682 820.590 590.869 730.810 580.656 47
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 600.558 1020.751 580.655 850.690 380.722 940.453 590.867 180.579 590.576 520.893 1050.523 380.293 340.733 300.571 830.692 470.659 890.606 500.875 630.804 610.668 43
DCM-Net0.658 600.778 460.702 770.806 390.619 620.813 630.468 530.693 760.494 830.524 680.941 750.449 690.298 320.510 810.821 430.675 550.727 660.568 680.826 860.803 620.637 54
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 620.698 830.743 650.650 870.564 790.820 530.505 350.758 560.631 330.479 800.945 630.480 530.226 670.572 630.774 520.690 500.735 620.614 460.853 800.776 830.597 70
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 630.752 600.734 690.664 830.583 740.815 590.399 840.754 580.639 300.535 640.942 730.470 570.309 270.665 380.539 850.650 640.708 720.635 370.857 790.793 700.642 52
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 640.778 460.731 700.699 700.577 750.829 420.446 650.736 640.477 890.523 700.945 630.454 640.269 530.484 890.749 570.618 770.738 600.599 540.827 850.792 730.621 59
PointConv-SFPN0.641 650.776 480.703 760.721 640.557 820.826 450.451 600.672 810.563 670.483 790.943 720.425 800.162 960.644 430.726 590.659 620.709 710.572 640.875 630.786 780.559 83
MVPNetpermissive0.641 650.831 290.715 720.671 800.590 700.781 780.394 860.679 780.642 280.553 570.937 800.462 600.256 590.649 410.406 990.626 750.691 790.666 280.877 610.792 730.608 63
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 670.717 780.701 780.692 730.576 760.801 680.467 550.716 690.563 670.459 860.953 390.429 760.169 930.581 600.854 320.605 800.710 690.550 780.894 480.793 700.575 75
FPConvpermissive0.639 680.785 430.760 510.713 680.603 650.798 700.392 870.534 1010.603 470.524 680.948 550.457 620.250 610.538 750.723 610.598 840.696 770.614 460.872 690.799 630.567 80
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 690.797 390.769 480.641 930.590 700.820 530.461 570.537 1000.637 310.536 630.947 570.388 900.206 770.656 390.668 720.647 680.732 640.585 620.868 740.793 700.473 103
PointSPNet0.637 700.734 690.692 850.714 670.576 760.797 710.446 650.743 620.598 500.437 910.942 730.403 860.150 1000.626 490.800 500.649 650.697 760.557 740.846 820.777 820.563 81
SConv0.636 710.830 300.697 810.752 600.572 780.780 800.445 670.716 690.529 730.530 650.951 450.446 710.170 920.507 840.666 730.636 730.682 820.541 840.886 530.799 630.594 71
Supervoxel-CNN0.635 720.656 880.711 730.719 650.613 630.757 890.444 700.765 540.534 720.566 540.928 910.478 540.272 490.636 440.531 870.664 590.645 930.508 910.864 760.792 730.611 60
joint point-basedpermissive0.634 730.614 960.778 420.667 820.633 600.825 460.420 780.804 440.467 920.561 550.951 450.494 460.291 360.566 650.458 940.579 900.764 460.559 730.838 830.814 550.598 69
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 740.731 710.688 880.675 770.591 690.784 770.444 700.565 970.610 410.492 770.949 530.456 630.254 600.587 570.706 640.599 830.665 880.612 490.868 740.791 760.579 74
3DSM_DMMF0.631 750.626 930.745 630.801 420.607 640.751 900.506 340.729 670.565 650.491 780.866 1080.434 720.197 840.595 550.630 770.709 400.705 740.560 710.875 630.740 930.491 98
PointNet2-SFPN0.631 750.771 520.692 850.672 780.524 870.837 330.440 720.706 740.538 710.446 880.944 690.421 820.219 720.552 710.751 560.591 860.737 610.543 830.901 430.768 850.557 84
APCF-Net0.631 750.742 660.687 900.672 780.557 820.792 750.408 800.665 820.545 700.508 730.952 430.428 770.186 870.634 460.702 660.620 760.706 730.555 750.873 670.798 650.581 73
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 780.604 980.741 670.766 560.590 700.747 910.501 370.734 650.503 820.527 660.919 970.454 640.323 220.550 730.420 980.678 540.688 800.544 810.896 460.795 670.627 58
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 790.800 380.625 1010.719 650.545 840.806 650.445 670.597 900.448 970.519 710.938 790.481 520.328 200.489 880.499 920.657 630.759 510.592 580.881 570.797 660.634 55
SegGroup_sempermissive0.627 800.818 340.747 620.701 690.602 660.764 860.385 910.629 870.490 850.508 730.931 900.409 850.201 810.564 660.725 600.618 770.692 780.539 850.873 670.794 680.548 87
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 810.830 300.694 830.757 580.563 800.772 840.448 640.647 850.520 760.509 720.949 530.431 750.191 850.496 860.614 790.647 680.672 860.535 870.876 620.783 790.571 76
dtc_net0.625 810.703 810.751 580.794 450.535 850.848 230.480 480.676 800.528 740.469 830.944 690.454 640.004 1140.464 910.636 760.704 430.758 520.548 800.924 260.787 770.492 97
HPEIN0.618 830.729 720.668 910.647 890.597 680.766 850.414 790.680 770.520 760.525 670.946 600.432 730.215 740.493 870.599 800.638 720.617 980.570 650.897 450.806 590.605 66
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-GCNpermissive0.610 840.858 230.772 440.489 1060.532 860.792 750.404 830.643 860.570 640.507 750.935 830.414 840.046 1110.510 810.702 660.602 820.705 740.549 790.859 780.773 840.534 90
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 850.760 570.667 920.649 880.521 880.793 730.457 580.648 840.528 740.434 930.947 570.401 870.153 990.454 920.721 620.648 670.717 680.536 860.904 380.765 860.485 99
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 860.634 920.743 650.697 720.601 670.781 780.437 740.585 930.493 840.446 880.933 880.394 880.011 1130.654 400.661 750.603 810.733 630.526 880.832 840.761 880.480 100
LAP-D0.594 870.720 760.692 850.637 940.456 980.773 830.391 890.730 660.587 540.445 900.940 770.381 910.288 370.434 950.453 960.591 860.649 910.581 630.777 920.749 920.610 62
DPC0.592 880.720 760.700 790.602 980.480 940.762 880.380 920.713 720.585 570.437 910.940 770.369 930.288 370.434 950.509 910.590 880.639 960.567 690.772 940.755 900.592 72
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 890.766 560.659 960.683 750.470 970.740 930.387 900.620 890.490 850.476 810.922 950.355 960.245 640.511 800.511 900.571 910.643 940.493 950.872 690.762 870.600 68
ROSMRF0.580 900.772 510.707 750.681 760.563 800.764 860.362 940.515 1020.465 930.465 850.936 820.427 790.207 760.438 930.577 820.536 940.675 850.486 960.723 1000.779 800.524 93
SD-DETR0.576 910.746 630.609 1050.445 1100.517 890.643 1050.366 930.714 710.456 950.468 840.870 1070.432 730.264 560.558 690.674 700.586 890.688 800.482 970.739 980.733 950.537 89
SQN_0.1%0.569 920.676 850.696 820.657 840.497 900.779 810.424 760.548 980.515 780.376 980.902 1040.422 810.357 80.379 1000.456 950.596 850.659 890.544 810.685 1030.665 1060.556 85
TextureNetpermissive0.566 930.672 870.664 930.671 800.494 920.719 950.445 670.678 790.411 1030.396 960.935 830.356 950.225 690.412 970.535 860.565 920.636 970.464 990.794 910.680 1030.568 79
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
DVVNet0.562 940.648 890.700 790.770 530.586 730.687 990.333 980.650 830.514 790.475 820.906 1010.359 940.223 710.340 1020.442 970.422 1050.668 870.501 920.708 1010.779 800.534 90
Pointnet++ & Featurepermissive0.557 950.735 680.661 950.686 740.491 930.744 920.392 870.539 990.451 960.375 990.946 600.376 920.205 780.403 980.356 1020.553 930.643 940.497 930.824 870.756 890.515 94
GMLPs0.538 960.495 1070.693 840.647 890.471 960.793 730.300 1010.477 1030.505 810.358 1010.903 1030.327 990.081 1080.472 900.529 880.448 1030.710 690.509 890.746 960.737 940.554 86
PanopticFusion-label0.529 970.491 1080.688 880.604 970.386 1030.632 1060.225 1110.705 750.434 1000.293 1070.815 1090.348 970.241 650.499 850.669 710.507 960.649 910.442 1050.796 900.602 1100.561 82
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_weak0.516 980.676 850.591 1080.609 950.442 990.774 820.335 970.597 900.422 1020.357 1020.932 890.341 980.094 1070.298 1040.528 890.473 1010.676 840.495 940.602 1090.721 980.349 110
Online SegFusion0.515 990.607 970.644 990.579 1000.434 1000.630 1070.353 950.628 880.440 980.410 940.762 1130.307 1010.167 940.520 780.403 1000.516 950.565 1010.447 1030.678 1040.701 1000.514 95
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 1000.558 1020.608 1060.424 1120.478 950.690 980.246 1070.586 920.468 910.450 870.911 990.394 880.160 970.438 930.212 1090.432 1040.541 1070.475 980.742 970.727 960.477 101
PCNN0.498 1010.559 1010.644 990.560 1020.420 1020.711 970.229 1090.414 1040.436 990.352 1030.941 750.324 1000.155 980.238 1090.387 1010.493 970.529 1080.509 890.813 890.751 910.504 96
Weakly-Openseg v30.489 1020.749 620.664 930.646 910.496 910.559 1110.122 1140.577 940.257 1140.364 1000.805 1100.198 1120.096 1060.510 810.496 930.361 1090.563 1020.359 1120.777 920.644 1070.532 92
3DMV0.484 1030.484 1090.538 1100.643 920.424 1010.606 1100.310 990.574 950.433 1010.378 970.796 1110.301 1020.214 750.537 760.208 1100.472 1020.507 1110.413 1080.693 1020.602 1100.539 88
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1040.577 1000.611 1040.356 1140.321 1110.715 960.299 1030.376 1080.328 1100.319 1050.944 690.285 1040.164 950.216 1120.229 1070.484 990.545 1060.456 1010.755 950.709 990.475 102
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1050.679 840.604 1070.578 1010.380 1040.682 1000.291 1040.106 1140.483 880.258 1120.920 960.258 1080.025 1120.231 1110.325 1030.480 1000.560 1040.463 1000.725 990.666 1050.231 114
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1060.474 1100.623 1020.463 1080.366 1060.651 1030.310 990.389 1070.349 1080.330 1040.937 800.271 1060.126 1030.285 1050.224 1080.350 1110.577 1000.445 1040.625 1070.723 970.394 106
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 1070.548 1040.548 1090.597 990.363 1070.628 1080.300 1010.292 1090.374 1050.307 1060.881 1060.268 1070.186 870.238 1090.204 1110.407 1060.506 1120.449 1020.667 1050.620 1090.462 104
SurfaceConvPF0.442 1070.505 1060.622 1030.380 1130.342 1090.654 1020.227 1100.397 1060.367 1060.276 1090.924 930.240 1090.198 830.359 1010.262 1050.366 1070.581 990.435 1060.640 1060.668 1040.398 105
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1090.437 1120.646 980.474 1070.369 1050.645 1040.353 950.258 1110.282 1120.279 1080.918 980.298 1030.147 1020.283 1060.294 1040.487 980.562 1030.427 1070.619 1080.633 1080.352 109
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1100.525 1050.647 970.522 1030.324 1100.488 1140.077 1150.712 730.353 1070.401 950.636 1150.281 1050.176 900.340 1020.565 840.175 1150.551 1050.398 1090.370 1150.602 1100.361 108
SPLAT Netcopyleft0.393 1110.472 1110.511 1110.606 960.311 1120.656 1010.245 1080.405 1050.328 1100.197 1130.927 920.227 1110.000 1160.001 1160.249 1060.271 1140.510 1090.383 1110.593 1100.699 1010.267 112
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 1120.297 1140.491 1120.432 1110.358 1080.612 1090.274 1050.116 1130.411 1030.265 1100.904 1020.229 1100.079 1090.250 1070.185 1120.320 1120.510 1090.385 1100.548 1110.597 1130.394 106
PointNet++permissive0.339 1130.584 990.478 1130.458 1090.256 1140.360 1150.250 1060.247 1120.278 1130.261 1110.677 1140.183 1130.117 1040.212 1130.145 1140.364 1080.346 1150.232 1150.548 1110.523 1140.252 113
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 1140.353 1130.290 1150.278 1150.166 1150.553 1120.169 1130.286 1100.147 1150.148 1150.908 1000.182 1140.064 1100.023 1150.018 1160.354 1100.363 1130.345 1130.546 1130.685 1020.278 111
ScanNetpermissive0.306 1150.203 1150.366 1140.501 1040.311 1120.524 1130.211 1120.002 1160.342 1090.189 1140.786 1120.145 1150.102 1050.245 1080.152 1130.318 1130.348 1140.300 1140.460 1140.437 1150.182 115
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 1160.000 1160.041 1160.172 1160.030 1160.062 1160.001 1160.035 1150.004 1160.051 1160.143 1160.019 1160.003 1150.041 1140.050 1150.003 1160.054 1160.018 1160.005 1160.264 1160.082 116


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Spherical Mask(CtoF)0.812 11.000 10.973 40.852 120.718 40.917 60.574 40.677 260.748 90.729 100.715 50.795 20.809 11.000 10.831 20.854 80.787 81.000 10.638 4
EV3D0.811 21.000 10.968 50.852 120.717 50.921 50.574 50.677 260.748 90.730 90.703 90.795 20.809 11.000 10.831 20.854 80.778 121.000 10.638 5
SIM3D0.803 31.000 10.967 60.863 110.692 140.924 40.552 80.732 200.667 180.732 80.662 120.796 10.789 81.000 10.803 60.864 50.766 171.000 10.643 3
OneFormer3Dcopyleft0.801 41.000 10.973 30.909 50.698 110.928 20.582 30.668 310.685 150.780 20.687 100.698 150.702 131.000 10.794 80.900 20.784 100.986 480.635 6
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception0.800 51.000 10.930 80.872 90.727 30.862 200.454 150.764 130.820 10.746 60.706 70.750 50.772 90.926 410.764 140.818 240.826 10.997 360.660 2
InsSSM0.799 61.000 10.915 100.710 370.729 20.925 30.664 10.670 290.770 60.766 30.739 20.737 60.700 141.000 10.792 90.829 180.815 30.997 360.625 8
TST3D0.795 71.000 10.929 90.918 40.709 80.884 150.596 20.704 230.769 70.734 70.644 170.699 140.751 111.000 10.794 70.876 40.757 190.997 360.550 28
MG-Former0.791 81.000 10.980 20.837 160.626 220.897 80.543 90.759 150.800 50.766 40.659 130.769 40.697 171.000 10.791 100.707 440.791 71.000 10.610 14
ExtMask3D0.789 91.000 10.988 10.756 300.706 90.912 70.429 160.647 360.806 40.755 50.673 110.689 160.772 101.000 10.789 110.852 100.811 41.000 10.617 11
Queryformer0.787 101.000 10.933 70.601 460.754 10.886 130.558 70.661 330.767 80.665 150.716 40.639 210.808 41.000 10.844 10.897 30.804 51.000 10.624 9
MAFT0.786 111.000 10.894 150.807 200.694 130.893 110.486 110.674 280.740 110.786 10.704 80.727 80.739 121.000 10.707 200.849 120.756 201.000 10.685 1
Mask3D0.780 121.000 10.786 390.716 350.696 120.885 140.500 100.714 210.810 30.672 140.715 50.679 170.809 11.000 10.831 20.833 160.787 81.000 10.602 16
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 130.903 520.903 120.806 210.609 280.886 120.568 60.815 60.705 140.711 110.655 140.652 200.685 201.000 10.789 120.809 250.776 141.000 10.583 21
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 141.000 10.803 320.937 10.684 150.865 170.213 310.870 20.664 190.571 210.758 10.702 120.807 51.000 10.653 270.902 10.792 61.000 10.626 7
SoftGrouppermissive0.761 151.000 10.808 280.845 140.716 60.862 190.243 280.824 40.655 210.620 160.734 30.699 130.791 70.981 350.716 180.844 130.769 151.000 10.594 19
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNetpermissive0.757 161.000 10.904 110.731 330.678 160.895 90.458 130.644 380.670 170.710 120.620 220.732 70.650 221.000 10.756 150.778 280.779 111.000 10.614 12
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
TD3Dpermissive0.751 171.000 10.774 400.867 100.621 240.934 10.404 170.706 220.812 20.605 190.633 200.626 220.690 191.000 10.640 290.820 210.777 131.000 10.612 13
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNetpermissive0.747 181.000 10.818 240.837 170.713 70.844 220.457 140.647 360.711 130.614 170.617 240.657 190.650 221.000 10.692 210.822 200.765 181.000 10.595 18
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut0.732 191.000 10.788 370.724 340.642 210.859 210.248 270.787 110.618 240.596 200.653 160.722 100.583 431.000 10.766 130.861 60.825 21.000 10.504 34
IPCA-Inst0.731 201.000 10.788 380.884 80.698 100.788 380.252 260.760 140.646 220.511 290.637 190.665 180.804 61.000 10.644 280.778 290.747 221.000 10.561 25
TopoSeg0.725 211.000 10.806 310.933 20.668 180.758 420.272 250.734 190.630 230.549 250.654 150.606 230.697 180.966 380.612 330.839 140.754 211.000 10.573 22
DKNet0.718 221.000 10.814 250.782 240.619 250.872 160.224 290.751 170.569 280.677 130.585 280.724 90.633 330.981 350.515 430.819 220.736 231.000 10.617 10
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 231.000 10.850 170.924 30.648 190.747 450.162 330.862 30.572 270.520 270.624 210.549 260.649 311.000 10.560 380.706 450.768 161.000 10.591 20
HAISpermissive0.699 241.000 10.849 180.820 180.675 170.808 320.279 230.757 160.465 340.517 280.596 260.559 250.600 371.000 10.654 260.767 310.676 270.994 440.560 26
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 251.000 10.697 560.888 70.556 350.803 330.387 180.626 400.417 390.556 240.585 290.702 110.600 371.000 10.824 50.720 430.692 251.000 10.509 33
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 261.000 10.799 340.811 190.622 230.817 270.376 190.805 90.590 260.487 330.568 320.525 300.650 220.835 510.600 340.829 170.655 301.000 10.526 30
DANCENET0.680 271.000 10.807 290.733 320.600 290.768 410.375 200.543 480.538 290.610 180.599 250.498 310.632 350.981 350.739 170.856 70.633 360.882 590.454 43
SphereSeg0.680 271.000 10.856 160.744 310.618 260.893 100.151 340.651 350.713 120.537 260.579 310.430 400.651 211.000 10.389 540.744 380.697 240.991 460.601 17
Box2Mask0.677 291.000 10.847 190.771 260.509 440.816 280.277 240.558 470.482 310.562 230.640 180.448 360.700 141.000 10.666 220.852 110.578 430.997 360.488 38
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 301.000 10.758 480.682 390.576 330.842 230.477 120.504 540.524 300.567 220.585 300.451 350.557 451.000 10.751 160.797 260.563 461.000 10.467 42
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 311.000 10.822 230.764 290.616 270.815 290.139 380.694 250.597 250.459 370.566 330.599 240.600 370.516 610.715 190.819 230.635 341.000 10.603 15
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 321.000 10.760 460.667 410.581 310.863 180.323 210.655 340.477 320.473 350.549 350.432 390.650 221.000 10.655 250.738 390.585 420.944 510.472 41
CSC-Pretrained0.648 331.000 10.810 260.768 270.523 420.813 300.143 370.819 50.389 420.422 460.511 390.443 370.650 221.000 10.624 310.732 400.634 351.000 10.375 50
PE0.645 341.000 10.773 420.798 230.538 370.786 390.088 460.799 100.350 460.435 440.547 360.545 270.646 320.933 400.562 370.761 340.556 510.997 360.501 36
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 351.000 10.758 470.582 520.539 360.826 260.046 510.765 120.372 440.436 430.588 270.539 290.650 221.000 10.577 350.750 360.653 320.997 360.495 37
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 361.000 10.841 200.893 60.531 390.802 340.115 430.588 450.448 360.438 410.537 380.430 410.550 460.857 430.534 410.764 330.657 290.987 470.568 23
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 371.000 10.895 140.800 220.480 480.676 500.144 360.737 180.354 450.447 380.400 520.365 470.700 141.000 10.569 360.836 150.599 381.000 10.473 40
PointGroup0.636 381.000 10.765 430.624 430.505 460.797 350.116 420.696 240.384 430.441 390.559 340.476 330.596 401.000 10.666 220.756 350.556 500.997 360.513 32
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group0.635 390.667 540.797 360.714 360.562 340.774 400.146 350.810 80.429 380.476 340.546 370.399 430.633 331.000 10.632 300.722 420.609 371.000 10.514 31
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
Mask3D_evaluation0.631 401.000 10.829 220.606 450.646 200.836 240.068 470.511 520.462 350.507 300.619 230.389 450.610 361.000 10.432 490.828 190.673 280.788 630.552 27
DENet0.629 411.000 10.797 350.608 440.589 300.627 540.219 300.882 10.310 480.402 510.383 540.396 440.650 221.000 10.663 240.543 620.691 261.000 10.568 24
3D-MPA0.611 421.000 10.833 210.765 280.526 410.756 430.136 400.588 450.470 330.438 420.432 480.358 490.650 220.857 430.429 500.765 320.557 491.000 10.430 45
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 431.000 10.801 330.599 470.535 380.728 470.286 220.436 580.679 160.491 310.433 460.256 510.404 580.857 430.620 320.724 410.510 561.000 10.539 29
AOIA0.601 441.000 10.761 450.687 380.485 470.828 250.008 580.663 320.405 410.405 500.425 490.490 320.596 400.714 540.553 400.779 270.597 390.992 450.424 47
PCJC0.578 451.000 10.810 270.583 510.449 510.813 310.042 520.603 430.341 470.490 320.465 430.410 420.650 220.835 510.264 600.694 490.561 470.889 560.504 35
SSEN0.575 461.000 10.761 440.473 540.477 490.795 360.066 480.529 500.658 200.460 360.461 440.380 460.331 600.859 420.401 530.692 510.653 311.000 10.348 52
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 470.528 640.708 550.626 420.580 320.745 460.063 490.627 390.240 520.400 520.497 400.464 340.515 471.000 10.475 450.745 370.571 441.000 10.429 46
NeuralBF0.555 480.667 540.896 130.843 150.517 430.751 440.029 530.519 510.414 400.439 400.465 420.000 700.484 490.857 430.287 580.693 500.651 331.000 10.485 39
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML0.549 491.000 10.807 300.588 500.327 560.647 520.004 600.815 70.180 550.418 470.364 560.182 540.445 521.000 10.442 480.688 520.571 451.000 10.396 48
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance0.539 501.000 10.621 590.300 570.530 400.698 480.127 410.533 490.222 530.430 450.400 510.365 470.574 440.938 390.472 460.659 540.543 520.944 510.347 53
One_Thing_One_Clickpermissive0.529 510.667 540.718 510.777 250.399 520.683 490.000 630.669 300.138 580.391 530.374 550.539 280.360 590.641 580.556 390.774 300.593 400.997 360.251 58
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN0.515 521.000 10.538 640.282 580.468 500.790 370.173 320.345 600.429 370.413 490.484 410.176 550.595 420.591 590.522 420.668 530.476 570.986 490.327 54
Occipital-SCS0.512 531.000 10.716 520.509 530.506 450.611 550.092 450.602 440.177 560.346 560.383 530.165 560.442 530.850 500.386 550.618 580.543 530.889 560.389 49
3D-BoNet0.488 541.000 10.672 580.590 490.301 580.484 650.098 440.620 410.306 490.341 570.259 600.125 580.434 550.796 530.402 520.499 640.513 550.909 550.439 44
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst0.478 550.667 540.712 540.595 480.259 610.550 610.000 630.613 420.175 570.250 620.434 450.437 380.411 570.857 430.485 440.591 610.267 670.944 510.359 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)
SPG_WSIS0.470 560.667 540.685 570.677 400.372 540.562 590.000 630.482 550.244 510.316 590.298 570.052 650.442 540.857 430.267 590.702 460.559 481.000 10.287 56
SALoss-ResNet0.459 571.000 10.737 500.159 680.259 600.587 570.138 390.475 560.217 540.416 480.408 500.128 570.315 610.714 540.411 510.536 630.590 410.873 600.304 55
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.447 580.528 640.555 620.381 550.382 53