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
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ALS-MinkowskiNetcopyleft0.414 10.610 10.322 20.271 10.852 10.710 10.973 10.572 20.719 20.795 10.477 40.506 10.601 10.000 100.804 40.646 20.804 10.344 20.777 10.984 10.671 10.879 20.936 10.342 30.632 50.449 20.817 30.475 60.723 10.798 10.376 70.832 10.693 10.031 80.564 10.510 100.000 10.893 10.905 10.672 130.314 10.000 70.718 10.153 10.542 10.397 20.726 20.752 70.252 60.226 10.916 10.800 10.047 120.807 20.769 10.709 20.630 20.769 10.217 80.000 30.285 10.598 30.846 80.535 10.956 20.000 30.137 80.784 10.464 50.463 100.230 80.000 10.598 20.662 60.000 40.087 20.000 10.135 10.900 10.780 100.703 10.741 10.571 20.149 90.697 30.646 10.000 30.076 10.000 10.025 70.000 30.106 40.981 10.000 10.043 50.113 30.888 10.248 120.404 30.252 40.314 10.220 50.245 10.466 60.366 10.159 20.000 30.149 50.690 20.000 10.531 20.253 10.285 40.460 10.440 40.813 10.230 10.283 40.159 90.000 10.728 10.666 40.958 10.000 10.021 40.252 40.118 30.000 50.445 30.223 90.285 10.194 30.390 20.000 10.475 10.842 70.000 10.455 30.000 10.250 40.458 70.000 10.865 10.000 10.000 10.635 10.359 20.972 10.087 20.447 10.000 10.000 50.000 10.129 20.532 50.446 60.503 30.071 110.135 110.699 30.717 10.097 10.000 10.665 10.000 20.000 21.000 10.752 40.000 20.000 10.000 10.142 80.200 10.259 11.000 10.000 1
PTv3 ScanNet2000.393 20.592 20.330 10.216 20.851 20.687 50.971 20.586 10.755 10.752 60.505 10.404 50.575 30.000 100.848 10.616 30.761 20.349 10.738 20.978 20.546 50.860 70.926 20.346 20.654 30.384 50.828 10.523 30.699 20.583 40.387 60.822 20.688 20.118 40.474 20.603 40.000 10.832 40.903 20.753 80.140 70.000 70.650 30.109 30.520 20.457 10.497 80.871 30.281 20.192 30.887 30.748 20.168 10.727 40.733 20.740 10.644 10.714 40.190 90.000 30.256 30.449 70.914 10.514 20.759 110.337 10.172 40.692 50.617 10.636 10.325 40.000 10.641 10.782 10.000 40.065 30.000 10.000 40.842 20.903 10.661 30.662 30.612 10.405 20.731 10.566 20.000 30.000 60.000 10.017 110.301 10.088 50.941 20.000 10.077 20.000 90.717 40.790 10.310 100.026 130.264 30.349 10.220 30.397 90.366 10.115 90.000 30.337 10.463 60.000 10.531 20.218 20.593 10.455 20.469 10.708 30.210 20.592 20.108 120.000 10.728 10.682 20.671 50.000 10.000 80.407 10.136 20.022 20.575 10.436 40.259 30.428 10.048 40.000 10.000 20.879 50.000 10.480 20.000 10.133 60.597 10.000 10.690 20.000 10.000 10.009 120.000 110.921 30.000 60.151 30.000 10.000 50.000 10.109 70.494 100.622 20.394 70.073 100.141 70.798 10.528 40.026 20.000 10.551 40.000 20.000 20.134 60.717 60.000 20.000 10.000 10.188 30.000 50.000 30.791 20.000 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 (Oral)
CeCo0.340 50.551 70.247 90.181 40.784 90.661 100.939 90.564 40.624 90.721 80.484 30.429 30.575 30.027 60.774 80.503 100.753 40.242 90.656 90.945 60.534 60.865 60.860 70.177 130.616 60.400 30.818 20.579 10.615 70.367 100.408 50.726 110.633 30.162 10.360 50.619 20.000 10.828 50.873 80.924 20.109 90.083 30.564 40.057 130.475 90.266 70.781 10.767 60.257 50.100 90.825 70.663 80.048 110.620 100.551 80.595 110.532 60.692 70.246 40.000 30.213 50.615 10.861 50.376 60.900 40.000 30.102 120.660 60.321 110.547 40.226 90.000 10.311 90.742 20.011 30.006 60.000 10.000 40.546 110.824 70.345 100.665 20.450 50.435 10.683 40.411 60.338 10.000 60.000 10.030 60.000 30.068 70.892 60.000 10.063 30.000 90.257 90.304 100.387 40.079 100.228 40.190 80.000 120.586 10.347 30.133 60.000 30.037 90.377 90.000 10.384 50.006 120.003 90.421 30.410 90.643 50.171 50.121 50.142 100.000 10.510 90.447 80.474 100.000 10.000 80.286 30.083 90.000 50.000 80.603 10.096 50.063 40.000 90.000 10.000 20.898 30.000 10.429 50.000 10.400 10.550 30.000 10.633 50.000 10.000 10.377 40.000 110.916 40.000 60.000 70.000 10.000 50.000 10.102 100.499 80.296 100.463 40.089 50.304 10.740 20.401 120.010 40.000 10.560 30.000 20.000 20.709 20.652 80.000 20.000 10.000 10.143 70.000 50.000 30.609 30.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
OA-CNN-L_ScanNet2000.333 70.558 30.269 70.124 90.821 40.703 20.946 50.569 30.662 30.748 70.487 20.455 20.572 50.000 100.789 60.534 70.736 70.271 40.713 30.949 50.498 120.877 30.860 70.332 50.706 10.474 10.788 60.406 90.637 40.495 70.355 80.805 40.592 100.015 120.396 40.602 50.000 10.799 70.876 60.713 120.276 20.000 70.493 90.080 70.448 110.363 30.661 30.833 50.262 40.125 50.823 80.665 70.076 70.720 50.557 70.637 70.517 70.672 90.227 60.000 30.158 90.496 50.843 90.352 80.835 90.000 30.103 110.711 40.527 20.526 50.320 50.000 10.568 50.625 80.067 10.000 70.000 10.001 30.806 40.836 60.621 80.591 60.373 70.314 50.668 60.398 70.003 20.000 60.000 10.016 120.024 20.043 110.906 50.000 10.052 40.000 90.384 80.330 90.342 60.100 80.223 50.183 90.112 50.476 50.313 60.130 80.196 20.112 80.370 100.000 10.234 80.071 70.160 50.403 40.398 100.492 110.197 30.076 90.272 30.000 10.200 130.560 70.735 40.000 10.000 80.000 80.110 60.002 40.021 70.412 50.000 80.000 50.000 90.000 10.000 20.794 80.000 10.445 40.000 10.022 70.509 60.000 10.517 110.000 10.000 10.001 130.245 30.915 50.024 30.089 40.000 10.262 20.000 10.103 90.524 60.392 90.515 20.013 130.251 40.411 110.662 20.001 80.000 10.473 90.000 20.000 20.150 50.699 70.000 20.000 10.000 10.166 50.000 50.024 20.000 70.000 1
PonderV2 ScanNet2000.346 40.552 60.270 60.175 50.810 60.682 60.950 40.560 50.641 80.761 20.398 90.357 80.570 60.113 20.804 40.603 50.750 50.283 30.681 50.952 40.548 40.874 40.852 90.290 80.700 20.356 90.792 40.445 80.545 90.436 80.351 90.787 60.611 60.050 70.290 100.519 90.000 10.825 60.888 40.842 30.259 30.100 20.558 50.070 100.497 60.247 100.457 90.889 20.248 70.106 80.817 90.691 50.094 50.729 30.636 50.620 100.503 90.660 100.243 50.000 30.212 60.590 40.860 60.400 40.881 50.000 30.202 20.622 80.408 70.499 70.261 70.000 10.385 70.636 70.000 40.000 70.000 10.000 40.433 120.843 50.660 50.574 100.481 30.336 40.677 50.486 40.000 30.030 20.000 10.034 50.000 30.080 60.869 80.000 10.000 80.000 90.540 60.727 20.232 130.115 70.186 60.193 70.000 120.403 80.326 50.103 100.000 30.290 30.392 80.000 10.346 60.062 80.424 30.375 50.431 50.667 40.115 100.082 80.239 50.000 10.504 100.606 50.584 80.000 10.002 60.186 60.104 80.000 50.394 40.384 60.083 60.000 50.007 70.000 10.000 20.880 40.000 10.377 80.000 10.263 30.565 20.000 10.608 70.000 10.000 10.304 60.009 70.924 20.000 60.000 70.000 10.000 50.000 10.128 30.584 20.475 50.412 60.076 90.269 30.621 50.509 50.010 40.000 10.491 80.063 10.000 20.472 40.880 20.000 20.000 10.000 10.179 40.125 20.000 30.441 60.000 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 60.533 90.279 40.155 60.801 80.689 30.946 50.539 70.660 60.759 30.380 100.333 100.583 20.000 100.788 70.529 80.740 60.261 80.679 70.940 90.525 90.860 70.883 50.226 90.613 70.397 40.720 90.512 40.565 80.620 20.417 40.775 90.629 40.158 20.298 80.579 80.000 10.835 20.883 50.927 10.114 80.079 40.511 80.073 90.508 40.312 40.629 40.861 40.192 120.098 110.908 20.636 90.032 130.563 130.514 110.664 40.505 80.697 60.225 70.000 30.264 20.411 90.860 60.321 90.960 10.058 20.109 100.776 20.526 30.557 20.303 60.000 10.339 80.712 40.000 40.014 50.000 10.000 40.638 80.856 30.641 60.579 90.107 130.119 110.661 70.416 50.000 30.000 60.000 10.007 130.000 30.067 80.910 40.000 10.000 80.000 90.463 70.448 50.294 120.324 10.293 20.211 60.108 60.448 70.068 130.141 50.000 30.330 20.699 10.000 10.256 70.192 40.000 110.355 60.418 60.209 130.146 80.679 10.101 130.000 10.503 110.687 10.671 50.000 10.000 80.174 70.117 40.000 50.122 60.515 20.104 40.259 20.312 30.000 10.000 20.765 90.000 10.369 100.000 10.183 50.422 100.000 10.646 30.000 10.000 10.565 20.001 100.125 130.010 40.002 60.000 10.487 10.000 10.075 120.548 30.420 70.233 120.082 70.138 100.430 100.427 90.000 90.000 10.549 50.000 20.000 20.074 70.409 120.000 20.000 10.000 10.152 60.051 30.000 30.598 40.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
AWCS0.305 100.508 100.225 100.142 70.782 100.634 130.937 100.489 110.578 100.721 80.364 110.355 90.515 80.023 70.764 100.523 90.707 100.264 70.633 100.922 100.507 110.886 10.804 110.179 110.436 120.300 100.656 120.529 20.501 110.394 90.296 120.820 30.603 70.131 30.179 130.619 20.000 10.707 120.865 100.773 60.171 50.010 60.484 100.063 110.463 100.254 90.332 120.649 90.220 90.100 90.729 110.613 110.071 90.582 110.628 60.702 30.424 110.749 20.137 110.000 30.142 100.360 100.863 40.305 100.877 60.000 30.173 30.606 100.337 100.478 90.154 110.000 10.253 100.664 50.000 40.000 70.000 10.000 40.626 90.782 90.302 120.602 50.185 110.282 60.651 90.317 90.000 30.000 60.000 10.022 90.000 30.154 10.876 70.000 10.014 70.063 80.029 130.553 40.467 20.084 90.124 100.157 120.049 100.373 100.252 80.097 110.000 30.219 40.542 30.000 10.392 40.172 60.000 110.339 70.417 70.533 100.093 110.115 60.195 70.000 10.516 80.288 120.741 30.000 10.001 70.233 50.056 100.000 50.159 50.334 70.077 70.000 50.000 90.000 10.000 20.749 100.000 10.411 60.000 10.008 80.452 90.000 10.595 80.000 10.000 10.220 80.006 80.894 90.006 50.000 70.000 10.000 50.000 10.112 50.504 70.404 80.551 10.093 40.129 130.484 80.381 130.000 90.000 10.396 110.000 20.000 20.620 30.402 130.000 20.000 10.000 10.142 80.000 50.000 30.512 50.000 1
OctFormer ScanNet200permissive0.326 90.539 80.265 80.131 80.806 70.670 90.943 80.535 80.662 30.705 120.423 70.407 40.505 90.003 80.765 90.582 60.686 110.227 120.680 60.943 70.601 20.854 90.892 40.335 40.417 130.357 80.724 80.453 70.632 50.596 30.432 30.783 70.512 120.021 110.244 110.637 10.000 10.787 80.873 80.743 100.000 130.000 70.534 70.110 20.499 50.289 60.626 50.620 100.168 130.204 20.849 60.679 60.117 30.633 80.684 30.650 60.552 40.684 80.312 20.000 30.175 80.429 80.865 30.413 30.837 80.000 30.145 60.626 70.451 60.487 80.513 10.000 10.529 60.613 90.000 40.033 40.000 10.000 40.828 30.871 20.622 70.587 70.411 60.137 100.645 100.343 80.000 30.000 60.000 10.022 90.000 30.026 130.829 90.000 10.022 60.089 50.842 20.253 110.318 90.296 20.178 70.291 30.224 20.584 20.200 100.132 70.000 30.128 70.227 120.000 10.230 90.047 90.149 60.331 80.412 80.618 60.164 60.102 70.522 10.000 10.655 40.378 90.469 110.000 10.000 80.000 80.105 70.000 50.000 80.483 30.000 80.000 50.028 60.000 10.000 20.906 10.000 10.339 110.000 10.000 90.457 80.000 10.612 60.000 10.000 10.408 30.000 110.900 70.000 60.000 70.000 10.029 40.000 10.074 130.455 110.479 40.427 50.079 80.140 80.496 70.414 100.022 30.000 10.471 100.000 20.000 20.000 80.722 50.000 20.000 10.000 10.138 100.000 50.000 30.000 70.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-F.T.0.332 80.556 40.270 50.123 100.816 50.682 60.946 50.549 60.657 70.756 40.459 50.376 70.550 70.001 90.807 30.616 30.727 80.267 50.691 40.942 80.530 80.872 50.874 60.330 60.542 100.374 60.792 40.400 100.673 30.572 50.433 20.793 50.623 50.008 130.351 60.594 60.000 10.783 90.876 60.833 40.213 40.000 70.537 60.091 50.519 30.304 50.620 60.942 10.264 30.124 60.855 50.695 40.086 60.646 70.506 120.658 50.535 50.715 30.314 10.000 30.241 40.608 20.897 20.359 70.858 70.000 30.076 130.611 90.392 80.509 60.378 30.000 10.579 30.565 120.000 40.000 70.000 10.000 40.755 50.806 80.661 30.572 110.350 80.181 70.660 80.300 100.000 30.000 60.000 10.023 80.000 30.042 120.930 30.000 10.000 80.077 60.584 50.392 70.339 70.185 60.171 80.308 20.006 110.563 30.256 70.150 30.000 30.002 120.345 110.000 10.045 100.197 30.063 70.323 90.453 30.600 70.163 70.037 110.349 20.000 10.672 30.679 30.753 20.000 10.000 80.000 80.117 40.000 50.000 80.291 80.000 80.000 50.039 50.000 10.000 20.899 20.000 10.374 90.000 10.000 90.545 40.000 10.634 40.000 10.000 10.074 90.223 40.914 60.000 60.021 50.000 10.000 50.000 10.112 50.498 90.649 10.383 80.095 20.135 110.449 90.432 80.008 60.000 10.518 60.000 20.000 20.000 80.796 30.000 20.000 10.000 10.138 100.000 50.000 30.000 70.000 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
BFANet ScanNet200permissive0.360 30.553 50.293 30.193 30.827 30.689 30.970 30.528 90.661 50.753 50.436 60.378 60.469 110.042 50.810 20.654 10.760 30.266 60.659 80.973 30.574 30.849 100.897 30.382 10.546 90.372 70.698 100.491 50.617 60.526 60.436 10.764 100.476 130.101 50.409 30.585 70.000 10.835 20.901 30.810 50.102 100.000 70.688 20.096 40.483 70.264 80.612 70.591 120.358 10.161 40.863 40.707 30.128 20.814 10.669 40.629 80.563 30.651 110.258 30.000 30.194 70.494 60.806 100.394 50.953 30.000 30.233 10.757 30.508 40.556 30.476 20.000 10.573 40.741 30.000 40.000 70.000 10.000 40.000 130.852 40.678 20.616 40.460 40.338 30.710 20.534 30.000 30.025 30.000 10.043 20.000 30.056 100.493 130.000 10.000 80.109 40.785 30.590 30.298 110.282 30.143 90.262 40.053 90.526 40.337 40.215 10.000 30.135 60.510 40.000 10.596 10.043 100.511 20.321 100.459 20.772 20.124 90.060 100.266 40.000 10.574 70.568 60.653 70.000 10.093 10.298 20.239 10.000 50.516 20.129 100.284 20.000 50.431 10.000 10.000 20.848 60.000 10.492 10.000 10.376 20.522 50.000 10.469 130.000 10.000 10.330 50.151 60.875 110.000 60.254 20.000 10.000 50.000 10.088 110.661 10.481 30.255 100.105 10.139 90.666 40.641 30.000 90.000 10.614 20.000 20.000 20.000 80.921 10.000 20.000 10.000 10.497 10.000 50.000 30.000 70.000 1
CSC-Pretrainpermissive0.249 130.455 130.171 120.079 130.766 130.659 110.930 130.494 100.542 130.700 130.314 130.215 130.430 130.121 10.697 130.441 120.683 120.235 100.609 130.895 120.476 130.816 120.770 130.186 100.634 40.216 130.734 70.340 120.471 120.307 120.293 130.591 130.542 110.076 60.205 120.464 110.000 10.484 130.832 130.766 70.052 120.000 70.413 120.059 120.418 120.222 120.318 130.609 110.206 110.112 70.743 100.625 100.076 70.579 120.548 90.590 120.371 120.552 130.081 120.003 20.142 100.201 130.638 130.233 120.686 130.000 30.142 70.444 130.375 90.247 130.198 100.000 10.128 130.454 130.019 20.097 10.000 10.000 40.553 100.557 120.373 90.545 120.164 120.014 130.547 120.174 110.000 30.002 40.000 10.037 30.000 30.063 90.664 120.000 10.000 80.130 20.170 100.152 130.335 80.079 100.110 110.175 100.098 70.175 130.166 110.045 130.207 10.014 100.465 50.000 10.001 130.001 130.046 80.299 110.327 120.537 90.033 120.012 130.186 80.000 10.205 120.377 100.463 120.000 10.058 30.000 80.055 110.041 10.000 80.105 120.000 80.000 50.000 90.000 10.000 20.398 110.000 10.308 130.000 10.000 90.319 110.000 10.543 100.000 10.000 10.062 110.004 90.862 120.000 60.000 70.000 10.000 50.000 10.123 40.316 120.225 110.250 110.094 30.180 50.332 120.441 70.000 90.000 10.310 130.000 20.000 20.000 80.592 100.000 20.000 10.000 10.203 20.000 50.000 30.000 70.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGroundpermissive0.272 110.485 110.184 110.106 110.778 110.676 80.932 110.479 130.572 110.718 100.399 80.265 110.453 120.085 30.745 110.446 110.726 90.232 110.622 110.901 110.512 100.826 110.786 120.178 120.549 80.277 110.659 110.381 110.518 100.295 130.323 100.777 80.599 80.028 90.321 70.363 120.000 10.708 110.858 110.746 90.063 110.022 50.457 110.077 80.476 80.243 110.402 100.397 130.233 80.077 130.720 130.610 120.103 40.629 90.437 130.626 90.446 100.702 50.190 90.005 10.058 120.322 110.702 120.244 110.768 100.000 30.134 90.552 110.279 120.395 110.147 120.000 10.207 110.612 100.000 40.000 70.000 10.000 40.658 70.566 110.323 110.525 130.229 100.179 80.467 130.154 120.000 30.002 40.000 10.051 10.000 30.127 20.703 100.000 10.000 80.216 10.112 120.358 80.547 10.187 50.092 120.156 130.055 80.296 110.252 80.143 40.000 30.014 100.398 70.000 10.028 120.173 50.000 110.265 120.348 110.415 120.179 40.019 120.218 60.000 10.597 60.274 130.565 90.000 10.012 50.000 80.039 120.022 20.000 80.117 110.000 80.000 50.000 90.000 10.000 20.324 120.000 10.384 70.000 10.000 90.251 130.000 10.566 90.000 10.000 10.066 100.404 10.886 100.199 10.000 70.000 10.059 30.000 10.136 10.540 40.127 130.295 90.085 60.143 60.514 60.413 110.000 90.000 10.498 70.000 20.000 20.000 80.623 90.000 20.000 10.000 10.132 120.000 50.000 30.000 70.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34Dpermissive0.253 120.463 120.154 130.102 120.771 120.650 120.932 110.483 120.571 120.710 110.331 120.250 120.492 100.044 40.703 120.419 130.606 130.227 120.621 120.865 130.531 70.771 130.813 100.291 70.484 110.242 120.612 130.282 130.440 130.351 110.299 110.622 120.593 90.027 100.293 90.310 130.000 10.757 100.858 110.737 110.150 60.164 10.368 130.084 60.381 130.142 130.357 110.720 80.214 100.092 120.724 120.596 130.056 100.655 60.525 100.581 130.352 130.594 120.056 130.000 30.014 130.224 120.772 110.205 130.720 120.000 30.159 50.531 120.163 130.294 120.136 130.000 10.169 120.589 110.000 40.000 70.000 10.002 20.663 60.466 130.265 130.582 80.337 90.016 120.559 110.084 130.000 30.000 60.000 10.036 40.000 30.125 30.670 110.000 10.102 10.071 70.164 110.406 60.386 50.046 120.068 130.159 110.117 40.284 120.111 120.094 120.000 30.000 130.197 130.000 10.044 110.013 110.002 100.228 130.307 130.588 80.025 130.545 30.134 110.000 10.655 40.302 110.282 130.000 10.060 20.000 80.035 130.000 50.000 80.097 130.000 80.000 50.005 80.000 10.000 20.096 130.000 10.334 120.000 10.000 90.274 120.000 10.513 120.000 10.000 10.280 70.194 50.897 80.000 60.000 70.000 10.000 50.000 10.108 80.279 130.189 120.141 130.059 120.272 20.307 130.445 60.003 70.000 10.353 120.000 20.026 10.000 80.581 110.001 10.000 10.000 10.093 130.002 40.000 30.000 70.000 1
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%chairtabledoorcouchcabinetshelfdeskoffice 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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 by
Mask3D Scannet2000.388 10.542 10.357 10.237 10.808 20.676 20.741 10.832 40.496 10.151 30.628 20.021 20.955 10.578 10.753 10.612 10.591 10.822 50.609 30.926 10.614 30.291 10.725 40.163 10.890 20.380 50.615 10.517 10.130 30.806 10.857 20.024 20.511 10.412 50.226 10.597 20.756 11.000 10.111 10.792 10.736 10.091 10.610 10.527 20.323 41.000 10.504 10.063 21.000 10.853 10.010 10.974 30.839 10.667 10.301 10.883 10.266 10.039 10.640 10.311 20.739 20.463 11.000 10.000 10.287 20.715 20.313 20.600 11.000 10.027 10.076 40.502 50.500 10.409 10.000 10.194 10.125 20.500 10.491 10.748 10.050 40.042 20.776 20.352 10.008 10.000 10.033 10.254 10.000 10.005 20.552 10.008 20.020 20.750 10.500 10.409 20.065 30.511 10.107 10.178 20.000 11.000 10.400 10.016 10.000 10.400 10.571 10.000 10.060 20.044 20.000 10.514 10.278 11.000 10.258 10.017 30.125 50.000 10.792 30.399 31.000 10.000 10.013 20.265 10.018 20.000 21.000 10.335 10.381 10.500 10.250 10.004 20.000 10.727 10.000 10.497 10.000 10.188 10.677 20.000 10.708 20.000 10.000 10.945 10.391 10.123 40.000 10.028 10.000 11.000 10.000 10.099 10.451 10.400 10.668 10.573 10.606 10.077 50.003 40.004 10.000 10.042 30.000 10.000 11.000 11.000 10.000 10.042 10.000 20.200 20.302 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
TD3D Scannet200permissive0.320 20.501 20.264 20.164 20.841 10.679 10.716 20.879 20.280 30.192 10.634 10.231 10.733 30.459 20.565 30.498 50.560 21.000 10.686 10.890 20.708 10.123 40.820 10.152 20.967 10.456 10.458 20.387 20.194 10.435 50.906 10.077 10.396 20.509 10.217 20.715 10.619 21.000 10.099 20.792 10.513 20.062 20.506 30.549 10.605 11.000 10.123 40.106 11.000 10.744 40.000 21.000 10.504 50.525 20.185 20.790 40.101 20.008 20.587 20.356 10.817 10.083 51.000 10.000 10.621 10.842 10.415 10.268 40.083 40.000 20.098 30.881 10.125 20.000 20.000 10.000 20.000 30.125 40.332 30.448 50.202 20.196 10.798 10.264 20.000 20.000 10.017 20.233 20.000 10.063 10.333 20.038 10.111 10.250 30.000 20.516 10.208 10.470 20.094 30.218 10.000 10.667 20.033 50.000 20.000 10.400 10.156 20.000 10.267 10.226 10.000 10.104 20.159 20.299 50.095 30.458 10.500 10.000 11.000 10.472 10.792 30.000 10.022 10.061 20.250 10.008 10.250 20.333 20.143 20.396 20.049 20.012 10.000 10.283 40.000 10.241 40.000 10.101 20.331 40.000 10.629 30.000 10.000 10.857 20.222 30.677 10.000 10.003 20.000 10.000 20.000 10.076 20.252 30.400 10.431 20.061 30.328 30.331 40.500 10.000 20.000 10.167 10.000 10.000 10.000 20.500 20.000 10.000 21.000 10.542 10.000 20.063 10.000 20.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
LGround Inst.permissive0.246 30.413 30.170 30.130 30.754 30.541 30.682 40.903 10.264 40.164 20.234 30.000 30.681 40.452 30.464 50.541 40.399 31.000 10.637 20.772 30.588 40.190 20.589 50.081 30.857 30.426 30.373 30.318 30.135 20.690 20.653 40.000 30.159 40.500 20.000 30.581 30.387 41.000 10.046 30.000 30.402 30.003 50.455 50.196 40.571 21.000 10.270 30.003 50.530 50.748 30.000 20.744 40.575 30.511 30.112 30.815 20.067 30.000 30.400 30.167 30.667 30.241 21.000 10.000 10.208 40.660 30.125 40.317 20.000 50.000 20.100 20.561 40.000 30.000 20.000 10.000 21.000 10.500 10.344 20.568 40.167 30.000 30.706 30.068 30.000 20.000 10.000 30.063 30.000 10.000 30.056 40.000 30.000 30.500 20.000 20.143 50.017 40.125 30.097 20.164 30.000 10.582 40.400 10.000 20.000 10.000 40.083 40.000 10.000 30.000 30.000 10.025 30.156 30.533 30.250 20.200 20.500 10.000 11.000 10.333 41.000 10.000 10.000 30.000 30.000 30.000 20.000 30.333 20.000 30.000 30.000 30.000 30.000 10.400 30.000 10.364 20.000 10.000 30.500 30.000 10.511 40.000 10.000 10.286 30.333 20.000 50.000 10.000 30.000 10.000 20.000 10.034 30.111 50.000 30.333 40.031 50.000 40.750 10.125 20.000 20.000 10.151 20.000 10.000 10.000 20.500 20.000 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.
Minkowski 34D Inst.permissive0.203 50.369 40.134 50.078 50.706 40.382 40.693 30.845 30.221 50.150 40.158 40.000 30.746 20.369 40.545 40.595 20.387 40.997 30.413 50.720 50.636 20.165 30.732 30.070 40.851 40.402 40.251 40.313 40.123 40.583 40.696 30.000 30.051 50.500 20.000 30.500 40.372 50.667 40.009 40.000 30.307 50.003 40.479 40.107 50.226 50.903 40.109 50.031 30.981 30.726 50.000 20.522 50.669 20.282 50.052 50.778 50.000 40.000 30.400 30.074 40.333 40.218 41.000 10.000 10.250 30.406 50.118 50.317 20.100 30.000 20.191 10.596 20.000 30.000 20.000 10.000 20.000 30.500 10.178 50.701 20.000 50.000 30.522 50.018 50.000 20.000 10.000 30.060 40.000 10.000 30.033 50.000 30.000 30.000 40.000 20.281 30.100 20.000 50.090 40.133 40.000 10.422 50.050 40.000 20.000 10.200 30.000 50.000 10.000 30.000 30.000 10.000 40.123 40.677 20.021 40.000 40.500 10.000 10.500 40.442 20.125 50.000 10.000 30.000 30.000 30.000 20.000 30.056 40.000 30.000 30.000 30.000 30.000 10.200 50.000 10.143 50.000 10.000 30.250 50.000 10.511 40.000 10.000 10.286 30.083 40.396 20.000 10.000 30.000 10.000 20.000 10.025 40.300 20.000 30.371 30.070 20.000 40.385 30.000 50.000 20.000 10.000 50.000 10.000 10.000 20.500 20.000 10.000 20.000 20.200 20.000 20.000 20.000 20.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.209 40.361 50.157 40.085 40.700 50.248 50.634 50.776 50.322 20.135 50.103 50.000 30.524 50.364 50.618 20.592 30.381 50.997 30.589 40.747 40.340 50.109 50.768 20.059 50.702 50.448 20.188 50.149 50.091 50.636 30.573 50.000 30.246 30.500 20.000 30.450 50.405 30.667 40.006 50.000 30.356 40.007 30.506 20.420 30.340 30.667 50.294 20.004 40.571 40.748 20.000 21.000 10.573 40.502 40.094 40.807 30.000 40.000 30.400 30.000 50.278 50.228 31.000 10.000 10.115 50.432 40.198 30.050 50.125 20.000 20.000 50.573 30.000 30.000 20.000 10.000 20.000 30.125 40.312 40.610 30.221 10.000 30.667 40.050 40.000 20.000 10.000 30.032 50.000 10.000 30.083 30.000 30.000 30.000 40.000 20.220 40.000 50.125 30.000 50.111 50.000 10.667 20.200 30.000 20.000 10.000 40.110 30.000 10.000 30.000 30.000 10.000 40.053 50.500 40.000 50.000 40.500 10.000 10.500 40.333 40.500 40.000 10.000 30.000 30.000 30.000 20.000 30.000 50.000 30.000 30.000 30.000 30.000 10.600 20.000 10.364 20.000 10.000 30.750 10.000 10.833 10.000 10.000 10.143 50.000 50.396 20.000 10.000 30.000 10.000 20.000 10.021 50.221 40.000 30.093 50.055 40.451 20.677 20.125 20.000 20.000 10.028 40.000 10.000 10.000 20.500 20.000 10.000 20.000 20.050 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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3-PPT-ALCcopyleft0.798 10.911 100.812 200.854 60.770 110.856 130.555 130.943 10.660 230.735 20.979 10.606 60.492 10.792 30.934 30.841 20.819 40.716 70.947 90.906 10.822 1
PTv3 ScanNet0.794 20.941 30.813 190.851 80.782 60.890 20.597 10.916 30.696 80.713 40.979 10.635 10.384 30.793 20.907 90.821 50.790 320.696 120.967 30.903 20.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV20.785 30.978 10.800 280.833 240.788 40.853 170.545 170.910 60.713 10.705 50.979 10.596 80.390 20.769 130.832 420.821 50.792 310.730 10.975 10.897 50.785 5
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 40.964 20.855 10.843 170.781 70.858 120.575 60.831 340.685 140.714 30.979 10.594 90.310 270.801 10.892 170.841 20.819 40.723 40.940 140.887 70.725 25
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 50.861 210.818 140.836 210.790 30.875 40.576 50.905 70.704 50.739 10.969 110.611 20.349 110.756 230.958 10.702 460.805 150.708 80.916 330.898 40.801 3
TTT-KD0.773 60.646 920.818 140.809 360.774 90.878 30.581 20.943 10.687 120.704 60.978 50.607 50.336 160.775 90.912 70.838 40.823 20.694 130.967 30.899 30.794 4
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 70.939 40.824 60.854 60.771 100.840 310.564 100.900 90.686 130.677 130.961 170.537 320.348 120.769 130.903 110.785 110.815 70.676 230.939 150.880 120.772 9
PPT-SpUNet-Joint0.766 80.932 50.794 340.829 260.751 230.854 150.540 210.903 80.630 350.672 160.963 150.565 220.357 90.788 40.900 130.737 260.802 160.685 180.950 70.887 70.780 6
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OctFormerpermissive0.766 80.925 70.808 240.849 100.786 50.846 270.566 90.876 160.690 100.674 150.960 180.576 180.226 680.753 250.904 100.777 130.815 70.722 50.923 280.877 140.776 8
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
OccuSeg+Semantic0.764 100.758 590.796 320.839 190.746 260.907 10.562 110.850 260.680 160.672 160.978 50.610 30.335 180.777 70.819 460.847 10.830 10.691 150.972 20.885 90.727 23
CU-Hybrid Net0.764 100.924 80.819 120.840 180.757 180.853 170.580 30.848 270.709 30.643 250.958 220.587 130.295 340.753 250.884 210.758 200.815 70.725 30.927 250.867 230.743 16
O-CNNpermissive0.762 120.924 80.823 70.844 160.770 110.852 190.577 40.847 290.711 20.640 290.958 220.592 100.217 740.762 180.888 180.758 200.813 110.726 20.932 230.868 220.744 15
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
DTC0.757 130.843 270.820 100.847 130.791 20.862 100.511 340.870 180.707 40.652 210.954 360.604 70.279 450.760 190.942 20.734 270.766 450.701 110.884 550.874 200.736 17
OA-CNN-L_ScanNet200.756 140.783 450.826 50.858 40.776 80.837 340.548 160.896 120.649 270.675 140.962 160.586 140.335 180.771 120.802 500.770 160.787 340.691 150.936 180.880 120.761 11
ConDaFormer0.755 150.927 60.822 80.836 210.801 10.849 220.516 310.864 230.651 260.680 120.958 220.584 160.282 420.759 210.855 320.728 290.802 160.678 200.880 600.873 210.756 13
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PNE0.755 150.786 430.835 40.834 230.758 160.849 220.570 80.836 330.648 280.668 180.978 50.581 170.367 70.683 360.856 300.804 70.801 200.678 200.961 50.889 60.716 30
P. Hermosilla: Point Neighborhood Embeddings.
DMF-Net0.752 170.906 130.793 360.802 420.689 410.825 470.556 120.867 190.681 150.602 450.960 180.555 280.365 80.779 60.859 270.747 230.795 280.717 60.917 320.856 310.764 10
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
PointTransformerV20.752 170.742 670.809 230.872 10.758 160.860 110.552 140.891 140.610 420.687 70.960 180.559 260.304 300.766 160.926 50.767 170.797 240.644 340.942 120.876 170.722 27
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
BPNetcopyleft0.749 190.909 110.818 140.811 340.752 210.839 330.485 480.842 300.673 180.644 240.957 260.528 380.305 290.773 100.859 270.788 90.818 60.693 140.916 330.856 310.723 26
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer0.749 190.793 410.790 370.807 380.750 250.856 130.524 270.881 150.588 540.642 280.977 90.591 110.274 480.781 50.929 40.804 70.796 250.642 350.947 90.885 90.715 31
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP0.748 210.623 950.804 260.859 30.745 270.824 490.501 380.912 50.690 100.685 90.956 270.567 210.320 240.768 150.918 60.720 340.802 160.676 230.921 300.881 110.779 7
StratifiedFormerpermissive0.747 220.901 140.803 270.845 150.757 180.846 270.512 330.825 370.696 80.645 230.956 270.576 180.262 590.744 300.861 260.742 240.770 430.705 90.899 450.860 280.734 18
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 230.771 530.819 120.848 120.702 380.865 90.397 860.899 100.699 60.664 190.948 560.588 120.330 200.746 290.851 360.764 180.796 250.704 100.935 190.866 240.728 21
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 230.870 190.838 20.858 40.729 320.850 210.501 380.874 170.587 550.658 200.956 270.564 230.299 320.765 170.900 130.716 370.812 120.631 400.939 150.858 290.709 32
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)
DiffSeg3D20.745 250.725 760.814 180.837 200.751 230.831 410.514 320.896 120.674 170.684 100.960 180.564 230.303 310.773 100.820 450.713 400.798 230.690 170.923 280.875 180.757 12
Retro-FPN0.744 260.842 280.800 280.767 560.740 280.836 360.541 190.914 40.672 190.626 330.958 220.552 290.272 500.777 70.886 200.696 470.801 200.674 260.941 130.858 290.717 28
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-Net0.743 270.620 960.799 310.849 100.730 310.822 510.493 450.897 110.664 200.681 110.955 300.562 250.378 40.760 190.903 110.738 250.801 200.673 270.907 370.877 140.745 14
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 280.860 220.765 500.819 290.769 130.848 240.533 230.829 350.663 210.631 320.955 300.586 140.274 480.753 250.896 150.729 280.760 510.666 290.921 300.855 330.733 19
LRPNet0.742 280.816 360.806 250.807 380.752 210.828 450.575 60.839 320.699 60.637 300.954 360.520 410.320 240.755 240.834 400.760 190.772 400.676 230.915 350.862 260.717 28
LargeKernel3D0.739 300.909 110.820 100.806 400.740 280.852 190.545 170.826 360.594 530.643 250.955 300.541 310.263 580.723 340.858 290.775 150.767 440.678 200.933 210.848 380.694 37
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 310.776 490.790 370.851 80.754 200.854 150.491 470.866 210.596 520.686 80.955 300.536 330.342 140.624 510.869 230.787 100.802 160.628 410.927 250.875 180.704 34
MinkowskiNetpermissive0.736 310.859 230.818 140.832 250.709 360.840 310.521 290.853 250.660 230.643 250.951 460.544 300.286 400.731 320.893 160.675 560.772 400.683 190.874 670.852 360.727 23
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 330.890 150.837 30.864 20.726 330.873 50.530 260.824 380.489 880.647 220.978 50.609 40.336 160.624 510.733 590.758 200.776 380.570 660.949 80.877 140.728 21
PointTransformer++0.725 340.727 750.811 220.819 290.765 140.841 300.502 370.814 430.621 380.623 350.955 300.556 270.284 410.620 530.866 240.781 120.757 550.648 320.932 230.862 260.709 32
SparseConvNet0.725 340.647 910.821 90.846 140.721 340.869 60.533 230.754 590.603 480.614 370.955 300.572 200.325 220.710 350.870 220.724 320.823 20.628 410.934 200.865 250.683 40
MatchingNet0.724 360.812 380.812 200.810 350.735 300.834 380.495 440.860 240.572 620.602 450.954 360.512 430.280 440.757 220.845 380.725 310.780 360.606 510.937 170.851 370.700 36
INS-Conv-semantic0.717 370.751 620.759 530.812 330.704 370.868 70.537 220.842 300.609 440.608 410.953 400.534 350.293 350.616 540.864 250.719 360.793 290.640 360.933 210.845 420.663 46
PointMetaBase0.714 380.835 290.785 390.821 270.684 430.846 270.531 250.865 220.614 390.596 490.953 400.500 460.246 640.674 370.888 180.692 480.764 470.624 430.849 820.844 430.675 42
contrastBoundarypermissive0.705 390.769 560.775 440.809 360.687 420.820 540.439 740.812 440.661 220.591 510.945 640.515 420.171 920.633 480.856 300.720 340.796 250.668 280.889 520.847 390.689 38
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 400.774 510.800 280.793 470.760 150.847 260.471 520.802 470.463 950.634 310.968 130.491 490.271 520.726 330.910 80.706 420.815 70.551 780.878 610.833 440.570 78
RFCR0.702 410.889 160.745 640.813 320.672 460.818 580.493 450.815 420.623 360.610 390.947 580.470 580.249 630.594 570.848 370.705 430.779 370.646 330.892 500.823 500.611 61
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 Click0.701 420.825 330.796 320.723 630.716 350.832 400.433 760.816 400.634 330.609 400.969 110.418 840.344 130.559 690.833 410.715 380.808 140.560 720.902 420.847 390.680 41
JSENetpermissive0.699 430.881 180.762 510.821 270.667 470.800 700.522 280.792 500.613 400.607 420.935 840.492 480.205 790.576 620.853 340.691 500.758 530.652 310.872 700.828 470.649 50
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-Click0.693 440.743 660.794 340.655 860.684 430.822 510.497 430.719 690.622 370.617 360.977 90.447 710.339 150.750 280.664 750.703 450.790 320.596 560.946 110.855 330.647 51
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 450.732 710.772 450.786 480.677 450.866 80.517 300.848 270.509 810.626 330.952 440.536 330.225 700.545 750.704 660.689 530.810 130.564 710.903 410.854 350.729 20
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 460.884 170.754 570.795 450.647 540.818 580.422 780.802 470.612 410.604 430.945 640.462 610.189 870.563 680.853 340.726 300.765 460.632 390.904 390.821 530.606 65
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 470.704 810.741 680.754 600.656 490.829 430.501 380.741 640.609 440.548 590.950 500.522 400.371 50.633 480.756 540.715 380.771 420.623 440.861 780.814 560.658 47
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 480.866 200.748 610.819 290.645 560.794 730.450 640.802 470.587 550.604 430.945 640.464 600.201 820.554 710.840 390.723 330.732 650.602 540.907 370.822 520.603 68
VACNN++0.684 490.728 740.757 560.776 530.690 390.804 680.464 570.816 400.577 610.587 520.945 640.508 450.276 470.671 380.710 640.663 610.750 590.589 610.881 580.832 460.653 49
DGNet0.684 490.712 800.784 400.782 520.658 480.835 370.499 420.823 390.641 300.597 480.950 500.487 510.281 430.575 630.619 790.647 690.764 470.620 460.871 730.846 410.688 39
KP-FCNN0.684 490.847 260.758 550.784 500.647 540.814 610.473 510.772 530.605 460.594 500.935 840.450 690.181 900.587 580.805 490.690 510.785 350.614 470.882 570.819 540.632 57
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
Superpoint Network0.683 520.851 250.728 720.800 440.653 510.806 660.468 540.804 450.572 620.602 450.946 610.453 680.239 670.519 800.822 430.689 530.762 500.595 580.895 480.827 480.630 58
PointContrast_LA_SEM0.683 520.757 600.784 400.786 480.639 580.824 490.408 810.775 520.604 470.541 610.934 880.532 360.269 540.552 720.777 520.645 720.793 290.640 360.913 360.824 490.671 43
VI-PointConv0.676 540.770 550.754 570.783 510.621 620.814 610.552 140.758 570.571 640.557 570.954 360.529 370.268 560.530 780.682 700.675 560.719 680.603 530.888 530.833 440.665 45
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 550.789 420.748 610.763 580.635 600.814 610.407 830.747 610.581 590.573 540.950 500.484 520.271 520.607 550.754 550.649 660.774 390.596 560.883 560.823 500.606 65
SALANet0.670 560.816 360.770 480.768 550.652 520.807 650.451 610.747 610.659 250.545 600.924 940.473 570.149 1020.571 650.811 480.635 750.746 600.623 440.892 500.794 690.570 78
O3DSeg0.668 570.822 340.771 470.496 1060.651 530.833 390.541 190.761 560.555 700.611 380.966 140.489 500.370 60.388 1000.580 820.776 140.751 570.570 660.956 60.817 550.646 52
PointASNLpermissive0.666 580.703 820.781 420.751 620.655 500.830 420.471 520.769 540.474 910.537 630.951 460.475 560.279 450.635 460.698 690.675 560.751 570.553 770.816 890.806 600.703 35
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 580.781 460.759 530.699 710.644 570.822 510.475 500.779 510.564 670.504 770.953 400.428 780.203 810.586 600.754 550.661 620.753 560.588 620.902 420.813 580.642 53
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 600.746 640.708 750.722 640.638 590.820 540.451 610.566 970.599 500.541 610.950 500.510 440.313 260.648 430.819 460.616 800.682 830.590 600.869 740.810 590.656 48
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 610.778 470.702 780.806 400.619 630.813 640.468 540.693 770.494 840.524 690.941 760.449 700.298 330.510 820.821 440.675 560.727 670.568 690.826 870.803 630.637 55
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN0.658 610.558 1030.751 590.655 860.690 390.722 950.453 600.867 190.579 600.576 530.893 1060.523 390.293 350.733 310.571 840.692 480.659 900.606 510.875 640.804 620.668 44
HPGCNN0.656 630.698 840.743 660.650 880.564 800.820 540.505 360.758 570.631 340.479 810.945 640.480 540.226 680.572 640.774 530.690 510.735 630.614 470.853 810.776 840.597 71
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 640.752 610.734 700.664 840.583 750.815 600.399 850.754 590.639 310.535 650.942 740.470 580.309 280.665 390.539 860.650 650.708 730.635 380.857 800.793 710.642 53
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 650.778 470.731 710.699 710.577 760.829 430.446 660.736 650.477 900.523 710.945 640.454 650.269 540.484 900.749 580.618 780.738 610.599 550.827 860.792 740.621 60
PointConv-SFPN0.641 660.776 490.703 770.721 650.557 830.826 460.451 610.672 820.563 680.483 800.943 730.425 810.162 970.644 440.726 600.659 630.709 720.572 650.875 640.786 790.559 84
MVPNetpermissive0.641 660.831 300.715 730.671 810.590 710.781 790.394 870.679 790.642 290.553 580.937 810.462 610.256 600.649 420.406 1000.626 760.691 800.666 290.877 620.792 740.608 64
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 680.717 790.701 790.692 740.576 770.801 690.467 560.716 700.563 680.459 870.953 400.429 770.169 940.581 610.854 330.605 810.710 700.550 790.894 490.793 710.575 76
FPConvpermissive0.639 690.785 440.760 520.713 690.603 660.798 710.392 880.534 1020.603 480.524 690.948 560.457 630.250 620.538 760.723 620.598 850.696 780.614 470.872 700.799 640.567 81
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 700.797 400.769 490.641 940.590 710.820 540.461 580.537 1010.637 320.536 640.947 580.388 910.206 780.656 400.668 730.647 690.732 650.585 630.868 750.793 710.473 104
PointSPNet0.637 710.734 700.692 860.714 680.576 770.797 720.446 660.743 630.598 510.437 920.942 740.403 870.150 1010.626 500.800 510.649 660.697 770.557 750.846 830.777 830.563 82
SConv0.636 720.830 310.697 820.752 610.572 790.780 810.445 680.716 700.529 740.530 660.951 460.446 720.170 930.507 850.666 740.636 740.682 830.541 850.886 540.799 640.594 72
Supervoxel-CNN0.635 730.656 890.711 740.719 660.613 640.757 900.444 710.765 550.534 730.566 550.928 920.478 550.272 500.636 450.531 880.664 600.645 940.508 920.864 770.792 740.611 61
joint point-basedpermissive0.634 740.614 970.778 430.667 830.633 610.825 470.420 790.804 450.467 930.561 560.951 460.494 470.291 370.566 660.458 950.579 910.764 470.559 740.838 840.814 560.598 70
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 750.731 720.688 890.675 780.591 700.784 780.444 710.565 980.610 420.492 780.949 540.456 640.254 610.587 580.706 650.599 840.665 890.612 500.868 750.791 770.579 75
3DSM_DMMF0.631 760.626 940.745 640.801 430.607 650.751 910.506 350.729 680.565 660.491 790.866 1090.434 730.197 850.595 560.630 780.709 410.705 750.560 720.875 640.740 940.491 99
PointNet2-SFPN0.631 760.771 530.692 860.672 790.524 880.837 340.440 730.706 750.538 720.446 890.944 700.421 830.219 730.552 720.751 570.591 870.737 620.543 840.901 440.768 860.557 85
APCF-Net0.631 760.742 670.687 910.672 790.557 830.792 760.408 810.665 830.545 710.508 740.952 440.428 780.186 880.634 470.702 670.620 770.706 740.555 760.873 680.798 660.581 74
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 790.604 990.741 680.766 570.590 710.747 920.501 380.734 660.503 830.527 670.919 980.454 650.323 230.550 740.420 990.678 550.688 810.544 820.896 470.795 680.627 59
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 800.800 390.625 1020.719 660.545 850.806 660.445 680.597 910.448 980.519 720.938 800.481 530.328 210.489 890.499 930.657 640.759 520.592 590.881 580.797 670.634 56
SegGroup_sempermissive0.627 810.818 350.747 630.701 700.602 670.764 870.385 920.629 880.490 860.508 740.931 910.409 860.201 820.564 670.725 610.618 780.692 790.539 860.873 680.794 690.548 88
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 820.830 310.694 840.757 590.563 810.772 850.448 650.647 860.520 770.509 730.949 540.431 760.191 860.496 870.614 800.647 690.672 870.535 880.876 630.783 800.571 77
dtc_net0.625 820.703 820.751 590.794 460.535 860.848 240.480 490.676 810.528 750.469 840.944 700.454 650.004 1150.464 920.636 770.704 440.758 530.548 810.924 270.787 780.492 98
HPEIN0.618 840.729 730.668 920.647 900.597 690.766 860.414 800.680 780.520 770.525 680.946 610.432 740.215 750.493 880.599 810.638 730.617 990.570 660.897 460.806 600.605 67
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-GCNpermissive0.610 850.858 240.772 450.489 1070.532 870.792 760.404 840.643 870.570 650.507 760.935 840.414 850.046 1120.510 820.702 670.602 830.705 750.549 800.859 790.773 850.534 91
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 860.760 580.667 930.649 890.521 890.793 740.457 590.648 850.528 750.434 940.947 580.401 880.153 1000.454 930.721 630.648 680.717 690.536 870.904 390.765 870.485 100
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 870.634 930.743 660.697 730.601 680.781 790.437 750.585 940.493 850.446 890.933 890.394 890.011 1140.654 410.661 760.603 820.733 640.526 890.832 850.761 890.480 101
LAP-D0.594 880.720 770.692 860.637 950.456 990.773 840.391 900.730 670.587 550.445 910.940 780.381 920.288 380.434 960.453 970.591 870.649 920.581 640.777 930.749 930.610 63
DPC0.592 890.720 770.700 800.602 990.480 950.762 890.380 930.713 730.585 580.437 920.940 780.369 940.288 380.434 960.509 920.590 890.639 970.567 700.772 950.755 910.592 73
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 900.766 570.659 970.683 760.470 980.740 940.387 910.620 900.490 860.476 820.922 960.355 970.245 650.511 810.511 910.571 920.643 950.493 960.872 700.762 880.600 69
ROSMRF0.580 910.772 520.707 760.681 770.563 810.764 870.362 950.515 1030.465 940.465 860.936 830.427 800.207 770.438 940.577 830.536 950.675 860.486 970.723 1010.779 810.524 94
SD-DETR0.576 920.746 640.609 1060.445 1110.517 900.643 1060.366 940.714 720.456 960.468 850.870 1080.432 740.264 570.558 700.674 710.586 900.688 810.482 980.739 990.733 960.537 90
SQN_0.1%0.569 930.676 860.696 830.657 850.497 910.779 820.424 770.548 990.515 790.376 990.902 1050.422 820.357 90.379 1010.456 960.596 860.659 900.544 820.685 1040.665 1070.556 86
TextureNetpermissive0.566 940.672 880.664 940.671 810.494 930.719 960.445 680.678 800.411 1040.396 970.935 840.356 960.225 700.412 980.535 870.565 930.636 980.464 1000.794 920.680 1040.568 80
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
DVVNet0.562 950.648 900.700 800.770 540.586 740.687 1000.333 990.650 840.514 800.475 830.906 1020.359 950.223 720.340 1030.442 980.422 1060.668 880.501 930.708 1020.779 810.534 91
Pointnet++ & Featurepermissive0.557 960.735 690.661 960.686 750.491 940.744 930.392 880.539 1000.451 970.375 1000.946 610.376 930.205 790.403 990.356 1030.553 940.643 950.497 940.824 880.756 900.515 95
GMLPs0.538 970.495 1080.693 850.647 900.471 970.793 740.300 1020.477 1040.505 820.358 1020.903 1040.327 1000.081 1090.472 910.529 890.448 1040.710 700.509 900.746 970.737 950.554 87
PanopticFusion-label0.529 980.491 1090.688 890.604 980.386 1040.632 1070.225 1120.705 760.434 1010.293 1080.815 1100.348 980.241 660.499 860.669 720.507 970.649 920.442 1060.796 910.602 1110.561 83
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_weak0.516 990.676 860.591 1090.609 960.442 1000.774 830.335 980.597 910.422 1030.357 1030.932 900.341 990.094 1080.298 1050.528 900.473 1020.676 850.495 950.602 1100.721 990.349 111
Online SegFusion0.515 1000.607 980.644 1000.579 1010.434 1010.630 1080.353 960.628 890.440 990.410 950.762 1140.307 1020.167 950.520 790.403 1010.516 960.565 1020.447 1040.678 1050.701 1010.514 96
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 1010.558 1030.608 1070.424 1130.478 960.690 990.246 1080.586 930.468 920.450 880.911 1000.394 890.160 980.438 940.212 1100.432 1050.541 1080.475 990.742 980.727 970.477 102
PCNN0.498 1020.559 1020.644 1000.560 1030.420 1030.711 980.229 1100.414 1050.436 1000.352 1040.941 760.324 1010.155 990.238 1100.387 1020.493 980.529 1090.509 900.813 900.751 920.504 97
Weakly-Openseg v30.489 1030.749 630.664 940.646 920.496 920.559 1120.122 1150.577 950.257 1150.364 1010.805 1110.198 1130.096 1070.510 820.496 940.361 1100.563 1030.359 1130.777 930.644 1080.532 93
3DMV0.484 1040.484 1100.538 1110.643 930.424 1020.606 1110.310 1000.574 960.433 1020.378 980.796 1120.301 1030.214 760.537 770.208 1110.472 1030.507 1120.413 1090.693 1030.602 1110.539 89
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1050.577 1010.611 1050.356 1150.321 1120.715 970.299 1040.376 1090.328 1110.319 1060.944 700.285 1050.164 960.216 1130.229 1080.484 1000.545 1070.456 1020.755 960.709 1000.475 103
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1060.679 850.604 1080.578 1020.380 1050.682 1010.291 1050.106 1150.483 890.258 1130.920 970.258 1090.025 1130.231 1120.325 1040.480 1010.560 1050.463 1010.725 1000.666 1060.231 115
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1070.474 1110.623 1030.463 1090.366 1070.651 1040.310 1000.389 1080.349 1090.330 1050.937 810.271 1070.126 1040.285 1060.224 1090.350 1120.577 1010.445 1050.625 1080.723 980.394 107
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 1080.548 1050.548 1100.597 1000.363 1080.628 1090.300 1020.292 1100.374 1060.307 1070.881 1070.268 1080.186 880.238 1100.204 1120.407 1070.506 1130.449 1030.667 1060.620 1100.462 105
SurfaceConvPF0.442 1080.505 1070.622 1040.380 1140.342 1100.654 1030.227 1110.397 1070.367 1070.276 1100.924 940.240 1100.198 840.359 1020.262 1060.366 1080.581 1000.435 1070.640 1070.668 1050.398 106
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1100.437 1130.646 990.474 1080.369 1060.645 1050.353 960.258 1120.282 1130.279 1090.918 990.298 1040.147 1030.283 1070.294 1050.487 990.562 1040.427 1080.619 1090.633 1090.352 110
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1110.525 1060.647 980.522 1040.324 1110.488 1150.077 1160.712 740.353 1080.401 960.636 1160.281 1060.176 910.340 1030.565 850.175 1160.551 1060.398 1100.370 1160.602 1110.361 109
SPLAT Netcopyleft0.393 1120.472 1120.511 1120.606 970.311 1130.656 1020.245 1090.405 1060.328 1110.197 1140.927 930.227 1120.000 1170.001 1170.249 1070.271 1150.510 1100.383 1120.593 1110.699 1020.267 113
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 1130.297 1150.491 1130.432 1120.358 1090.612 1100.274 1060.116 1140.411 1040.265 1110.904 1030.229 1110.079 1100.250 1080.185 1130.320 1130.510 1100.385 1110.548 1120.597 1140.394 107
PointNet++permissive0.339 1140.584 1000.478 1140.458 1100.256 1150.360 1160.250 1070.247 1130.278 1140.261 1120.677 1150.183 1140.117 1050.212 1140.145 1150.364 1090.346 1160.232 1160.548 1120.523 1150.252 114
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 1150.353 1140.290 1160.278 1160.166 1160.553 1130.169 1140.286 1110.147 1160.148 1160.908 1010.182 1150.064 1110.023 1160.018 1170.354 1110.363 1140.345 1140.546 1140.685 1030.278 112
ScanNetpermissive0.306 1160.203 1160.366 1150.501 1050.311 1130.524 1140.211 1130.002 1170.342 1100.189 1150.786 1130.145 1160.102 1060.245 1090.152 1140.318 1140.348 1150.300 1150.460 1150.437 1160.182 116
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 1170.000 1170.041 1170.172 1170.030 1170.062 1170.001 1170.035 1160.004 1170.051 1170.143 1170.019 1170.003 1160.041 1150.050 1160.003 1170.054 1170.018 1170.005 1170.264 1170.082 117


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PointRel0.816 11.000 10.971 50.908 60.743 20.923 50.573 60.714 210.695 150.734 80.747 20.725 90.809 11.000 10.814 60.899 30.820 31.000 10.610 15
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
Spherical Mask(CtoF)0.812 21.000 10.973 40.852 130.718 50.917 70.574 40.677 270.748 90.729 110.715 60.795 20.809 11.000 10.831 20.854 90.787 91.000 10.638 4
EV3D0.811 31.000 10.968 60.852 130.717 60.921 60.574 50.677 270.748 90.730 100.703 100.795 20.809 11.000 10.831 20.854 90.778 131.000 10.638 5
SIM3D0.803 41.000 10.967 70.863 120.692 150.924 40.552 90.732 200.667 190.732 90.662 130.796 10.789 91.000 10.803 70.864 60.766 181.000 10.643 3
OneFormer3Dcopyleft0.801 51.000 10.973 30.909 50.698 120.928 20.582 30.668 320.685 160.780 20.687 110.698 160.702 141.000 10.794 90.900 20.784 110.986 490.635 6
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception0.800 61.000 10.930 90.872 100.727 40.862 210.454 160.764 130.820 10.746 60.706 80.750 50.772 100.926 420.764 150.818 250.826 10.997 370.660 2
InsSSM0.799 71.000 10.915 110.710 380.729 30.925 30.664 10.670 300.770 60.766 30.739 30.737 60.700 151.000 10.792 100.829 190.815 40.997 370.625 8
TST3D0.795 81.000 10.929 100.918 40.709 90.884 160.596 20.704 240.769 70.734 70.644 180.699 150.751 121.000 10.794 80.876 50.757 200.997 370.550 29
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
MG-Former0.791 91.000 10.980 20.837 170.626 230.897 90.543 100.759 150.800 50.766 40.659 140.769 40.697 181.000 10.791 110.707 450.791 81.000 10.610 14
ExtMask3D0.789 101.000 10.988 10.756 310.706 100.912 80.429 170.647 370.806 40.755 50.673 120.689 170.772 111.000 10.789 120.852 110.811 51.000 10.617 11
Queryformer0.787 111.000 10.933 80.601 470.754 10.886 140.558 80.661 340.767 80.665 160.716 50.639 220.808 51.000 10.844 10.897 40.804 61.000 10.624 9
MAFT0.786 121.000 10.894 160.807 210.694 140.893 120.486 120.674 290.740 110.786 10.704 90.727 80.739 131.000 10.707 210.849 130.756 211.000 10.685 1
Mask3D0.780 131.000 10.786 400.716 360.696 130.885 150.500 110.714 210.810 30.672 150.715 60.679 180.809 11.000 10.831 20.833 170.787 91.000 10.602 17
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 140.903 530.903 130.806 220.609 290.886 130.568 70.815 60.705 140.711 120.655 150.652 210.685 211.000 10.789 130.809 260.776 151.000 10.583 22
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 151.000 10.803 330.937 10.684 160.865 180.213 320.870 20.664 200.571 220.758 10.702 130.807 61.000 10.653 280.902 10.792 71.000 10.626 7
SoftGrouppermissive0.761 161.000 10.808 290.845 150.716 70.862 200.243 290.824 40.655 220.620 170.734 40.699 140.791 80.981 360.716 190.844 140.769 161.000 10.594 20
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNetpermissive0.757 171.000 10.904 120.731 340.678 170.895 100.458 140.644 390.670 180.710 130.620 230.732 70.650 231.000 10.756 160.778 290.779 121.000 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
TD3Dpermissive0.751 181.000 10.774 410.867 110.621 250.934 10.404 180.706 230.812 20.605 200.633 210.626 230.690 201.000 10.640 300.820 220.777 141.000 10.612 13
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNetpermissive0.747 191.000 10.818 250.837 180.713 80.844 230.457 150.647 370.711 130.614 180.617 250.657 200.650 231.000 10.692 220.822 210.765 191.000 10.595 19
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut0.732 201.000 10.788 380.724 350.642 220.859 220.248 280.787 110.618 250.596 210.653 170.722 110.583 441.000 10.766 140.861 70.825 21.000 10.504 35
IPCA-Inst0.731 211.000 10.788 390.884 90.698 110.788 390.252 270.760 140.646 230.511 300.637 200.665 190.804 71.000 10.644 290.778 300.747 231.000 10.561 26
TopoSeg0.725 221.000 10.806 320.933 20.668 190.758 430.272 260.734 190.630 240.549 260.654 160.606 240.697 190.966 390.612 340.839 150.754 221.000 10.573 23
DKNet0.718 231.000 10.814 260.782 250.619 260.872 170.224 300.751 170.569 290.677 140.585 290.724 100.633 340.981 360.515 440.819 230.736 241.000 10.617 10
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 241.000 10.850 180.924 30.648 200.747 460.162 340.862 30.572 280.520 280.624 220.549 270.649 321.000 10.560 390.706 460.768 171.000 10.591 21
HAISpermissive0.699 251.000 10.849 190.820 190.675 180.808 330.279 240.757 160.465 350.517 290.596 270.559 260.600 381.000 10.654 270.767 320.676 280.994 450.560 27
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 261.000 10.697 570.888 80.556 360.803 340.387 190.626 410.417 400.556 250.585 300.702 120.600 381.000 10.824 50.720 440.692 261.000 10.509 34
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 271.000 10.799 350.811 200.622 240.817 280.376 200.805 90.590 270.487 340.568 330.525 310.650 230.835 520.600 350.829 180.655 311.000 10.526 31
SphereSeg0.680 281.000 10.856 170.744 320.618 270.893 110.151 350.651 360.713 120.537 270.579 320.430 410.651 221.000 10.389 550.744 390.697 250.991 470.601 18
DANCENET0.680 281.000 10.807 300.733 330.600 300.768 420.375 210.543 490.538 300.610 190.599 260.498 320.632 360.981 360.739 180.856 80.633 370.882 600.454 44
Box2Mask0.677 301.000 10.847 200.771 270.509 450.816 290.277 250.558 480.482 320.562 240.640 190.448 370.700 151.000 10.666 230.852 120.578 440.997 370.488 39
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 311.000 10.758 490.682 400.576 340.842 240.477 130.504 550.524 310.567 230.585 310.451 360.557 461.000 10.751 170.797 270.563 471.000 10.467 43
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 321.000 10.822 240.764 300.616 280.815 300.139 390.694 260.597 260.459 380.566 340.599 250.600 380.516 620.715 200.819 240.635 351.000 10.603 16
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 331.000 10.760 470.667 420.581 320.863 190.323 220.655 350.477 330.473 360.549 360.432 400.650 231.000 10.655 260.738 400.585 430.944 520.472 42
CSC-Pretrained0.648 341.000 10.810 270.768 280.523 430.813 310.143 380.819 50.389 430.422 470.511 400.443 380.650 231.000 10.624 320.732 410.634 361.000 10.375 51
PE0.645 351.000 10.773 430.798 240.538 380.786 400.088 470.799 100.350 470.435 450.547 370.545 280.646 330.933 410.562 380.761 350.556 520.997 370.501 37
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 361.000 10.758 480.582 530.539 370.826 270.046 520.765 120.372 450.436 440.588 280.539 300.650 231.000 10.577 360.750 370.653 330.997 370.495 38
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 371.000 10.841 210.893 70.531 400.802 350.115 440.588 460.448 370.438 420.537 390.430 420.550 470.857 440.534 420.764 340.657 300.987 480.568 24
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 381.000 10.895 150.800 230.480 490.676 510.144 370.737 180.354 460.447 390.400 530.365 480.700 151.000 10.569 370.836 160.599 391.000 10.473 41
PointGroup0.636 391.000 10.765 440.624 440.505 470.797 360.116 430.696 250.384 440.441 400.559 350.476 340.596 411.000 10.666 230.756 360.556 510.997 370.513 33
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+Group0.635 400.667 550.797 370.714 370.562 350.774 410.146 360.810 80.429 390.476 350.546 380.399 440.633 341.000 10.632 310.722 430.609 381.000 10.514 32
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_evaluation0.631 411.000 10.829 230.606 460.646 210.836 250.068 480.511 530.462 360.507 310.619 240.389 460.610 371.000 10.432 500.828 200.673 290.788 640.552 28
DENet0.629 421.000 10.797 360.608 450.589 310.627 550.219 310.882 10.310 490.402 520.383 550.396 450.650 231.000 10.663 250.543 630.691 271.000 10.568 25
3D-MPA0.611 431.000 10.833 220.765 290.526 420.756 440.136 410.588 460.470 340.438 430.432 490.358 500.650 230.857 440.429 510.765 330.557 501.000 10.430 46
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 441.000 10.801 340.599 480.535 390.728 480.286 230.436 590.679 170.491 320.433 470.256 520.404 590.857 440.620 330.724 420.510 571.000 10.539 30
AOIA0.601 451.000 10.761 460.687 390.485 480.828 260.008 590.663 330.405 420.405 510.425 500.490 330.596 410.714 550.553 410.779 280.597 400.992 460.424 48
PCJC0.578 461.000 10.810 280.583 520.449 520.813 320.042 530.603 440.341 480.490 330.465 440.410 430.650 230.835 520.264 610.694 500.561 480.889 570.504 36
SSEN0.575 471.000 10.761 450.473 550.477 500.795 370.066 490.529 510.658 210.460 370.461 450.380 470.331 610.859 430.401 540.692 520.653 321.000 10.348 53
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 480.528 650.708 560.626 430.580 330.745 470.063 500.627 400.240 530.400 530.497 410.464 350.515 481.000 10.475 46