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 iouwallchairfloortabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PonderV2 ScanNet2000.346 20.552 40.270 40.175 30.810 40.682 40.950 20.560 40.641 60.761 10.398 70.357 60.570 50.113 20.804 30.603 30.750 30.283 20.681 40.952 20.548 20.874 30.852 70.290 60.700 20.356 70.792 30.445 60.545 70.436 60.351 70.787 50.611 50.050 60.290 80.519 80.000 10.825 40.888 20.842 30.259 20.100 20.558 30.070 80.497 50.247 80.457 70.889 20.248 50.106 60.817 70.691 30.094 40.729 10.636 30.620 80.503 70.660 90.243 40.000 30.212 50.590 30.860 60.400 30.881 30.000 30.202 10.622 60.408 50.499 60.261 60.000 10.385 50.636 50.000 40.000 60.000 10.000 30.433 110.843 40.660 30.574 80.481 20.336 30.677 30.486 20.000 30.030 10.000 10.034 40.000 30.080 50.869 70.000 10.000 70.000 70.540 40.727 20.232 110.115 50.186 50.193 50.000 100.403 60.326 30.103 80.000 30.290 30.392 60.000 10.346 40.062 70.424 20.375 40.431 30.667 20.115 80.082 70.239 40.000 10.504 80.606 40.584 60.000 10.002 40.186 40.104 60.000 50.394 20.384 60.083 40.000 40.007 50.000 10.000 10.880 40.000 10.377 60.000 10.263 20.565 20.000 10.608 60.000 10.000 10.304 40.009 50.924 10.000 50.000 50.000 10.000 50.000 10.128 20.584 10.475 40.412 50.076 80.269 30.621 30.509 30.010 30.000 10.491 60.063 10.000 20.472 30.880 10.000 20.000 10.000 10.179 30.125 10.000 20.441 50.000 1
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.
L3DETR-ScanNet_2000.336 40.533 70.279 20.155 40.801 60.689 20.946 30.539 60.660 40.759 20.380 80.333 80.583 10.000 90.788 50.529 60.740 40.261 60.679 60.940 70.525 70.860 60.883 30.226 70.613 60.397 30.720 80.512 40.565 60.620 10.417 30.775 80.629 30.158 20.298 60.579 70.000 10.835 10.883 30.927 10.114 70.079 40.511 60.073 70.508 30.312 30.629 30.861 40.192 100.098 90.908 10.636 70.032 110.563 110.514 90.664 30.505 60.697 50.225 60.000 30.264 10.411 70.860 60.321 70.960 10.058 20.109 80.776 10.526 30.557 20.303 50.000 10.339 60.712 30.000 40.014 40.000 10.000 30.638 70.856 30.641 40.579 70.107 110.119 90.661 50.416 30.000 30.000 40.000 10.007 110.000 30.067 70.910 30.000 10.000 70.000 70.463 50.448 40.294 100.324 10.293 10.211 40.108 50.448 50.068 110.141 30.000 30.330 20.699 10.000 10.256 50.192 30.000 90.355 50.418 40.209 110.146 70.679 10.101 110.000 10.503 90.687 10.671 40.000 10.000 60.174 50.117 20.000 50.122 40.515 20.104 20.259 20.312 10.000 10.000 10.765 70.000 10.369 80.000 10.183 30.422 80.000 10.646 20.000 10.000 10.565 10.001 80.125 110.010 30.002 40.000 10.487 10.000 10.075 100.548 20.420 50.233 100.082 60.138 90.430 80.427 70.000 80.000 10.549 30.000 20.000 20.074 60.409 100.000 20.000 10.000 10.152 50.051 20.000 20.598 30.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
PPT-SpUNet-F.T.0.332 60.556 30.270 30.123 80.816 30.682 40.946 30.549 50.657 50.756 30.459 40.376 50.550 60.001 80.807 20.616 10.727 60.267 40.691 30.942 60.530 60.872 40.874 40.330 40.542 80.374 50.792 30.400 80.673 20.572 40.433 10.793 40.623 40.008 110.351 40.594 60.000 10.783 70.876 40.833 40.213 30.000 70.537 40.091 30.519 20.304 40.620 50.942 10.264 20.124 40.855 30.695 20.086 50.646 50.506 100.658 40.535 30.715 20.314 10.000 30.241 30.608 20.897 20.359 50.858 50.000 30.076 110.611 70.392 60.509 50.378 20.000 10.579 20.565 100.000 40.000 60.000 10.000 30.755 40.806 70.661 10.572 90.350 60.181 60.660 60.300 80.000 30.000 40.000 10.023 60.000 30.042 100.930 20.000 10.000 70.077 40.584 30.392 60.339 60.185 40.171 70.308 20.006 90.563 30.256 50.150 10.000 30.002 100.345 90.000 10.045 80.197 20.063 50.323 80.453 20.600 50.163 60.037 90.349 20.000 10.672 20.679 30.753 10.000 10.000 60.000 60.117 20.000 50.000 60.291 80.000 60.000 40.039 30.000 10.000 10.899 20.000 10.374 70.000 10.000 70.545 40.000 10.634 30.000 10.000 10.074 70.223 30.914 50.000 50.021 30.000 10.000 50.000 10.112 40.498 70.649 10.383 70.095 10.135 100.449 70.432 60.008 50.000 10.518 40.000 20.000 20.000 70.796 20.000 20.000 10.000 10.138 80.000 40.000 20.000 60.000 1
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
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.851 10.687 30.971 10.586 10.755 10.752 40.505 10.404 40.575 20.000 90.848 10.616 10.761 10.349 10.738 10.978 10.546 30.860 60.926 10.346 10.654 30.384 40.828 10.523 30.699 10.583 30.387 50.822 10.688 10.118 40.474 10.603 40.000 10.832 20.903 10.753 70.140 60.000 70.650 10.109 20.520 10.457 10.497 60.871 30.281 10.192 20.887 20.748 10.168 10.727 20.733 10.740 10.644 10.714 30.190 70.000 30.256 20.449 50.914 10.514 10.759 90.337 10.172 30.692 30.617 10.636 10.325 30.000 10.641 10.782 10.000 40.065 20.000 10.000 30.842 10.903 10.661 10.662 20.612 10.405 20.731 10.566 10.000 30.000 40.000 10.017 90.301 10.088 40.941 10.000 10.077 20.000 70.717 20.790 10.310 90.026 110.264 20.349 10.220 20.397 70.366 10.115 70.000 30.337 10.463 40.000 10.531 10.218 10.593 10.455 10.469 10.708 10.210 10.592 20.108 100.000 10.728 10.682 20.671 40.000 10.000 60.407 10.136 10.022 20.575 10.436 40.259 10.428 10.048 20.000 10.000 10.879 50.000 10.480 10.000 10.133 40.597 10.000 10.690 10.000 10.000 10.009 100.000 90.921 20.000 50.151 10.000 10.000 50.000 10.109 60.494 80.622 20.394 60.073 90.141 70.798 10.528 20.026 10.000 10.551 20.000 20.000 20.134 50.717 40.000 20.000 10.000 10.188 20.000 40.000 20.791 10.000 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
OA-CNN-L_ScanNet2000.333 50.558 20.269 50.124 70.821 20.703 10.946 30.569 20.662 20.748 50.487 20.455 10.572 40.000 90.789 40.534 50.736 50.271 30.713 20.949 30.498 100.877 20.860 50.332 30.706 10.474 10.788 50.406 70.637 30.495 50.355 60.805 30.592 90.015 100.396 20.602 50.000 10.799 50.876 40.713 110.276 10.000 70.493 70.080 50.448 90.363 20.661 20.833 50.262 30.125 30.823 60.665 50.076 60.720 30.557 50.637 60.517 50.672 80.227 50.000 30.158 70.496 40.843 80.352 60.835 70.000 30.103 90.711 20.527 20.526 40.320 40.000 10.568 30.625 60.067 10.000 60.000 10.001 20.806 30.836 50.621 60.591 40.373 50.314 40.668 40.398 50.003 20.000 40.000 10.016 100.024 20.043 90.906 40.000 10.052 40.000 70.384 60.330 80.342 50.100 60.223 40.183 70.112 40.476 40.313 40.130 60.196 20.112 60.370 80.000 10.234 60.071 60.160 30.403 30.398 80.492 90.197 20.076 80.272 30.000 10.200 110.560 50.735 30.000 10.000 60.000 60.110 40.002 40.021 50.412 50.000 60.000 40.000 70.000 10.000 10.794 60.000 10.445 20.000 10.022 50.509 50.000 10.517 100.000 10.000 10.001 110.245 20.915 40.024 20.089 20.000 10.262 20.000 10.103 80.524 40.392 70.515 20.013 110.251 40.411 90.662 10.001 70.000 10.473 70.000 20.000 20.150 40.699 50.000 20.000 10.000 10.166 40.000 40.024 10.000 60.000 1
CeCo0.340 30.551 50.247 70.181 20.784 70.661 80.939 70.564 30.624 70.721 60.484 30.429 20.575 20.027 50.774 60.503 80.753 20.242 70.656 70.945 40.534 40.865 50.860 50.177 110.616 50.400 20.818 20.579 10.615 50.367 80.408 40.726 90.633 20.162 10.360 30.619 20.000 10.828 30.873 60.924 20.109 80.083 30.564 20.057 110.475 70.266 60.781 10.767 60.257 40.100 70.825 50.663 60.048 100.620 80.551 60.595 90.532 40.692 60.246 30.000 30.213 40.615 10.861 50.376 40.900 20.000 30.102 100.660 40.321 90.547 30.226 70.000 10.311 70.742 20.011 30.006 50.000 10.000 30.546 100.824 60.345 80.665 10.450 30.435 10.683 20.411 40.338 10.000 40.000 10.030 50.000 30.068 60.892 50.000 10.063 30.000 70.257 70.304 90.387 30.079 80.228 30.190 60.000 100.586 10.347 20.133 40.000 30.037 70.377 70.000 10.384 30.006 100.003 70.421 20.410 70.643 30.171 40.121 40.142 80.000 10.510 70.447 60.474 80.000 10.000 60.286 20.083 70.000 50.000 60.603 10.096 30.063 30.000 70.000 10.000 10.898 30.000 10.429 30.000 10.400 10.550 30.000 10.633 40.000 10.000 10.377 30.000 90.916 30.000 50.000 50.000 10.000 50.000 10.102 90.499 60.296 80.463 30.089 40.304 10.740 20.401 100.010 30.000 10.560 10.000 20.000 20.709 10.652 60.000 20.000 10.000 10.143 60.000 40.000 20.609 20.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS0.305 80.508 80.225 80.142 50.782 80.634 110.937 80.489 90.578 80.721 60.364 90.355 70.515 70.023 60.764 80.523 70.707 80.264 50.633 80.922 80.507 90.886 10.804 90.179 90.436 100.300 80.656 100.529 20.501 90.394 70.296 100.820 20.603 60.131 30.179 110.619 20.000 10.707 100.865 80.773 50.171 40.010 60.484 80.063 90.463 80.254 70.332 100.649 80.220 70.100 70.729 90.613 90.071 80.582 90.628 40.702 20.424 90.749 10.137 90.000 30.142 80.360 80.863 40.305 80.877 40.000 30.173 20.606 80.337 80.478 80.154 90.000 10.253 80.664 40.000 40.000 60.000 10.000 30.626 80.782 80.302 100.602 30.185 90.282 50.651 70.317 70.000 30.000 40.000 10.022 70.000 30.154 10.876 60.000 10.014 60.063 60.029 110.553 30.467 20.084 70.124 80.157 100.049 80.373 80.252 60.097 90.000 30.219 40.542 20.000 10.392 20.172 50.000 90.339 60.417 50.533 80.093 90.115 50.195 60.000 10.516 60.288 100.741 20.000 10.001 50.233 30.056 80.000 50.159 30.334 70.077 50.000 40.000 70.000 10.000 10.749 80.000 10.411 40.000 10.008 60.452 70.000 10.595 70.000 10.000 10.220 60.006 60.894 80.006 40.000 50.000 10.000 50.000 10.112 40.504 50.404 60.551 10.093 30.129 110.484 60.381 110.000 80.000 10.396 90.000 20.000 20.620 20.402 110.000 20.000 10.000 10.142 70.000 40.000 20.512 40.000 1
LGroundpermissive0.272 90.485 90.184 90.106 90.778 90.676 60.932 90.479 110.572 90.718 80.399 60.265 90.453 100.085 30.745 90.446 90.726 70.232 90.622 90.901 90.512 80.826 90.786 100.178 100.549 70.277 90.659 90.381 90.518 80.295 110.323 80.777 70.599 70.028 70.321 50.363 100.000 10.708 90.858 90.746 80.063 90.022 50.457 90.077 60.476 60.243 90.402 80.397 110.233 60.077 110.720 110.610 100.103 30.629 70.437 110.626 70.446 80.702 40.190 70.005 10.058 100.322 90.702 100.244 90.768 80.000 30.134 70.552 90.279 100.395 90.147 100.000 10.207 90.612 80.000 40.000 60.000 10.000 30.658 60.566 90.323 90.525 110.229 80.179 70.467 110.154 100.000 30.002 20.000 10.051 10.000 30.127 20.703 90.000 10.000 70.216 10.112 100.358 70.547 10.187 30.092 100.156 110.055 70.296 90.252 60.143 20.000 30.014 80.398 50.000 10.028 100.173 40.000 90.265 100.348 90.415 100.179 30.019 100.218 50.000 10.597 50.274 110.565 70.000 10.012 30.000 60.039 100.022 20.000 60.117 90.000 60.000 40.000 70.000 10.000 10.324 100.000 10.384 50.000 10.000 70.251 110.000 10.566 80.000 10.000 10.066 80.404 10.886 90.199 10.000 50.000 10.059 30.000 10.136 10.540 30.127 110.295 80.085 50.143 60.514 40.413 90.000 80.000 10.498 50.000 20.000 20.000 70.623 70.000 20.000 10.000 10.132 100.000 40.000 20.000 60.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34Dpermissive0.253 100.463 100.154 110.102 100.771 100.650 100.932 90.483 100.571 100.710 90.331 100.250 100.492 90.044 40.703 100.419 110.606 110.227 100.621 100.865 110.531 50.771 110.813 80.291 50.484 90.242 100.612 110.282 110.440 110.351 90.299 90.622 100.593 80.027 80.293 70.310 110.000 10.757 80.858 90.737 100.150 50.164 10.368 110.084 40.381 110.142 110.357 90.720 70.214 80.092 100.724 100.596 110.056 90.655 40.525 80.581 110.352 110.594 100.056 110.000 30.014 110.224 100.772 90.205 110.720 100.000 30.159 40.531 100.163 110.294 100.136 110.000 10.169 100.589 90.000 40.000 60.000 10.002 10.663 50.466 110.265 110.582 60.337 70.016 100.559 90.084 110.000 30.000 40.000 10.036 30.000 30.125 30.670 100.000 10.102 10.071 50.164 90.406 50.386 40.046 100.068 110.159 90.117 30.284 100.111 100.094 100.000 30.000 110.197 110.000 10.044 90.013 90.002 80.228 110.307 110.588 60.025 110.545 30.134 90.000 10.655 30.302 90.282 110.000 10.060 10.000 60.035 110.000 50.000 60.097 110.000 60.000 40.005 60.000 10.000 10.096 110.000 10.334 100.000 10.000 70.274 100.000 10.513 110.000 10.000 10.280 50.194 40.897 70.000 50.000 50.000 10.000 50.000 10.108 70.279 110.189 100.141 110.059 100.272 20.307 110.445 40.003 60.000 10.353 100.000 20.026 10.000 70.581 90.001 10.000 10.000 10.093 110.002 30.000 20.000 60.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
OctFormer ScanNet200permissive0.326 70.539 60.265 60.131 60.806 50.670 70.943 60.535 70.662 20.705 100.423 50.407 30.505 80.003 70.765 70.582 40.686 90.227 100.680 50.943 50.601 10.854 80.892 20.335 20.417 110.357 60.724 70.453 50.632 40.596 20.432 20.783 60.512 110.021 90.244 90.637 10.000 10.787 60.873 60.743 90.000 110.000 70.534 50.110 10.499 40.289 50.626 40.620 90.168 110.204 10.849 40.679 40.117 20.633 60.684 20.650 50.552 20.684 70.312 20.000 30.175 60.429 60.865 30.413 20.837 60.000 30.145 50.626 50.451 40.487 70.513 10.000 10.529 40.613 70.000 40.033 30.000 10.000 30.828 20.871 20.622 50.587 50.411 40.137 80.645 80.343 60.000 30.000 40.000 10.022 70.000 30.026 110.829 80.000 10.022 50.089 30.842 10.253 100.318 80.296 20.178 60.291 30.224 10.584 20.200 80.132 50.000 30.128 50.227 100.000 10.230 70.047 80.149 40.331 70.412 60.618 40.164 50.102 60.522 10.000 10.655 30.378 70.469 90.000 10.000 60.000 60.105 50.000 50.000 60.483 30.000 60.000 40.028 40.000 10.000 10.906 10.000 10.339 90.000 10.000 70.457 60.000 10.612 50.000 10.000 10.408 20.000 90.900 60.000 50.000 50.000 10.029 40.000 10.074 110.455 90.479 30.427 40.079 70.140 80.496 50.414 80.022 20.000 10.471 80.000 20.000 20.000 70.722 30.000 20.000 10.000 10.138 80.000 40.000 20.000 60.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CSC-Pretrainpermissive0.249 110.455 110.171 100.079 110.766 110.659 90.930 110.494 80.542 110.700 110.314 110.215 110.430 110.121 10.697 110.441 100.683 100.235 80.609 110.895 100.476 110.816 100.770 110.186 80.634 40.216 110.734 60.340 100.471 100.307 100.293 110.591 110.542 100.076 50.205 100.464 90.000 10.484 110.832 110.766 60.052 100.000 70.413 100.059 100.418 100.222 100.318 110.609 100.206 90.112 50.743 80.625 80.076 60.579 100.548 70.590 100.371 100.552 110.081 100.003 20.142 80.201 110.638 110.233 100.686 110.000 30.142 60.444 110.375 70.247 110.198 80.000 10.128 110.454 110.019 20.097 10.000 10.000 30.553 90.557 100.373 70.545 100.164 100.014 110.547 100.174 90.000 30.002 20.000 10.037 20.000 30.063 80.664 110.000 10.000 70.130 20.170 80.152 110.335 70.079 80.110 90.175 80.098 60.175 110.166 90.045 110.207 10.014 80.465 30.000 10.001 110.001 110.046 60.299 90.327 100.537 70.033 100.012 110.186 70.000 10.205 100.377 80.463 100.000 10.058 20.000 60.055 90.041 10.000 60.105 100.000 60.000 40.000 70.000 10.000 10.398 90.000 10.308 110.000 10.000 70.319 90.000 10.543 90.000 10.000 10.062 90.004 70.862 100.000 50.000 50.000 10.000 50.000 10.123 30.316 100.225 90.250 90.094 20.180 50.332 100.441 50.000 80.000 10.310 110.000 20.000 20.000 70.592 80.000 20.000 10.000 10.203 10.000 40.000 20.000 60.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


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




Method Infoavg aphead apcommon aptail apchairtabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
TD3D Scannet200permissive0.211 20.332 20.177 20.103 20.662 10.413 20.463 20.705 10.192 30.145 10.266 20.215 10.452 40.209 20.222 50.219 50.315 20.893 10.380 20.617 20.439 20.047 40.646 10.080 20.610 30.253 10.237 20.293 20.135 10.379 50.494 20.048 10.252 20.451 20.184 20.483 10.395 20.852 10.083 20.551 20.278 20.036 20.337 20.266 20.544 10.963 10.079 50.039 10.740 20.604 20.000 20.586 10.283 20.282 20.059 20.633 30.028 20.004 20.559 20.309 20.420 20.028 51.000 10.000 10.456 10.411 10.372 10.060 40.046 40.000 20.040 40.694 10.083 20.000 20.000 10.000 20.000 30.083 40.252 20.260 50.200 10.160 10.669 20.111 20.000 20.000 10.006 20.169 20.000 10.007 10.296 20.032 10.074 10.139 30.000 20.321 20.031 10.108 20.088 20.157 10.000 10.231 50.026 50.000 20.000 10.356 20.052 20.000 10.240 10.147 10.000 10.015 20.046 30.144 30.073 30.414 10.222 40.000 10.806 10.343 30.486 30.000 10.008 10.038 20.083 10.002 10.028 20.074 20.032 20.150 20.039 20.008 10.000 10.250 40.000 10.125 40.000 10.052 20.260 30.000 10.143 50.000 10.000 10.543 20.207 20.404 10.000 10.003 20.000 10.000 20.000 10.037 20.093 40.272 20.342 10.039 40.281 20.249 30.224 10.000 20.000 10.074 10.000 10.000 10.000 20.278 20.000 10.000 20.889 10.323 10.000 20.014 10.000 20.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Minkowski 34D Inst.permissive0.130 40.246 40.083 40.043 50.547 50.236 40.415 40.672 20.141 50.133 30.067 40.000 30.521 20.114 50.238 40.289 20.232 40.883 20.182 50.373 50.486 10.076 30.488 40.022 40.529 40.199 50.110 40.217 40.100 20.460 40.319 40.000 30.025 50.472 10.000 30.394 30.210 40.537 40.004 40.000 30.083 50.000 50.299 40.061 50.201 50.761 40.084 40.008 30.720 30.557 50.000 20.317 50.280 30.094 50.020 50.564 50.000 40.000 30.400 30.048 40.259 40.101 31.000 10.000 10.190 30.142 50.094 50.137 30.089 30.000 20.101 10.355 50.000 30.000 20.000 10.000 20.000 30.444 20.082 50.384 20.000 50.000 30.334 50.004 50.000 20.000 10.000 30.041 40.000 10.000 30.026 50.000 30.000 30.000 40.000 20.082 50.022 30.000 50.021 40.088 40.000 10.241 40.033 40.000 20.000 10.067 30.000 50.000 10.000 30.000 30.000 10.000 40.026 40.262 20.016 40.000 40.278 10.000 10.500 40.394 10.028 50.000 10.000 30.000 30.000 30.000 20.000 30.019 40.000 30.000 30.000 30.000 30.000 10.156 50.000 10.032 50.000 10.000 30.194 50.000 10.248 40.000 10.000 10.099 40.019 40.308 20.000 10.000 30.000 10.000 20.000 10.007 40.122 20.000 30.175 30.063 20.000 40.271 10.000 50.000 20.000 10.000 50.000 10.000 10.000 20.278 20.000 10.000 20.000 20.111 30.000 20.000 20.000 20.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
Mask3D Scannet2000.278 10.383 10.263 10.168 10.661 20.465 10.572 10.665 30.391 10.121 40.304 10.015 20.647 10.349 10.474 10.489 10.321 10.816 50.351 30.722 10.402 40.195 10.515 30.082 10.795 10.215 20.396 10.377 10.082 40.724 10.586 10.015 20.277 10.377 50.201 10.475 20.572 10.778 30.089 10.759 10.556 10.068 10.506 10.467 10.323 30.778 20.427 10.027 20.789 10.744 10.003 10.570 20.561 10.337 10.265 10.711 10.258 10.031 10.569 10.311 10.441 10.179 11.000 10.000 10.233 20.411 20.283 20.380 10.667 10.016 10.048 30.418 20.139 10.173 10.000 10.086 10.014 20.500 10.384 10.497 10.044 30.032 20.752 10.287 10.003 10.000 10.007 10.208 10.000 10.001 20.349 10.008 20.014 20.509 10.500 10.323 10.023 20.176 10.107 10.105 30.000 10.605 10.378 10.016 10.000 10.400 10.192 10.000 10.048 20.037 20.000 10.275 10.119 10.810 10.258 10.006 30.083 50.000 10.568 20.377 20.708 10.000 10.005 20.147 10.014 20.000 20.556 10.085 10.325 10.500 10.083 10.004 20.000 10.590 10.000 10.365 10.000 10.116 10.491 10.000 10.626 10.000 10.000 10.579 10.391 10.050 40.000 10.028 10.000 10.222 10.000 10.063 10.302 10.356 10.149 40.573 10.415 10.013 50.002 40.004 10.000 10.005 40.000 10.000 10.444 10.514 10.000 10.028 10.000 20.156 20.267 10.000 21.000 10.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
LGround Inst.permissive0.154 30.275 30.108 30.060 30.573 30.381 30.434 30.654 40.190 40.141 20.097 30.000 30.503 30.180 30.252 30.242 40.242 30.881 30.448 10.494 30.429 30.078 20.364 50.024 30.654 20.213 40.222 30.239 30.099 30.616 20.363 30.000 30.092 30.444 30.000 30.383 40.209 50.815 20.030 30.000 30.166 30.002 40.295 50.099 40.364 20.778 20.177 30.001 40.427 50.585 40.000 20.470 30.268 50.205 30.045 30.642 20.007 30.000 30.333 50.148 30.407 30.130 21.000 10.000 10.156 40.189 30.097 40.169 20.000 50.000 20.056 20.400 30.000 30.000 20.000 10.000 20.556 10.278 30.203 30.323 40.019 40.000 30.402 40.026 30.000 20.000 10.000 30.044 30.000 10.000 30.037 40.000 30.000 30.181 20.000 20.127 30.006 40.028 40.023 30.115 20.000 10.327 20.267 20.000 20.000 10.000 40.028 30.000 10.000 30.000 30.000 10.003 30.048 20.135 40.222 20.089 20.278 10.000 10.514 30.333 40.611 20.000 10.000 30.000 30.000 30.000 20.000 30.037 30.000 30.000 30.000 30.000 30.000 10.322 20.000 10.209 20.000 10.000 30.278 20.000 10.302 30.000 10.000 10.143 30.148 30.000 50.000 10.000 30.000 10.000 20.000 10.015 30.064 50.000 30.272 20.031 50.000 40.257 20.028 20.000 20.000 10.041 20.000 10.000 10.000 20.222 50.000 10.000 20.000 20.000 50.000 20.000 20.000 20.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.123 50.223 50.082 50.046 40.564 40.152 50.394 50.578 50.235 20.116 50.034 50.000 30.348 50.119 40.297 20.285 30.202 50.838 40.323 40.407 40.184 50.037 50.516 20.013 50.424 50.214 30.093 50.105 50.078 50.542 30.250 50.000 30.064 40.444 30.000 30.224 50.231 30.537 40.001 50.000 30.126 40.004 30.308 30.193 30.244 40.343 50.228 20.000 50.441 40.588 30.000 20.338 40.275 40.189 40.030 40.600 40.000 40.000 30.378 40.000 50.108 50.098 41.000 10.000 10.096 50.172 40.144 30.011 50.125 20.000 20.000 50.376 40.000 30.000 20.000 10.000 20.000 30.042 50.141 40.377 30.051 20.000 30.483 30.017 40.000 20.000 10.000 30.022 50.000 10.000 30.065 30.000 30.000 30.000 40.000 20.094 40.000 50.042 30.000 50.064 50.000 10.259 30.089 30.000 20.000 10.000 40.022 40.000 10.000 30.000 30.000 10.000 40.018 50.111 50.000 50.000 40.278 10.000 10.444 50.333 40.333 40.000 10.000 30.000 30.000 30.000 20.000 30.000 50.000 30.000 30.000 30.000 30.000 10.267 30.000 10.184 30.000 10.000 30.211 40.000 10.378 20.000 10.000 10.063 50.000 50.275 30.000 10.000 30.000 10.000 20.000 10.007 50.105 30.000 30.032 50.045 30.198 30.171 40.028 20.000 20.000 10.006 30.000 10.000 10.000 20.278 20.000 10.000 20.000 20.044 40.000 20.000 20.000 20.000 1
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort by
OccuSeg+Semantic0.764 80.758 560.796 280.839 170.746 220.907 10.562 100.850 220.680 140.672 130.978 40.610 30.335 150.777 60.819 420.847 10.830 10.691 120.972 20.885 70.727 19
SparseConvNet0.725 300.647 860.821 90.846 120.721 300.869 50.533 200.754 540.603 440.614 330.955 260.572 170.325 190.710 310.870 190.724 280.823 20.628 370.934 170.865 210.683 36
Mix3Dpermissive0.781 30.964 20.855 10.843 150.781 60.858 100.575 50.831 300.685 120.714 20.979 10.594 60.310 240.801 10.892 140.841 20.819 30.723 40.940 110.887 50.725 21
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
BPNetcopyleft0.749 160.909 100.818 130.811 310.752 180.839 300.485 430.842 260.673 150.644 200.957 220.528 340.305 260.773 80.859 240.788 70.818 40.693 110.916 290.856 270.723 22
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
ResLFE_HDS0.772 50.939 40.824 60.854 60.771 80.840 280.564 90.900 70.686 110.677 100.961 140.537 280.348 100.769 100.903 80.785 90.815 50.676 190.939 120.880 100.772 7
OctFormerpermissive0.766 60.925 70.808 200.849 90.786 40.846 240.566 80.876 130.690 90.674 120.960 150.576 150.226 630.753 210.904 70.777 110.815 50.722 50.923 250.877 120.776 6
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
ClickSeg_Semantic0.703 360.774 480.800 240.793 430.760 120.847 230.471 470.802 430.463 900.634 270.968 110.491 450.271 470.726 290.910 50.706 370.815 50.551 730.878 560.833 400.570 73
CU-Hybrid Net0.764 80.924 80.819 110.840 160.757 150.853 140.580 20.848 230.709 30.643 210.958 180.587 100.295 300.753 210.884 180.758 170.815 50.725 30.927 220.867 190.743 13
O-CNNpermissive0.762 100.924 80.823 70.844 140.770 90.852 160.577 30.847 250.711 20.640 250.958 180.592 70.217 690.762 150.888 150.758 170.813 90.726 20.932 200.868 180.744 12
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
VMNetpermissive0.746 200.870 180.838 20.858 40.729 280.850 180.501 330.874 140.587 510.658 170.956 230.564 200.299 280.765 140.900 100.716 330.812 100.631 360.939 120.858 250.709 28
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
PicassoNet-IIpermissive0.692 410.732 670.772 410.786 440.677 410.866 70.517 270.848 230.509 760.626 290.952 390.536 290.225 650.545 710.704 620.689 480.810 110.564 660.903 370.854 310.729 16
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One Thing One Click0.701 380.825 310.796 280.723 590.716 310.832 360.433 710.816 360.634 290.609 350.969 90.418 790.344 110.559 650.833 380.715 340.808 120.560 670.902 380.847 350.680 37
Swin3Dpermissive0.779 40.861 200.818 130.836 180.790 20.875 30.576 40.905 50.704 40.739 10.969 90.611 20.349 90.756 190.958 10.702 410.805 130.708 70.916 290.898 20.801 2
ConDaFormer0.755 120.927 60.822 80.836 180.801 10.849 190.516 280.864 190.651 220.680 90.958 180.584 130.282 380.759 170.855 290.728 250.802 140.678 160.880 550.873 170.756 10
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
MSP0.748 180.623 890.804 220.859 30.745 230.824 440.501 330.912 30.690 90.685 70.956 230.567 180.320 210.768 120.918 40.720 300.802 140.676 190.921 260.881 90.779 5
PPT-SpUNet-Joint0.766 60.932 50.794 300.829 230.751 200.854 120.540 180.903 60.630 310.672 130.963 120.565 190.357 70.788 30.900 100.737 230.802 140.685 140.950 50.887 50.780 4
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
RPN0.736 270.776 460.790 330.851 70.754 170.854 120.491 420.866 170.596 480.686 60.955 260.536 290.342 120.624 470.869 200.787 80.802 140.628 370.927 220.875 160.704 30
PNE0.755 120.786 400.835 40.834 200.758 130.849 190.570 70.836 290.648 240.668 150.978 40.581 140.367 50.683 320.856 270.804 50.801 180.678 160.961 40.889 40.716 26
P. Hermosilla: Point Neighborhood Embeddings.
EQ-Net0.743 230.620 900.799 270.849 90.730 270.822 460.493 400.897 90.664 170.681 80.955 260.562 210.378 30.760 160.903 80.738 220.801 180.673 230.907 330.877 120.745 11
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
Retro-FPN0.744 220.842 260.800 240.767 520.740 240.836 330.541 170.914 20.672 160.626 290.958 180.552 250.272 450.777 60.886 170.696 420.801 180.674 220.941 100.858 250.717 24
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
PointTransformerV20.752 140.742 630.809 190.872 10.758 130.860 90.552 120.891 110.610 380.687 50.960 150.559 220.304 270.766 130.926 30.767 140.797 210.644 300.942 90.876 150.722 23
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
contrastBoundarypermissive0.705 350.769 530.775 400.809 330.687 380.820 490.439 690.812 400.661 190.591 460.945 590.515 380.171 870.633 440.856 270.720 300.796 220.668 240.889 480.847 350.689 34
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
Virtual MVFusion0.746 200.771 500.819 110.848 110.702 340.865 80.397 810.899 80.699 50.664 160.948 510.588 90.330 170.746 250.851 330.764 150.796 220.704 90.935 160.866 200.728 17
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
PointConvFormer0.749 160.793 380.790 330.807 340.750 210.856 110.524 240.881 120.588 500.642 240.977 70.591 80.274 430.781 40.929 20.804 50.796 220.642 310.947 70.885 70.715 27
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
DMF-Net0.752 140.906 120.793 320.802 380.689 370.825 420.556 110.867 150.681 130.602 400.960 150.555 240.365 60.779 50.859 240.747 200.795 250.717 60.917 280.856 270.764 8
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
INS-Conv-semantic0.717 330.751 590.759 480.812 300.704 330.868 60.537 190.842 260.609 400.608 360.953 350.534 310.293 310.616 500.864 220.719 320.793 260.640 320.933 180.845 380.663 42
PointContrast_LA_SEM0.683 480.757 570.784 360.786 440.639 530.824 440.408 760.775 480.604 430.541 560.934 830.532 320.269 490.552 680.777 480.645 670.793 260.640 320.913 320.824 450.671 39
PonderV20.785 20.978 10.800 240.833 210.788 30.853 140.545 150.910 40.713 10.705 40.979 10.596 50.390 10.769 100.832 390.821 30.792 280.730 10.975 10.897 30.785 3
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
One-Thing-One-Click0.693 400.743 620.794 300.655 820.684 390.822 460.497 380.719 640.622 330.617 320.977 70.447 660.339 130.750 240.664 710.703 400.790 290.596 520.946 80.855 290.647 47
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PTv3 ScanNet0.794 10.941 30.813 160.851 70.782 50.890 20.597 10.916 10.696 70.713 30.979 10.635 10.384 20.793 20.907 60.821 30.790 290.696 100.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
OA-CNN-L_ScanNet200.756 110.783 420.826 50.858 40.776 70.837 310.548 140.896 100.649 230.675 110.962 130.586 110.335 150.771 90.802 460.770 130.787 310.691 120.936 150.880 100.761 9
KP-FCNN0.684 450.847 250.758 500.784 460.647 490.814 560.473 460.772 490.605 420.594 450.935 790.450 640.181 850.587 540.805 450.690 460.785 320.614 430.882 520.819 500.632 52
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
MatchingNet0.724 320.812 350.812 170.810 320.735 260.834 350.495 390.860 200.572 580.602 400.954 320.512 390.280 400.757 180.845 350.725 270.780 330.606 470.937 140.851 330.700 32
RFCR0.702 370.889 150.745 590.813 290.672 420.818 530.493 400.815 380.623 320.610 340.947 530.470 530.249 580.594 530.848 340.705 380.779 340.646 290.892 460.823 460.611 56
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
IPCA0.731 290.890 140.837 30.864 20.726 290.873 40.530 230.824 340.489 830.647 180.978 40.609 40.336 140.624 470.733 550.758 170.776 350.570 620.949 60.877 120.728 17
ROSMRF3D0.673 510.789 390.748 560.763 540.635 550.814 560.407 780.747 560.581 550.573 490.950 450.484 470.271 470.607 510.754 510.649 610.774 360.596 520.883 510.823 460.606 60
LRPNet0.742 240.816 330.806 210.807 340.752 180.828 400.575 50.839 280.699 50.637 260.954 320.520 370.320 210.755 200.834 370.760 160.772 370.676 190.915 310.862 220.717 24
MinkowskiNetpermissive0.736 270.859 220.818 130.832 220.709 320.840 280.521 260.853 210.660 200.643 210.951 410.544 260.286 360.731 280.893 130.675 510.772 370.683 150.874 620.852 320.727 19
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
FusionNet0.688 430.704 760.741 630.754 560.656 450.829 380.501 330.741 590.609 400.548 540.950 450.522 360.371 40.633 440.756 500.715 340.771 390.623 400.861 730.814 510.658 43
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
StratifiedFormerpermissive0.747 190.901 130.803 230.845 130.757 150.846 240.512 290.825 330.696 70.645 190.956 230.576 150.262 540.744 260.861 230.742 210.770 400.705 80.899 410.860 240.734 14
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
LargeKernel3D0.739 260.909 100.820 100.806 360.740 240.852 160.545 150.826 320.594 490.643 210.955 260.541 270.263 530.723 300.858 260.775 120.767 410.678 160.933 180.848 340.694 33
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
Feature_GeometricNetpermissive0.690 420.884 160.754 520.795 410.647 490.818 530.422 730.802 430.612 370.604 380.945 590.462 560.189 820.563 640.853 310.726 260.765 420.632 350.904 350.821 490.606 60
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
DGNet0.684 450.712 750.784 360.782 480.658 440.835 340.499 370.823 350.641 260.597 430.950 450.487 460.281 390.575 590.619 750.647 640.764 430.620 420.871 680.846 370.688 35
PointMetaBase0.714 340.835 270.785 350.821 240.684 390.846 240.531 220.865 180.614 350.596 440.953 350.500 420.246 590.674 330.888 150.692 430.764 430.624 390.849 770.844 390.675 38
joint point-basedpermissive0.634 690.614 910.778 390.667 790.633 560.825 420.420 740.804 410.467 880.561 510.951 410.494 430.291 330.566 620.458 890.579 860.764 430.559 690.838 790.814 510.598 65
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
Superpoint Network0.683 480.851 240.728 670.800 400.653 470.806 610.468 490.804 410.572 580.602 400.946 560.453 630.239 620.519 760.822 400.689 480.762 460.595 540.895 440.827 440.630 53
SAT0.742 240.860 210.765 450.819 260.769 100.848 210.533 200.829 310.663 180.631 280.955 260.586 110.274 430.753 210.896 120.729 240.760 470.666 250.921 260.855 290.733 15
DenSeR0.628 750.800 360.625 960.719 620.545 800.806 610.445 630.597 860.448 930.519 670.938 750.481 480.328 180.489 840.499 880.657 590.759 480.592 550.881 530.797 620.634 51
dtc_net0.625 770.703 770.751 540.794 420.535 810.848 210.480 440.676 760.528 700.469 790.944 650.454 600.004 1090.464 870.636 730.704 390.758 490.548 760.924 240.787 730.492 92
JSENetpermissive0.699 390.881 170.762 460.821 240.667 430.800 650.522 250.792 460.613 360.607 370.935 790.492 440.205 740.576 580.853 310.691 450.758 490.652 270.872 650.828 430.649 46
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
PointTransformer++0.725 300.727 710.811 180.819 260.765 110.841 270.502 320.814 390.621 340.623 310.955 260.556 230.284 370.620 490.866 210.781 100.757 510.648 280.932 200.862 220.709 28
PointConvpermissive0.666 530.781 430.759 480.699 670.644 520.822 460.475 450.779 470.564 630.504 720.953 350.428 730.203 760.586 560.754 510.661 570.753 520.588 580.902 380.813 530.642 48
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 530.703 770.781 380.751 580.655 460.830 370.471 470.769 500.474 860.537 580.951 410.475 510.279 410.635 420.698 650.675 510.751 530.553 720.816 840.806 550.703 31
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
VACNN++0.684 450.728 700.757 510.776 490.690 350.804 630.464 520.816 360.577 570.587 470.945 590.508 410.276 420.671 340.710 600.663 560.750 540.589 570.881 530.832 420.653 45
SALANet0.670 520.816 330.770 430.768 510.652 480.807 600.451 560.747 560.659 210.545 550.924 890.473 520.149 970.571 610.811 440.635 700.746 550.623 400.892 460.794 640.570 73
RandLA-Netpermissive0.645 600.778 440.731 660.699 670.577 710.829 380.446 610.736 600.477 850.523 660.945 590.454 600.269 490.484 850.749 540.618 730.738 560.599 510.827 810.792 690.621 55
PointNet2-SFPN0.631 710.771 500.692 810.672 750.524 830.837 310.440 680.706 700.538 670.446 840.944 650.421 780.219 680.552 680.751 530.591 820.737 570.543 790.901 400.768 810.557 80
HPGCNN0.656 580.698 790.743 610.650 840.564 750.820 490.505 310.758 520.631 300.479 760.945 590.480 490.226 630.572 600.774 490.690 460.735 580.614 430.853 760.776 790.597 66
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
wsss-transformer0.600 820.634 870.743 610.697 690.601 630.781 740.437 700.585 890.493 800.446 840.933 840.394 840.011 1080.654 370.661 720.603 770.733 590.526 840.832 800.761 840.480 95
PD-Net0.638 650.797 370.769 440.641 890.590 660.820 490.461 530.537 950.637 280.536 590.947 530.388 860.206 730.656 360.668 690.647 640.732 600.585 590.868 700.793 660.473 98
Feature-Geometry Netpermissive0.685 440.866 190.748 560.819 260.645 510.794 680.450 590.802 430.587 510.604 380.945 590.464 550.201 770.554 670.840 360.723 290.732 600.602 500.907 330.822 480.603 63
DCM-Net0.658 560.778 440.702 730.806 360.619 580.813 590.468 490.693 720.494 790.524 640.941 710.449 650.298 290.510 780.821 410.675 510.727 620.568 640.826 820.803 580.637 50
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
VI-PointConv0.676 500.770 520.754 520.783 470.621 570.814 560.552 120.758 520.571 600.557 520.954 320.529 330.268 510.530 740.682 660.675 510.719 630.603 490.888 490.833 400.665 41
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
AttAN0.609 810.760 550.667 880.649 850.521 840.793 690.457 540.648 800.528 700.434 890.947 530.401 830.153 950.454 880.721 590.648 630.717 640.536 820.904 350.765 820.485 94
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
GMLPs0.538 920.495 1020.693 800.647 860.471 910.793 690.300 970.477 980.505 770.358 960.903 990.327 950.081 1030.472 860.529 840.448 990.710 650.509 850.746 910.737 900.554 82
PointMRNet0.640 630.717 740.701 740.692 700.576 720.801 640.467 510.716 650.563 640.459 820.953 350.429 720.169 890.581 570.854 300.605 760.710 650.550 740.894 450.793 660.575 71
PointConv-SFPN0.641 610.776 460.703 720.721 610.557 780.826 410.451 560.672 770.563 640.483 750.943 680.425 760.162 920.644 400.726 560.659 580.709 670.572 610.875 590.786 740.559 79
SAFNet-segpermissive0.654 590.752 580.734 650.664 800.583 700.815 550.399 800.754 540.639 270.535 600.942 690.470 530.309 250.665 350.539 810.650 600.708 680.635 340.857 750.793 660.642 48
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
APCF-Net0.631 710.742 630.687 860.672 750.557 780.792 710.408 760.665 780.545 660.508 690.952 390.428 730.186 830.634 430.702 630.620 720.706 690.555 710.873 630.798 610.581 69
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
SPH3D-GCNpermissive0.610 800.858 230.772 410.489 1010.532 820.792 710.404 790.643 820.570 610.507 710.935 790.414 800.046 1060.510 780.702 630.602 780.705 700.549 750.859 740.773 800.534 86
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
3DSM_DMMF0.631 710.626 880.745 590.801 390.607 600.751 860.506 300.729 630.565 620.491 740.866 1040.434 680.197 800.595 520.630 740.709 360.705 700.560 670.875 590.740 890.491 93
PointSPNet0.637 660.734 660.692 810.714 640.576 720.797 670.446 610.743 580.598 470.437 870.942 690.403 820.150 960.626 460.800 470.649 610.697 720.557 700.846 780.777 780.563 77
FPConvpermissive0.639 640.785 410.760 470.713 650.603 610.798 660.392 830.534 960.603 440.524 640.948 510.457 580.250 570.538 720.723 580.598 800.696 730.614 430.872 650.799 590.567 76
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
SegGroup_sempermissive0.627 760.818 320.747 580.701 660.602 620.764 820.385 870.629 830.490 810.508 690.931 860.409 810.201 770.564 630.725 570.618 730.692 740.539 810.873 630.794 640.548 83
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MVPNetpermissive0.641 610.831 280.715 680.671 770.590 660.781 740.394 820.679 740.642 250.553 530.937 760.462 560.256 550.649 380.406 940.626 710.691 750.666 250.877 570.792 690.608 59
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
SD-DETR0.576 870.746 600.609 1000.445 1050.517 850.643 1010.366 890.714 670.456 910.468 800.870 1030.432 690.264 520.558 660.674 670.586 850.688 760.482 930.739 930.733 910.537 85
FusionAwareConv0.630 740.604 930.741 630.766 530.590 660.747 870.501 330.734 610.503 780.527 620.919 930.454 600.323 200.550 700.420 930.678 500.688 760.544 770.896 430.795 630.627 54
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
SConv0.636 670.830 290.697 770.752 570.572 740.780 760.445 630.716 650.529 690.530 610.951 410.446 670.170 880.507 800.666 700.636 690.682 780.541 800.886 500.799 590.594 67
PPCNN++permissive0.663 550.746 600.708 700.722 600.638 540.820 490.451 560.566 910.599 460.541 560.950 450.510 400.313 230.648 390.819 420.616 750.682 780.590 560.869 690.810 540.656 44
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
subcloud_weak0.516 940.676 810.591 1030.609 910.442 940.774 780.335 930.597 860.422 980.357 970.932 850.341 940.094 1020.298 990.528 850.473 970.676 800.495 900.602 1040.721 940.349 105
ROSMRF0.580 860.772 490.707 710.681 730.563 760.764 820.362 900.515 970.465 890.465 810.936 780.427 750.207 720.438 890.577 780.536 900.675 810.486 920.723 950.779 760.524 88
SIConv0.625 770.830 290.694 790.757 550.563 760.772 800.448 600.647 810.520 720.509 680.949 490.431 710.191 810.496 820.614 760.647 640.672 820.535 830.876 580.783 750.571 72
DVVNet0.562 900.648 850.700 750.770 500.586 690.687 950.333 940.650 790.514 750.475 780.906 970.359 900.223 670.340 970.442 920.422 1010.668 830.501 880.708 960.779 760.534 86
PointMTL0.632 700.731 680.688 840.675 740.591 650.784 730.444 660.565 920.610 380.492 730.949 490.456 590.254 560.587 540.706 610.599 790.665 840.612 460.868 700.791 720.579 70
MVF-GNN0.658 560.558 970.751 540.655 820.690 350.722 900.453 550.867 150.579 560.576 480.893 1010.523 350.293 310.733 270.571 790.692 430.659 850.606 470.875 590.804 570.668 40
SQN_0.1%0.569 880.676 810.696 780.657 810.497 860.779 770.424 720.548 930.515 740.376 940.902 1000.422 770.357 70.379 950.456 900.596 810.659 850.544 770.685 980.665 1020.556 81
LAP-D0.594 830.720 720.692 810.637 900.456 930.773 790.391 850.730 620.587 510.445 860.940 730.381 870.288 340.434 910.453 910.591 820.649 870.581 600.777 880.749 880.610 58
PanopticFusion-label0.529 930.491 1030.688 840.604 930.386 980.632 1020.225 1070.705 710.434 960.293 1020.815 1050.348 930.241 610.499 810.669 680.507 920.649 870.442 1010.796 860.602 1050.561 78
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Supervoxel-CNN0.635 680.656 840.711 690.719 620.613 590.757 850.444 660.765 510.534 680.566 500.928 870.478 500.272 450.636 410.531 830.664 550.645 890.508 870.864 720.792 690.611 56
Pointnet++ & Featurepermissive0.557 910.735 650.661 900.686 710.491 880.744 880.392 830.539 940.451 920.375 950.946 560.376 880.205 740.403 940.356 970.553 890.643 900.497 890.824 830.756 850.515 89
CCRFNet0.589 850.766 540.659 910.683 720.470 920.740 890.387 860.620 850.490 810.476 770.922 910.355 920.245 600.511 770.511 860.571 870.643 900.493 910.872 650.762 830.600 64
DPC0.592 840.720 720.700 750.602 940.480 890.762 840.380 880.713 680.585 540.437 870.940 730.369 890.288 340.434 910.509 870.590 840.639 920.567 650.772 890.755 860.592 68
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
TextureNetpermissive0.566 890.672 830.664 890.671 770.494 870.719 910.445 630.678 750.411 990.396 920.935 790.356 910.225 650.412 930.535 820.565 880.636 930.464 950.794 870.680 990.568 75
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
HPEIN0.618 790.729 690.668 870.647 860.597 640.766 810.414 750.680 730.520 720.525 630.946 560.432 690.215 700.493 830.599 770.638 680.617 940.570 620.897 420.806 550.605 62
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SurfaceConvPF0.442 1020.505 1010.622 980.380 1080.342 1040.654 980.227 1060.397 1010.367 1020.276 1040.924 890.240 1050.198 790.359 960.262 1000.366 1030.581 950.435 1020.640 1010.668 1000.398 100
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
DGCNN_reproducecopyleft0.446 1010.474 1050.623 970.463 1030.366 1010.651 990.310 950.389 1020.349 1040.330 990.937 760.271 1020.126 990.285 1000.224 1030.350 1060.577 960.445 1000.625 1020.723 930.394 101
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
Online SegFusion0.515 950.607 920.644 940.579 960.434 950.630 1030.353 910.628 840.440 940.410 900.762 1080.307 970.167 900.520 750.403 950.516 910.565 970.447 990.678 990.701 960.514 90
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
Tangent Convolutionspermissive0.438 1040.437 1070.646 930.474 1020.369 1000.645 1000.353 910.258 1060.282 1080.279 1030.918 940.298 990.147 980.283 1010.294 990.487 940.562 980.427 1030.619 1030.633 1030.352 104
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
FCPNpermissive0.447 1000.679 800.604 1020.578 970.380 990.682 960.291 1000.106 1090.483 840.258 1070.920 920.258 1040.025 1070.231 1060.325 980.480 960.560 990.463 960.725 940.666 1010.231 109
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
3DWSSS0.425 1050.525 1000.647 920.522 990.324 1050.488 1090.077 1100.712 690.353 1030.401 910.636 1100.281 1010.176 860.340 970.565 800.175 1100.551 1000.398 1050.370 1100.602 1050.361 103
PointCNN with RGBpermissive0.458 990.577 950.611 990.356 1090.321 1060.715 920.299 990.376 1030.328 1060.319 1000.944 650.285 1000.164 910.216 1070.229 1020.484 950.545 1010.456 970.755 900.709 950.475 97
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
3DMV, FTSDF0.501 960.558 970.608 1010.424 1070.478 900.690 940.246 1030.586 880.468 870.450 830.911 950.394 840.160 930.438 890.212 1040.432 1000.541 1020.475 940.742 920.727 920.477 96
PCNN0.498 970.559 960.644 940.560 980.420 970.711 930.229 1050.414 990.436 950.352 980.941 710.324 960.155 940.238 1040.387 960.493 930.529 1030.509 850.813 850.751 870.504 91
SPLAT Netcopyleft0.393 1060.472 1060.511 1060.606 920.311 1070.656 970.245 1040.405 1000.328 1060.197 1080.927 880.227 1070.000 1110.001 1110.249 1010.271 1090.510 1040.383 1070.593 1050.699 970.267 107
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 1070.297 1090.491 1070.432 1060.358 1030.612 1050.274 1010.116 1080.411 990.265 1050.904 980.229 1060.079 1040.250 1020.185 1070.320 1070.510 1040.385 1060.548 1060.597 1080.394 101
3DMV0.484 980.484 1040.538 1050.643 880.424 960.606 1060.310 950.574 900.433 970.378 930.796 1060.301 980.214 710.537 730.208 1050.472 980.507 1060.413 1040.693 970.602 1050.539 84
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PNET20.442 1020.548 990.548 1040.597 950.363 1020.628 1040.300 970.292 1040.374 1010.307 1010.881 1020.268 1030.186 830.238 1040.204 1060.407 1020.506 1070.449 980.667 1000.620 1040.462 99
SSC-UNetpermissive0.308 1090.353 1080.290 1100.278 1100.166 1100.553 1070.169 1090.286 1050.147 1100.148 1100.908 960.182 1090.064 1050.023 1100.018 1110.354 1050.363 1080.345 1080.546 1080.685 980.278 106
ScanNetpermissive0.306 1100.203 1100.366 1090.501 1000.311 1070.524 1080.211 1080.002 1110.342 1050.189 1090.786 1070.145 1100.102 1010.245 1030.152 1080.318 1080.348 1090.300 1090.460 1090.437 1100.182 110
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
PointNet++permissive0.339 1080.584 940.478 1080.458 1040.256 1090.360 1100.250 1020.247 1070.278 1090.261 1060.677 1090.183 1080.117 1000.212 1080.145 1090.364 1040.346 1100.232 1100.548 1060.523 1090.252 108
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ERROR0.054 1110.000 1110.041 1110.172 1110.030 1110.062 1110.001 1110.035 1100.004 1110.051 1110.143 1110.019 1110.003 1100.041 1090.050 1100.003 1110.054 1110.018 1110.005 1110.264 1110.082 111


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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort by
OneFormer3Dcopyleft0.566 80.781 180.697 30.562 20.431 70.770 20.331 10.400 370.373 100.529 10.504 130.568 100.475 200.732 190.470 70.762 10.550 110.871 260.379 11
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
GraphCut0.552 111.000 10.611 110.438 180.392 130.714 140.139 150.598 100.327 130.389 140.510 100.598 50.427 310.754 130.463 110.761 20.588 40.903 130.329 20
Spherical Mask(CtoF)0.616 10.946 30.654 80.555 30.434 60.769 30.271 50.604 70.447 30.505 40.549 10.698 10.716 10.775 90.480 50.747 30.575 60.925 70.436 3
Queryformer0.583 50.926 50.702 20.393 270.504 10.733 130.276 40.527 230.373 90.479 80.534 70.533 140.697 40.720 210.436 130.745 40.592 30.958 30.363 14
ExtMask3D0.598 20.852 120.692 40.433 210.461 40.791 10.264 60.488 290.493 10.508 30.528 90.594 60.706 30.791 50.483 30.734 50.595 20.911 100.437 2
PBNetpermissive0.573 70.926 50.575 170.619 10.472 20.736 110.239 90.487 300.383 80.459 100.506 120.533 130.585 90.767 100.404 150.717 60.559 90.969 20.381 10
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
SPFormerpermissive0.549 120.745 210.640 90.484 90.395 120.739 90.311 30.566 140.335 120.468 90.492 140.555 120.478 190.747 150.436 120.712 70.540 120.893 170.343 19
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
Box2Mask0.433 280.741 220.463 410.433 200.283 310.625 350.103 180.298 500.125 400.260 300.424 240.322 210.472 210.701 250.363 180.711 80.309 490.882 210.272 35
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
SoftGroup++0.513 150.704 270.578 160.398 260.363 210.704 150.061 270.647 40.297 200.378 170.537 60.343 180.614 80.828 40.295 250.710 90.505 170.875 250.394 7
DANCENET0.504 170.926 50.579 130.472 110.367 180.626 340.165 140.432 320.221 250.408 130.449 210.411 160.564 110.746 160.421 140.707 100.438 240.846 340.288 30
UniPerception0.588 40.963 20.667 60.493 80.472 30.750 70.229 100.528 220.468 20.498 70.542 30.643 20.530 150.661 290.463 100.695 110.599 10.972 10.420 5
MAFT0.596 30.889 90.721 10.448 150.460 50.768 40.251 70.558 160.408 40.504 50.539 50.616 40.618 70.858 20.482 40.684 120.551 100.931 60.450 1
TD3Dpermissive0.489 190.852 120.511 310.434 190.322 250.735 120.101 200.512 250.355 110.349 180.468 180.283 260.514 170.676 280.268 300.671 130.510 140.908 110.329 21
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
SIM3D0.575 60.889 90.675 50.284 430.401 110.762 60.329 20.531 210.408 50.521 20.541 40.587 70.646 50.744 170.467 80.665 140.579 50.886 200.425 4
DualGroup0.469 220.815 150.552 200.398 250.374 160.683 200.130 160.539 190.310 140.327 210.407 250.276 270.447 240.535 390.342 190.659 150.455 220.900 160.301 25
ISBNetpermissive0.559 100.939 40.655 70.383 300.426 80.763 50.180 120.534 200.386 60.499 60.509 110.621 30.427 300.704 240.467 90.649 160.571 80.948 40.401 6
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
Mask3D0.566 80.926 50.597 120.408 240.420 90.737 100.239 80.598 90.386 70.458 110.549 10.568 110.716 10.601 350.480 50.646 170.575 60.922 80.364 13
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SoftGrouppermissive0.504 170.667 340.579 140.372 320.381 140.694 170.072 230.677 20.303 160.387 150.531 80.319 220.582 100.754 120.318 210.643 180.492 180.907 120.388 8
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
DKNet0.532 130.815 150.624 100.517 50.377 150.749 80.107 170.509 260.304 150.437 120.475 150.581 80.539 130.775 80.339 200.640 190.506 150.901 140.385 9
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSTNetpermissive0.506 160.738 240.549 220.497 70.316 260.693 180.178 130.377 400.198 290.330 190.463 190.576 90.515 160.857 30.494 10.637 200.457 210.943 50.290 29
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
TopoSeg0.479 210.704 270.564 180.467 130.366 190.633 320.068 240.554 170.262 220.328 200.447 220.323 200.534 140.722 200.288 270.614 210.482 190.912 90.358 17
RPGN0.428 290.630 420.508 330.367 330.249 380.658 260.016 460.673 30.131 380.234 350.383 300.270 280.434 260.748 140.274 290.609 220.406 290.842 380.267 36
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
OccuSeg+instance0.486 200.802 170.536 240.428 220.369 170.702 160.205 110.331 450.301 170.379 160.474 160.327 190.437 250.862 10.485 20.601 230.394 320.846 360.273 33
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
AOIA0.387 360.704 270.515 300.385 290.225 430.669 220.005 530.482 310.126 390.181 480.269 440.221 360.426 320.478 450.218 370.592 240.371 350.851 300.242 39
IPCA-Inst0.520 140.889 90.551 210.548 40.418 100.665 240.064 260.585 110.260 230.277 280.471 170.500 150.644 60.785 60.369 160.591 250.511 130.878 230.362 15
Dyco3Dcopyleft0.395 340.642 400.518 280.447 160.259 370.666 230.050 320.251 550.166 320.231 360.362 310.232 340.331 410.535 380.229 350.587 260.438 250.850 310.317 22
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
SSEN0.384 370.852 120.494 370.192 490.226 420.648 290.022 410.398 380.299 190.277 270.317 370.231 350.194 520.514 420.196 390.586 270.444 230.843 370.184 45
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Mask3D_evaluation0.382 380.593 440.520 270.390 280.314 270.600 380.018 450.287 530.151 340.281 250.387 280.169 430.429 270.654 310.172 450.578 280.384 340.670 520.278 32
Mask-Group0.434 270.778 190.516 290.471 120.330 240.658 250.029 380.526 240.249 240.256 310.400 260.309 230.384 380.296 550.368 170.575 290.425 270.877 240.362 16
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
DD-UNet+Group0.436 250.630 420.508 340.480 100.310 280.624 360.065 250.638 50.174 310.256 320.384 290.194 390.428 280.759 110.289 260.574 300.400 300.849 330.291 28
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
3D-MPA0.355 420.457 540.484 380.299 400.277 330.591 410.047 340.332 430.212 270.217 390.278 400.193 400.413 350.410 490.195 400.574 310.352 380.849 320.213 42
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.392 350.778 190.530 250.220 480.278 320.567 420.083 220.330 460.299 180.270 290.310 380.143 450.260 450.624 330.277 280.568 320.361 370.865 280.301 24
HAISpermissive0.457 240.704 270.561 190.457 140.364 200.673 210.046 350.547 180.194 300.308 230.426 230.288 250.454 230.711 220.262 310.563 330.434 260.889 190.344 18
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
PointGroup0.407 310.639 410.496 350.415 230.243 400.645 300.021 430.570 130.114 410.211 430.359 320.217 370.428 290.660 300.256 320.562 340.341 410.860 290.291 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]
RWSeg0.348 440.475 510.456 420.320 390.275 340.476 470.020 440.491 280.056 520.212 420.320 360.261 290.302 430.520 400.182 430.557 350.285 510.867 270.197 43
One_Thing_One_Clickpermissive0.326 460.472 520.361 470.232 470.183 470.555 430.000 590.498 270.038 540.195 460.226 480.362 170.168 530.469 470.251 330.553 360.335 430.846 350.117 54
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PCJC0.375 390.704 270.542 230.284 420.197 460.649 280.006 500.426 330.138 360.242 330.304 390.183 420.388 370.629 320.141 520.546 370.344 400.738 470.283 31
PE0.396 330.667 340.467 390.446 170.243 390.624 370.022 420.577 120.106 420.219 380.340 330.239 320.487 180.475 460.225 360.541 380.350 390.818 410.273 34
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
INS-Conv-instance0.435 260.716 260.495 360.355 340.331 230.689 190.102 190.394 390.208 280.280 260.395 270.250 300.544 120.741 180.309 230.536 390.391 330.842 390.258 37
GICN0.341 450.580 460.371 460.344 360.198 450.469 480.052 300.564 150.093 430.212 410.212 490.127 470.347 400.537 370.206 380.525 400.329 440.729 480.241 40
CSC-Pretrained0.405 320.738 240.465 400.331 380.205 440.655 270.051 310.601 80.092 450.211 440.329 350.198 380.459 220.775 70.195 410.524 410.400 310.878 220.184 46
ClickSeg_Instance0.366 400.654 380.375 450.184 500.302 290.592 400.050 330.300 490.093 440.283 240.277 410.249 310.426 330.615 340.299 240.504 420.367 360.832 400.191 44
SSEC0.465 230.667 340.578 150.502 60.362 220.641 310.035 360.605 60.291 210.323 220.451 200.296 240.417 340.677 270.245 340.501 430.506 160.900 150.366 12
MTML0.282 490.577 470.380 440.182 510.107 550.430 500.001 560.422 340.057 510.179 490.162 520.070 520.229 470.511 430.161 460.491 440.313 460.650 550.162 48
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
NeuralBF0.353 430.593 440.511 320.375 310.264 350.597 390.008 480.332 440.160 330.229 370.274 430.000 660.206 490.678 260.155 490.485 450.422 280.816 420.254 38
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
SegGroup_inspermissive0.246 540.556 480.335 500.062 610.115 540.490 460.000 590.297 510.018 580.186 470.142 530.083 500.233 460.216 570.153 500.469 460.251 520.744 460.083 57
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
Occipital-SCS0.320 470.679 330.352 480.334 370.229 410.436 490.025 390.412 360.058 500.161 530.240 460.085 490.262 440.496 440.187 420.467 470.328 450.775 430.231 41
SPG_WSIS0.251 530.380 560.274 550.289 410.144 500.413 520.000 590.311 470.065 480.113 550.130 550.029 580.204 500.388 500.108 550.459 480.311 470.769 440.127 53
SphereSeg0.357 410.651 390.411 430.345 350.264 360.630 330.059 280.289 520.212 260.240 340.336 340.158 440.305 420.557 360.159 480.455 490.341 420.726 490.294 26
DENet0.413 300.741 220.520 260.237 460.284 300.523 450.097 210.691 10.138 350.209 450.229 470.238 330.390 360.707 230.310 220.448 500.470 200.892 180.310 23
Region-18class0.146 590.175 650.321 530.080 560.062 570.357 550.000 590.307 480.002 630.066 600.044 600.000 660.018 640.036 650.054 570.447 510.133 590.472 580.060 61
Sparse R-CNN0.292 480.704 270.213 580.153 520.154 490.551 440.053 290.212 560.132 370.174 500.274 420.070 510.363 390.441 480.176 440.424 520.234 530.758 450.161 50
3D-BEVIS0.117 610.250 610.308 540.020 650.009 670.269 630.006 510.008 650.029 550.037 650.014 660.003 640.036 630.147 610.042 600.381 530.118 620.362 610.069 60
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
MASCpermissive0.254 510.463 530.249 570.113 530.167 480.412 530.000 580.374 410.073 460.173 510.243 450.130 460.228 480.368 510.160 470.356 540.208 540.711 510.136 52
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.161 560.407 550.155 630.068 580.043 620.346 570.001 550.134 580.005 610.088 560.106 570.037 560.135 570.321 540.028 620.339 550.116 630.466 590.093 56
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SALoss-ResNet0.262 500.667 340.335 490.067 590.123 530.427 510.022 400.280 540.058 490.216 400.211 500.039 550.142 550.519 410.106 560.338 560.310 480.721 500.138 51
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)
UNet-backbone0.161 560.519 490.259 560.084 550.059 580.325 600.002 540.093 610.009 600.077 590.064 580.045 540.044 620.161 590.045 580.331 570.180 560.566 560.033 66
Hier3Dcopyleft0.117 610.222 630.161 620.054 630.027 640.289 610.000 590.124 590.001 650.079 580.061 590.027 590.141 560.240 560.005 660.310 580.129 600.153 660.081 58
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
3D-BoNet0.253 520.519 490.324 520.251 450.137 520.345 580.031 370.419 350.069 470.162 520.131 540.052 530.202 510.338 530.147 510.301 590.303 500.651 540.178 47
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
ASIS0.085 650.037 660.080 660.066 600.047 610.282 620.000 590.052 620.002 640.047 640.026 640.001 650.046 610.194 580.031 610.264 600.140 580.167 650.047 65
R-PointNet0.158 580.356 570.173 610.113 540.140 510.359 540.012 470.023 640.039 530.134 540.123 560.008 620.089 580.149 600.117 540.221 610.128 610.563 570.094 55
PanopticFusion-inst0.214 550.250 610.330 510.275 440.103 560.228 640.000 590.345 420.024 560.088 570.203 510.186 410.167 540.367 520.125 530.221 620.112 640.666 530.162 49
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SemRegionNet-20cls0.121 600.296 590.203 590.071 570.058 590.349 560.000 590.150 570.019 570.054 620.034 630.017 610.052 600.042 640.013 650.209 630.183 550.371 600.057 62
Sem_Recon_ins0.098 640.295 600.187 600.015 660.036 630.213 650.005 520.038 630.003 620.056 610.037 610.036 570.015 650.051 620.044 590.209 640.098 650.354 620.071 59
tmp0.113 630.333 580.151 640.056 620.053 600.344 590.000 590.105 600.016 590.049 630.035 620.020 600.053 590.048 630.013 640.183 650.173 570.344 630.054 63
Sgpn_scannet0.049 660.023 670.134 650.031 640.013 660.144 660.006 490.008 660.000 660.028 660.017 650.003 630.009 670.000 660.021 630.122 660.095 660.175 640.054 64
MaskRCNN 2d->3d Proj0.022 670.185 640.000 670.000 670.015 650.000 670.000 570.006 670.000 660.010 670.006 670.107 480.012 660.000 660.002 670.027 670.004 670.022 670.001 67


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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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 3