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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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
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.
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
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
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
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
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


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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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.


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3 ScanNet0.794 10.941 30.813 170.851 70.782 50.890 20.597 10.916 20.696 70.713 30.979 10.635 10.384 20.793 20.907 70.821 40.790 300.696 100.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
PonderV20.785 20.978 10.800 250.833 210.788 30.853 150.545 160.910 50.713 10.705 40.979 10.596 60.390 10.769 110.832 400.821 40.792 290.730 10.975 10.897 40.785 4
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3Dpermissive0.781 30.964 20.855 10.843 150.781 60.858 110.575 60.831 310.685 130.714 20.979 10.594 70.310 260.801 10.892 150.841 20.819 40.723 40.940 130.887 60.725 22
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 130.836 180.790 20.875 40.576 50.905 60.704 40.739 10.969 100.611 20.349 100.756 200.958 10.702 430.805 140.708 70.916 310.898 30.801 2
TTT-KD0.773 50.646 890.818 130.809 330.774 80.878 30.581 20.943 10.687 110.704 50.978 40.607 50.336 150.775 80.912 50.838 30.823 20.694 110.967 30.899 20.794 3
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS0.772 60.939 40.824 60.854 60.771 90.840 290.564 100.900 80.686 120.677 110.961 160.537 290.348 110.769 110.903 90.785 100.815 60.676 200.939 140.880 110.772 8
OctFormerpermissive0.766 70.925 70.808 210.849 90.786 40.846 250.566 90.876 140.690 90.674 130.960 170.576 160.226 650.753 220.904 80.777 120.815 60.722 50.923 270.877 130.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint0.766 70.932 50.794 310.829 230.751 210.854 130.540 200.903 70.630 320.672 140.963 140.565 200.357 80.788 30.900 110.737 250.802 150.685 150.950 70.887 60.780 5
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OccuSeg+Semantic0.764 90.758 570.796 290.839 170.746 230.907 10.562 110.850 230.680 150.672 140.978 40.610 30.335 170.777 60.819 430.847 10.830 10.691 130.972 20.885 80.727 20
CU-Hybrid Net0.764 90.924 80.819 110.840 160.757 160.853 150.580 30.848 240.709 30.643 220.958 200.587 110.295 320.753 220.884 190.758 190.815 60.725 30.927 240.867 200.743 14
O-CNNpermissive0.762 110.924 80.823 70.844 140.770 100.852 170.577 40.847 260.711 20.640 260.958 200.592 80.217 710.762 160.888 160.758 190.813 100.726 20.932 220.868 190.744 13
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
OA-CNN-L_ScanNet200.756 120.783 430.826 50.858 40.776 70.837 320.548 150.896 110.649 240.675 120.962 150.586 120.335 170.771 100.802 470.770 150.787 320.691 130.936 170.880 110.761 10
ConDaFormer0.755 130.927 60.822 80.836 180.801 10.849 200.516 300.864 200.651 230.680 100.958 200.584 140.282 400.759 180.855 300.728 270.802 150.678 170.880 570.873 180.756 11
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
PNE0.755 130.786 410.835 40.834 200.758 140.849 200.570 80.836 300.648 250.668 160.978 40.581 150.367 60.683 330.856 280.804 60.801 190.678 170.961 50.889 50.716 27
P. Hermosilla: Point Neighborhood Embeddings.
PointTransformerV20.752 150.742 650.809 200.872 10.758 140.860 100.552 130.891 120.610 390.687 60.960 170.559 230.304 290.766 140.926 30.767 160.797 220.644 310.942 110.876 160.722 24
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 150.906 120.793 330.802 390.689 380.825 440.556 120.867 160.681 140.602 420.960 170.555 250.365 70.779 50.859 250.747 220.795 260.717 60.917 300.856 280.764 9
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointConvFormer0.749 170.793 390.790 340.807 350.750 220.856 120.524 260.881 130.588 510.642 250.977 80.591 90.274 450.781 40.929 20.804 60.796 230.642 320.947 90.885 80.715 28
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 170.909 100.818 130.811 310.752 190.839 310.485 450.842 270.673 160.644 210.957 240.528 350.305 280.773 90.859 250.788 80.818 50.693 120.916 310.856 280.723 23
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 190.623 920.804 230.859 30.745 240.824 460.501 350.912 40.690 90.685 80.956 250.567 190.320 230.768 130.918 40.720 320.802 150.676 200.921 280.881 100.779 6
StratifiedFormerpermissive0.747 200.901 130.803 240.845 130.757 160.846 250.512 310.825 340.696 70.645 200.956 250.576 160.262 560.744 270.861 240.742 230.770 410.705 80.899 430.860 250.734 15
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 210.771 510.819 110.848 110.702 350.865 90.397 830.899 90.699 50.664 170.948 530.588 100.330 190.746 260.851 340.764 170.796 230.704 90.935 180.866 210.728 18
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 210.870 180.838 20.858 40.729 290.850 190.501 350.874 150.587 520.658 180.956 250.564 210.299 300.765 150.900 110.716 350.812 110.631 370.939 140.858 260.709 29
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Retro-FPN0.744 230.842 260.800 250.767 530.740 250.836 340.541 180.914 30.672 170.626 300.958 200.552 260.272 470.777 60.886 180.696 440.801 190.674 230.941 120.858 260.717 25
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net0.743 240.620 930.799 280.849 90.730 280.822 480.493 420.897 100.664 180.681 90.955 280.562 220.378 30.760 170.903 90.738 240.801 190.673 240.907 350.877 130.745 12
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 250.816 340.806 220.807 350.752 190.828 420.575 60.839 290.699 50.637 270.954 340.520 380.320 230.755 210.834 380.760 180.772 380.676 200.915 330.862 230.717 25
SAT0.742 250.860 210.765 470.819 260.769 110.848 220.533 220.829 320.663 190.631 290.955 280.586 120.274 450.753 220.896 130.729 260.760 480.666 260.921 280.855 300.733 16
LargeKernel3D0.739 270.909 100.820 100.806 370.740 250.852 170.545 160.826 330.594 500.643 220.955 280.541 280.263 550.723 310.858 270.775 140.767 420.678 170.933 200.848 350.694 34
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 280.776 470.790 340.851 70.754 180.854 130.491 440.866 180.596 490.686 70.955 280.536 300.342 130.624 480.869 210.787 90.802 150.628 380.927 240.875 170.704 31
MinkowskiNetpermissive0.736 280.859 220.818 130.832 220.709 330.840 290.521 280.853 220.660 210.643 220.951 430.544 270.286 380.731 290.893 140.675 530.772 380.683 160.874 640.852 330.727 20
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 300.890 140.837 30.864 20.726 300.873 50.530 250.824 350.489 850.647 190.978 40.609 40.336 150.624 480.733 560.758 190.776 360.570 630.949 80.877 130.728 18
SparseConvNet0.725 310.647 880.821 90.846 120.721 310.869 60.533 220.754 560.603 450.614 340.955 280.572 180.325 210.710 320.870 200.724 300.823 20.628 380.934 190.865 220.683 37
PointTransformer++0.725 310.727 730.811 190.819 260.765 120.841 280.502 340.814 400.621 350.623 320.955 280.556 240.284 390.620 500.866 220.781 110.757 520.648 290.932 220.862 230.709 29
MatchingNet0.724 330.812 360.812 180.810 320.735 270.834 360.495 410.860 210.572 590.602 420.954 340.512 400.280 420.757 190.845 360.725 290.780 340.606 480.937 160.851 340.700 33
INS-Conv-semantic0.717 340.751 600.759 500.812 300.704 340.868 70.537 210.842 270.609 410.608 380.953 370.534 320.293 330.616 510.864 230.719 340.793 270.640 330.933 200.845 390.663 43
PointMetaBase0.714 350.835 270.785 360.821 240.684 400.846 250.531 240.865 190.614 360.596 460.953 370.500 430.246 610.674 340.888 160.692 450.764 440.624 400.849 790.844 400.675 39
contrastBoundarypermissive0.705 360.769 540.775 410.809 330.687 390.820 510.439 710.812 410.661 200.591 480.945 610.515 390.171 890.633 450.856 280.720 320.796 230.668 250.889 500.847 360.689 35
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 370.774 490.800 250.793 440.760 130.847 240.471 490.802 440.463 920.634 280.968 120.491 460.271 490.726 300.910 60.706 390.815 60.551 750.878 580.833 410.570 75
RFCR0.702 380.889 150.745 610.813 290.672 430.818 550.493 420.815 390.623 330.610 360.947 550.470 550.249 600.594 540.848 350.705 400.779 350.646 300.892 480.823 470.611 58
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click0.701 390.825 310.796 290.723 600.716 320.832 380.433 730.816 370.634 300.609 370.969 100.418 810.344 120.559 660.833 390.715 360.808 130.560 690.902 400.847 360.680 38
JSENetpermissive0.699 400.881 170.762 480.821 240.667 440.800 670.522 270.792 470.613 370.607 390.935 810.492 450.205 760.576 590.853 320.691 470.758 500.652 280.872 670.828 440.649 47
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click0.693 410.743 640.794 310.655 830.684 400.822 480.497 400.719 660.622 340.617 330.977 80.447 680.339 140.750 250.664 720.703 420.790 300.596 530.946 100.855 300.647 48
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 420.732 690.772 420.786 450.677 420.866 80.517 290.848 240.509 780.626 300.952 410.536 300.225 670.545 720.704 630.689 500.810 120.564 680.903 390.854 320.729 17
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 430.884 160.754 540.795 420.647 510.818 550.422 750.802 440.612 380.604 400.945 610.462 580.189 840.563 650.853 320.726 280.765 430.632 360.904 370.821 500.606 62
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 440.704 780.741 650.754 570.656 460.829 400.501 350.741 610.609 410.548 560.950 470.522 370.371 40.633 450.756 510.715 360.771 400.623 410.861 750.814 530.658 44
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 450.866 190.748 580.819 260.645 530.794 700.450 610.802 440.587 520.604 400.945 610.464 570.201 790.554 680.840 370.723 310.732 620.602 510.907 350.822 490.603 65
KP-FCNN0.684 460.847 250.758 520.784 470.647 510.814 580.473 480.772 500.605 430.594 470.935 810.450 660.181 870.587 550.805 460.690 480.785 330.614 440.882 540.819 510.632 54
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 460.728 720.757 530.776 500.690 360.804 650.464 540.816 370.577 580.587 490.945 610.508 420.276 440.671 350.710 610.663 580.750 560.589 580.881 550.832 430.653 46
DGNet0.684 460.712 770.784 370.782 490.658 450.835 350.499 390.823 360.641 270.597 450.950 470.487 480.281 410.575 600.619 760.647 660.764 440.620 430.871 700.846 380.688 36
Superpoint Network0.683 490.851 240.728 690.800 410.653 480.806 630.468 510.804 420.572 590.602 420.946 580.453 650.239 640.519 770.822 410.689 500.762 470.595 550.895 460.827 450.630 55
PointContrast_LA_SEM0.683 490.757 580.784 370.786 450.639 550.824 460.408 780.775 490.604 440.541 580.934 850.532 330.269 510.552 690.777 490.645 690.793 270.640 330.913 340.824 460.671 40
VI-PointConv0.676 510.770 530.754 540.783 480.621 590.814 580.552 130.758 540.571 610.557 540.954 340.529 340.268 530.530 750.682 670.675 530.719 650.603 500.888 510.833 410.665 42
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 520.789 400.748 580.763 550.635 570.814 580.407 800.747 580.581 560.573 510.950 470.484 490.271 490.607 520.754 520.649 630.774 370.596 530.883 530.823 470.606 62
SALANet0.670 530.816 340.770 450.768 520.652 490.807 620.451 580.747 580.659 220.545 570.924 910.473 540.149 990.571 620.811 450.635 720.746 570.623 410.892 480.794 660.570 75
O3DSeg0.668 540.822 320.771 440.496 1030.651 500.833 370.541 180.761 530.555 670.611 350.966 130.489 470.370 50.388 970.580 790.776 130.751 540.570 630.956 60.817 520.646 49
PointConvpermissive0.666 550.781 440.759 500.699 680.644 540.822 480.475 470.779 480.564 640.504 740.953 370.428 750.203 780.586 570.754 520.661 590.753 530.588 590.902 400.813 550.642 50
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 550.703 790.781 390.751 590.655 470.830 390.471 490.769 510.474 880.537 600.951 430.475 530.279 430.635 430.698 660.675 530.751 540.553 740.816 860.806 570.703 32
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 570.746 620.708 720.722 610.638 560.820 510.451 580.566 940.599 470.541 580.950 470.510 410.313 250.648 400.819 430.616 770.682 800.590 570.869 710.810 560.656 45
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 580.558 1000.751 560.655 830.690 360.722 920.453 570.867 160.579 570.576 500.893 1030.523 360.293 330.733 280.571 810.692 450.659 870.606 480.875 610.804 590.668 41
DCM-Net0.658 580.778 450.702 750.806 370.619 600.813 610.468 510.693 740.494 810.524 660.941 730.449 670.298 310.510 790.821 420.675 530.727 640.568 660.826 840.803 600.637 52
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 600.698 810.743 630.650 850.564 770.820 510.505 330.758 540.631 310.479 780.945 610.480 510.226 650.572 610.774 500.690 480.735 600.614 440.853 780.776 810.597 68
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 610.752 590.734 670.664 810.583 720.815 570.399 820.754 560.639 280.535 620.942 710.470 550.309 270.665 360.539 830.650 620.708 700.635 350.857 770.793 680.642 50
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 620.778 450.731 680.699 680.577 730.829 400.446 630.736 620.477 870.523 680.945 610.454 620.269 510.484 870.749 550.618 750.738 580.599 520.827 830.792 710.621 57
PointConv-SFPN0.641 630.776 470.703 740.721 620.557 800.826 430.451 580.672 790.563 650.483 770.943 700.425 780.162 940.644 410.726 570.659 600.709 690.572 620.875 610.786 760.559 81
MVPNetpermissive0.641 630.831 280.715 700.671 780.590 680.781 760.394 840.679 760.642 260.553 550.937 780.462 580.256 570.649 390.406 970.626 730.691 770.666 260.877 590.792 710.608 61
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 650.717 760.701 760.692 710.576 740.801 660.467 530.716 670.563 650.459 840.953 370.429 740.169 910.581 580.854 310.605 780.710 670.550 760.894 470.793 680.575 73
FPConvpermissive0.639 660.785 420.760 490.713 660.603 630.798 680.392 850.534 990.603 450.524 660.948 530.457 600.250 590.538 730.723 590.598 820.696 750.614 440.872 670.799 610.567 78
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 670.797 380.769 460.641 910.590 680.820 510.461 550.537 980.637 290.536 610.947 550.388 880.206 750.656 370.668 700.647 660.732 620.585 600.868 720.793 680.473 101
PointSPNet0.637 680.734 680.692 830.714 650.576 740.797 690.446 630.743 600.598 480.437 890.942 710.403 840.150 980.626 470.800 480.649 630.697 740.557 720.846 800.777 800.563 79
SConv0.636 690.830 290.697 790.752 580.572 760.780 780.445 650.716 670.529 710.530 630.951 430.446 690.170 900.507 820.666 710.636 710.682 800.541 820.886 520.799 610.594 69
Supervoxel-CNN0.635 700.656 860.711 710.719 630.613 610.757 870.444 680.765 520.534 700.566 520.928 890.478 520.272 470.636 420.531 850.664 570.645 910.508 890.864 740.792 710.611 58
joint point-basedpermissive0.634 710.614 940.778 400.667 800.633 580.825 440.420 760.804 420.467 900.561 530.951 430.494 440.291 350.566 630.458 920.579 880.764 440.559 710.838 810.814 530.598 67
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 720.731 700.688 860.675 750.591 670.784 750.444 680.565 950.610 390.492 750.949 510.456 610.254 580.587 550.706 620.599 810.665 860.612 470.868 720.791 740.579 72
3DSM_DMMF0.631 730.626 910.745 610.801 400.607 620.751 880.506 320.729 650.565 630.491 760.866 1060.434 700.197 820.595 530.630 750.709 380.705 720.560 690.875 610.740 910.491 96
PointNet2-SFPN0.631 730.771 510.692 830.672 760.524 850.837 320.440 700.706 720.538 690.446 860.944 670.421 800.219 700.552 690.751 540.591 840.737 590.543 810.901 420.768 830.557 82
APCF-Net0.631 730.742 650.687 880.672 760.557 800.792 730.408 780.665 800.545 680.508 710.952 410.428 750.186 850.634 440.702 640.620 740.706 710.555 730.873 650.798 630.581 71
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 760.604 960.741 650.766 540.590 680.747 890.501 350.734 630.503 800.527 640.919 950.454 620.323 220.550 710.420 960.678 520.688 780.544 790.896 450.795 650.627 56
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 770.800 370.625 990.719 630.545 820.806 630.445 650.597 880.448 950.519 690.938 770.481 500.328 200.489 860.499 900.657 610.759 490.592 560.881 550.797 640.634 53
SegGroup_sempermissive0.627 780.818 330.747 600.701 670.602 640.764 840.385 890.629 850.490 830.508 710.931 880.409 830.201 790.564 640.725 580.618 750.692 760.539 830.873 650.794 660.548 85
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 790.830 290.694 810.757 560.563 780.772 820.448 620.647 830.520 740.509 700.949 510.431 730.191 830.496 840.614 770.647 660.672 840.535 850.876 600.783 770.571 74
dtc_net0.625 790.703 790.751 560.794 430.535 830.848 220.480 460.676 780.528 720.469 810.944 670.454 620.004 1120.464 890.636 740.704 410.758 500.548 780.924 260.787 750.492 95
HPEIN0.618 810.729 710.668 890.647 870.597 660.766 830.414 770.680 750.520 740.525 650.946 580.432 710.215 720.493 850.599 780.638 700.617 960.570 630.897 440.806 570.605 64
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCNpermissive0.610 820.858 230.772 420.489 1040.532 840.792 730.404 810.643 840.570 620.507 730.935 810.414 820.046 1090.510 790.702 640.602 800.705 720.549 770.859 760.773 820.534 88
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 830.760 560.667 900.649 860.521 860.793 710.457 560.648 820.528 720.434 910.947 550.401 850.153 970.454 900.721 600.648 650.717 660.536 840.904 370.765 840.485 97
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 840.634 900.743 630.697 700.601 650.781 760.437 720.585 910.493 820.446 860.933 860.394 860.011 1110.654 380.661 730.603 790.733 610.526 860.832 820.761 860.480 98
LAP-D0.594 850.720 740.692 830.637 920.456 960.773 810.391 870.730 640.587 520.445 880.940 750.381 890.288 360.434 930.453 940.591 840.649 890.581 610.777 900.749 900.610 60
DPC0.592 860.720 740.700 770.602 960.480 920.762 860.380 900.713 700.585 550.437 890.940 750.369 910.288 360.434 930.509 890.590 860.639 940.567 670.772 920.755 880.592 70
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 870.766 550.659 940.683 730.470 950.740 910.387 880.620 870.490 830.476 790.922 930.355 940.245 620.511 780.511 880.571 890.643 920.493 930.872 670.762 850.600 66
ROSMRF0.580 880.772 500.707 730.681 740.563 780.764 840.362 920.515 1000.465 910.465 830.936 800.427 770.207 740.438 910.577 800.536 920.675 830.486 940.723 980.779 780.524 91
SD-DETR0.576 890.746 620.609 1030.445 1080.517 870.643 1030.366 910.714 690.456 930.468 820.870 1050.432 710.264 540.558 670.674 680.586 870.688 780.482 950.739 960.733 930.537 87
SQN_0.1%0.569 900.676 830.696 800.657 820.497 880.779 790.424 740.548 960.515 760.376 960.902 1020.422 790.357 80.379 980.456 930.596 830.659 870.544 790.685 1010.665 1040.556 83
TextureNetpermissive0.566 910.672 850.664 910.671 780.494 900.719 930.445 650.678 770.411 1010.396 940.935 810.356 930.225 670.412 950.535 840.565 900.636 950.464 970.794 890.680 1010.568 77
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet0.562 920.648 870.700 770.770 510.586 710.687 970.333 960.650 810.514 770.475 800.906 990.359 920.223 690.340 1000.442 950.422 1030.668 850.501 900.708 990.779 780.534 88
Pointnet++ & Featurepermissive0.557 930.735 670.661 930.686 720.491 910.744 900.392 850.539 970.451 940.375 970.946 580.376 900.205 760.403 960.356 1000.553 910.643 920.497 910.824 850.756 870.515 92
GMLPs0.538 940.495 1050.693 820.647 870.471 940.793 710.300 990.477 1010.505 790.358 990.903 1010.327 970.081 1060.472 880.529 860.448 1010.710 670.509 870.746 940.737 920.554 84
PanopticFusion-label0.529 950.491 1060.688 860.604 950.386 1010.632 1040.225 1090.705 730.434 980.293 1050.815 1070.348 950.241 630.499 830.669 690.507 940.649 890.442 1030.796 880.602 1080.561 80
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 960.676 830.591 1060.609 930.442 970.774 800.335 950.597 880.422 1000.357 1000.932 870.341 960.094 1050.298 1020.528 870.473 990.676 820.495 920.602 1070.721 960.349 108
Online SegFusion0.515 970.607 950.644 970.579 980.434 980.630 1050.353 930.628 860.440 960.410 920.762 1110.307 990.167 920.520 760.403 980.516 930.565 990.447 1010.678 1020.701 980.514 93
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF0.501 980.558 1000.608 1040.424 1100.478 930.690 960.246 1050.586 900.468 890.450 850.911 970.394 860.160 950.438 910.212 1070.432 1020.541 1050.475 960.742 950.727 940.477 99
PCNN0.498 990.559 990.644 970.560 1000.420 1000.711 950.229 1070.414 1020.436 970.352 1010.941 730.324 980.155 960.238 1070.387 990.493 950.529 1060.509 870.813 870.751 890.504 94
Weakly-Openseg v30.489 1000.749 610.664 910.646 890.496 890.559 1090.122 1120.577 920.257 1120.364 980.805 1080.198 1100.096 1040.510 790.496 910.361 1070.563 1000.359 1100.777 900.644 1050.532 90
3DMV0.484 1010.484 1070.538 1080.643 900.424 990.606 1080.310 970.574 930.433 990.378 950.796 1090.301 1000.214 730.537 740.208 1080.472 1000.507 1090.413 1060.693 1000.602 1080.539 86
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1020.577 980.611 1020.356 1120.321 1090.715 940.299 1010.376 1060.328 1080.319 1030.944 670.285 1020.164 930.216 1100.229 1050.484 970.545 1040.456 990.755 930.709 970.475 100
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1030.679 820.604 1050.578 990.380 1020.682 980.291 1020.106 1120.483 860.258 1100.920 940.258 1060.025 1100.231 1090.325 1010.480 980.560 1020.463 980.725 970.666 1030.231 112
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1040.474 1080.623 1000.463 1060.366 1040.651 1010.310 970.389 1050.349 1060.330 1020.937 780.271 1040.126 1010.285 1030.224 1060.350 1090.577 980.445 1020.625 1050.723 950.394 104
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET20.442 1050.548 1020.548 1070.597 970.363 1050.628 1060.300 990.292 1070.374 1030.307 1040.881 1040.268 1050.186 850.238 1070.204 1090.407 1040.506 1100.449 1000.667 1030.620 1070.462 102
SurfaceConvPF0.442 1050.505 1040.622 1010.380 1110.342 1070.654 1000.227 1080.397 1040.367 1040.276 1070.924 910.240 1070.198 810.359 990.262 1030.366 1050.581 970.435 1040.640 1040.668 1020.398 103
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1070.437 1100.646 960.474 1050.369 1030.645 1020.353 930.258 1090.282 1100.279 1060.918 960.298 1010.147 1000.283 1040.294 1020.487 960.562 1010.427 1050.619 1060.633 1060.352 107
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1080.525 1030.647 950.522 1010.324 1080.488 1120.077 1130.712 710.353 1050.401 930.636 1130.281 1030.176 880.340 1000.565 820.175 1130.551 1030.398 1070.370 1130.602 1080.361 106
SPLAT Netcopyleft0.393 1090.472 1090.511 1090.606 940.311 1100.656 990.245 1060.405 1030.328 1080.197 1110.927 900.227 1090.000 1140.001 1140.249 1040.271 1120.510 1070.383 1090.593 1080.699 990.267 110
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 1100.297 1120.491 1100.432 1090.358 1060.612 1070.274 1030.116 1110.411 1010.265 1080.904 1000.229 1080.079 1070.250 1050.185 1100.320 1100.510 1070.385 1080.548 1090.597 1110.394 104
PointNet++permissive0.339 1110.584 970.478 1110.458 1070.256 1120.360 1130.250 1040.247 1100.278 1110.261 1090.677 1120.183 1110.117 1020.212 1110.145 1120.364 1060.346 1130.232 1130.548 1090.523 1120.252 111
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 1120.353 1110.290 1130.278 1130.166 1130.553 1100.169 1110.286 1080.147 1130.148 1130.908 980.182 1120.064 1080.023 1130.018 1140.354 1080.363 1110.345 1110.546 1110.685 1000.278 109
ScanNetpermissive0.306 1130.203 1130.366 1120.501 1020.311 1100.524 1110.211 1100.002 1140.342 1070.189 1120.786 1100.145 1130.102 1030.245 1060.152 1110.318 1110.348 1120.300 1120.460 1120.437 1130.182 113
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 1140.000 1140.041 1140.172 1140.030 1140.062 1140.001 1140.035 1130.004 1140.051 1140.143 1140.019 1140.003 1130.041 1120.050 1130.003 1140.054 1140.018 1140.005 1140.264 1140.082 114


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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Spherical Mask(CtoF)0.616 10.946 30.654 90.555 40.434 70.769 30.271 60.604 70.447 30.505 40.549 10.698 10.716 10.775 100.480 50.747 30.575 60.925 70.436 3
ExtMask3D0.598 20.852 120.692 40.433 220.461 40.791 10.264 70.488 290.493 10.508 30.528 90.594 60.706 30.791 50.483 30.734 60.595 20.911 110.437 2
MAFT0.596 30.889 90.721 10.448 160.460 50.768 40.251 80.558 160.408 40.504 50.539 50.616 40.618 70.858 20.482 40.684 130.551 100.931 60.450 1
UniPerception0.588 40.963 20.667 70.493 90.472 30.750 70.229 110.528 220.468 20.498 70.542 30.643 20.530 160.661 300.463 100.695 120.599 10.972 10.420 5
Queryformer0.583 50.926 50.702 20.393 280.504 10.733 130.276 50.527 230.373 100.479 80.534 70.533 150.697 40.720 220.436 140.745 40.592 30.958 30.363 14
SIM3D0.575 60.889 90.675 60.284 440.401 120.762 60.329 20.531 210.408 50.521 20.541 40.587 70.646 50.744 180.467 80.665 150.579 50.886 210.425 4
PBNetpermissive0.573 70.926 50.575 180.619 10.472 20.736 110.239 100.487 300.383 90.459 110.506 120.533 140.585 90.767 110.404 160.717 70.559 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
TST3D0.569 80.778 190.675 50.598 20.451 60.727 140.280 40.476 320.395 60.472 90.457 200.583 80.580 110.777 70.462 120.735 50.547 120.919 90.333 20
Mask3D0.566 90.926 50.597 130.408 250.420 100.737 100.239 90.598 90.386 80.458 120.549 10.568 120.716 10.601 360.480 50.646 180.575 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
OneFormer3Dcopyleft0.566 90.781 180.697 30.562 30.431 80.770 20.331 10.400 380.373 110.529 10.504 130.568 110.475 210.732 200.470 70.762 10.550 110.871 270.379 11
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
ISBNetpermissive0.559 110.939 40.655 80.383 310.426 90.763 50.180 130.534 200.386 70.499 60.509 110.621 30.427 310.704 250.467 90.649 170.571 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
GraphCut0.552 121.000 10.611 120.438 190.392 140.714 150.139 160.598 100.327 140.389 150.510 100.598 50.427 320.754 140.463 110.761 20.588 40.903 140.329 21
SPFormerpermissive0.549 130.745 220.640 100.484 100.395 130.739 90.311 30.566 140.335 130.468 100.492 140.555 130.478 200.747 160.436 130.712 80.540 130.893 180.343 19
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet0.532 140.815 150.624 110.517 60.377 160.749 80.107 180.509 260.304 160.437 130.475 150.581 90.539 140.775 90.339 210.640 200.506 160.901 150.385 9
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst0.520 150.889 90.551 220.548 50.418 110.665 250.064 270.585 110.260 240.277 290.471 170.500 160.644 60.785 60.369 170.591 260.511 140.878 240.362 15
SoftGroup++0.513 160.704 280.578 170.398 270.363 220.704 160.061 280.647 40.297 210.378 180.537 60.343 190.614 80.828 40.295 260.710 100.505 180.875 260.394 7
SSTNetpermissive0.506 170.738 250.549 230.497 80.316 270.693 190.178 140.377 410.198 300.330 200.463 190.576 100.515 170.857 30.494 10.637 210.457 220.943 50.290 30
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGrouppermissive0.504 180.667 350.579 150.372 330.381 150.694 180.072 240.677 20.303 170.387 160.531 80.319 230.582 100.754 130.318 220.643 190.492 190.907 130.388 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]
DANCENET0.504 180.926 50.579 140.472 120.367 190.626 350.165 150.432 330.221 260.408 140.449 220.411 170.564 120.746 170.421 150.707 110.438 250.846 350.288 31
TD3Dpermissive0.489 200.852 120.511 320.434 200.322 260.735 120.101 210.512 250.355 120.349 190.468 180.283 270.514 180.676 290.268 310.671 140.510 150.908 120.329 22
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
OccuSeg+instance0.486 210.802 170.536 250.428 230.369 180.702 170.205 120.331 460.301 180.379 170.474 160.327 200.437 260.862 10.485 20.601 240.394 330.846 370.273 34
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg0.479 220.704 280.564 190.467 140.366 200.633 330.068 250.554 170.262 230.328 210.447 230.323 210.534 150.722 210.288 280.614 220.482 200.912 100.358 17
DualGroup0.469 230.815 150.552 210.398 260.374 170.683 210.130 170.539 190.310 150.327 220.407 260.276 280.447 250.535 400.342 200.659 160.455 230.900 170.301 26
SSEC0.465 240.667 350.578 160.502 70.362 230.641 320.035 370.605 60.291 220.323 230.451 210.296 250.417 350.677 280.245 350.501 440.506 170.900 160.366 12
HAISpermissive0.457 250.704 280.561 200.457 150.364 210.673 220.046 360.547 180.194 310.308 240.426 240.288 260.454 240.711 230.262 320.563 340.434 270.889 200.344 18
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group0.436 260.630 430.508 350.480 110.310 290.624 370.065 260.638 50.174 320.256 330.384 300.194 400.428 290.759 120.289 270.574 310.400 310.849 340.291 29
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance0.435 270.716 270.495 370.355 350.331 240.689 200.102 200.394 400.208 290.280 270.395 280.250 310.544 130.741 190.309 240.536 400.391 340.842 400.258 38
Mask-Group0.434 280.778 190.516 300.471 130.330 250.658 260.029 390.526 240.249 250.256 320.400 270.309 240.384 390.296 560.368 180.575 300.425 280.877 250.362 16
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask0.433 290.741 230.463 420.433 210.283 320.625 360.103 190.298 510.125 410.260 310.424 250.322 220.472 220.701 260.363 190.711 90.309 500.882 220.272 36
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN0.428 300.630 430.508 340.367 340.249 390.658 270.016 470.673 30.131 390.234 360.383 310.270 290.434 270.748 150.274 300.609 230.406 300.842 390.267 37
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet0.413 310.741 230.520 270.237 470.284 310.523 460.097 220.691 10.138 360.209 460.229 480.238 340.390 370.707 240.310 230.448 510.470 210.892 190.310 24
PointGroup0.407 320.639 420.496 360.415 240.243 410.645 310.021 440.570 130.114 420.211 440.359 330.217 380.428 300.660 310.256 330.562 350.341 420.860 300.291 28
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
CSC-Pretrained0.405 330.738 250.465 410.331 390.205 450.655 280.051 320.601 80.092 460.211 450.329 360.198 390.459 230.775 80.195 420.524 420.400 320.878 230.184 47
PE0.396 340.667 350.467 400.446 180.243 400.624 380.022 430.577 120.106 430.219 390.340 340.239 330.487 190.475 470.225 370.541 390.350 400.818 420.273 35
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3Dcopyleft0.395 350.642 410.518 290.447 170.259 380.666 240.050 330.251 560.166 330.231 370.362 320.232 350.331 420.535 390.229 360.587 270.438 260.850 320.317 23
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS0.392 360.778 190.530 260.220 490.278 330.567 430.083 230.330 470.299 190.270 300.310 390.143 460.260 460.624 340.277 290.568 330.361 380.865 290.301 25
AOIA0.387 370.704 280.515 310.385 300.225 440.669 230.005 540.482 310.126 400.181 490.269 450.221 370.426 330.478 460.218 380.592 250.371 360.851 310.242 40
SSEN0.384 380.852 120.494 380.192 500.226 430.648 300.022 420.398 390.299 200.277 280.317 380.231 360.194 530.514 430.196 400.586 280.444 240.843 380.184 46
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Mask3D_evaluation0.382 390.593 450.520 280.390 290.314 280.600 390.018 460.287 540.151 350.281 260.387 290.169 440.429 280.654 320.172 460.578 290.384 350.670 530.278 33
PCJC0.375 400.704 280.542 240.284 430.197 470.649 290.006 510.426 340.138 370.242 340.304 400.183 430.388 380.629 330.141 530.546 380.344 410.738 480.283 32
ClickSeg_Instance0.366 410.654 390.375 460.184 510.302 300.592 410.050 340.300 500.093 450.283 250.277 420.249 320.426 340.615 350.299 250.504 430.367 370.832 410.191 45
SphereSeg0.357 420.651 400.411 440.345 360.264 370.630 340.059 290.289 530.212 270.240 350.336 350.158 450.305 430.557 370.159 490.455 500.341 430.726 500.294 27
3D-MPA0.355 430.457 550.484 390.299 410.277 340.591 420.047 350.332 440.212 280.217 400.278 410.193 410.413 360.410 500.195 410.574 320.352 390.849 330.213 43
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF0.353 440.593 450.511 330.375 320.264 360.597 400.008 490.332 450.160 340.229 380.274 440.000 670.206 500.678 270.155 500.485 460.422 290.816 430.254 39
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
RWSeg0.348 450.475 520.456 430.320 400.275 350.476 480.020 450.491 280.056 530.212 430.320 370.261 300.302 440.520 410.182 440.557 360.285 520.867 280.197 44
GICN0.341 460.580 470.371 470.344 370.198 460.469 490.052 310.564 150.093 440.212 420.212 500.127 480.347 410.537 380.206 390.525 410.329 450.729 490.241 41
One_Thing_One_Clickpermissive0.326 470.472 530.361 480.232 480.183 480.555 440.000 600.498 270.038 550.195 470.226 490.362 180.168 540.469 480.251 340.553 370.335 440.846 360.117 55
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS0.320 480.679 340.352 490.334 380.229 420.436 500.025 400.412 370.058 510.161 540.240 470.085 500.262 450.496 450.187 430.467 480.328 460.775 440.231 42
Sparse R-CNN0.292 490.704 280.213 590.153 530.154 500.551 450.053 300.212 570.132 380.174 510.274 430.070 520.363 400.441 490.176 450.424 530.234 540.758 460.161 51
MTML0.282 500.577 480.380 450.182 520.107 560.430 510.001 570.422 350.057 520.179 500.162 530.070 530.229 480.511 440.161 470.491 450.313 470.650 560.162 49
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet0.262 510.667 350.335 500.067 600.123 540.427 520.022 410.280 550.058 500.216 410.211 510.039 560.142 560.519 420.106 570.338 570.310 490.721 510.138 52
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.254 520.463 540.249 580.113 540.167 490.412 540.000 590.374 420.073 470.173 520.243 460.130 470.228 490.368 520.160 480.356 550.208 550.711 520.136 53
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet0.253 530.519 500.324 530.251 460.137 530.345 590.031 380.419 360.069 480.162 530.131 550.052 540.202 520.338 540.147 520.301 600.303 510.651 550.178 48
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
SPG_WSIS0.251 540.380 570.274 560.289 420.144 510.413 530.000 600.311 480.065 490.113 560.130 560.029 590.204 510.388 510.108 560.459 490.311 480.769 450.127 54
SegGroup_inspermissive0.246 550.556 490.335 510.062 620.115 550.490 470.000 600.297 520.018 590.186 480.142 540.083 510.233 470.216 580.153 510.469 470.251 530.744 470.083 58
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst0.214 560.250 620.330 520.275 450.103 570.228 650.000 600.345 430.024 570.088 580.203 520.186 420.167 550.367 530.125 540.221 630.112 650.666 540.162 50
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
UNet-backbone0.161 570.519 500.259 570.084 560.059 590.325 610.002 550.093 620.009 610.077 600.064 590.045 550.044 630.161 600.045 590.331 580.180 570.566 570.033 67
3D-SISpermissive0.161 570.407 560.155 640.068 590.043 630.346 580.001 560.134 590.005 620.088 570.106 580.037 570.135 580.321 550.028 630.339 560.116 640.466 600.093 57
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.158 590.356 580.173 620.113 550.140 520.359 550.012 480.023 650.039 540.134 550.123 570.008 630.089 590.149 610.117 550.221 620.128 620.563 580.094 56
Region-18class0.146 600.175 660.321 540.080 570.062 580.357 560.000 600.307 490.002 640.066 610.044 610.000 670.018 650.036 660.054 580.447 520.133 600.472 590.060 62
SemRegionNet-20cls0.121 610.296 600.203 600.071 580.058 600.349 570.000 600.150 580.019 580.054 630.034 640.017 620.052 610.042 650.013 660.209 640.183 560.371 610.057 63
3D-BEVIS0.117 620.250 620.308 550.020 660.009 680.269 640.006 520.008 660.029 560.037 660.014 670.003 650.036 640.147 620.042 610.381 540.118 630.362 620.069 61
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3Dcopyleft0.117 620.222 640.161 630.054 640.027 650.289 620.000 600.124 600.001 660.079 590.061 600.027 600.141 570.240 570.005 670.310 590.129 610.153 670.081 59
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp0.113 640.333 590.151 650.056 630.053 610.344 600.000 600.105 610.016 600.049 640.035 630.020 610.053 600.048 640.013 650.183 660.173 580.344 640.054 64
Sem_Recon_ins0.098 650.295 610.187 610.015 670.036 640.213 660.005 530.038 640.003 630.056 620.037 620.036 580.015 660.051 630.044 600.209 650.098 660.354 630.071 60
ASIS0.085 660.037 670.080 670.066 610.047 620.282 630.000 600.052 630.002 650.047 650.026 650.001 660.046 620.194 590.031 620.264 610.140 590.167 660.047 66
Sgpn_scannet0.049 670.023 680.134 660.031 650.013 670.144 670.006 500.008 670.000 670.028 670.017 660.003 640.009 680.000 670.021 640.122 670.095 670.175 650.054 65
MaskRCNN 2d->3d Proj0.022 680.185 650.000 680.000 680.015 660.000 680.000 580.006 680.000 670.010 680.006 680.107 490.012 670.000 670.002 680.027 680.004 680.022 680.001 68


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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoor