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 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 by
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
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 (Oral)
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
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
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
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
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
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.
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
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


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 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 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
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
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.
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


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 30.813 180.851 70.782 60.890 20.597 10.916 20.696 80.713 30.979 10.635 10.384 20.793 20.907 80.821 40.790 300.696 110.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV20.785 20.978 10.800 260.833 220.788 40.853 160.545 160.910 50.713 10.705 40.979 10.596 70.390 10.769 110.832 410.821 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 10.843 160.781 70.858 120.575 60.831 320.685 140.714 20.979 10.594 80.310 260.801 10.892 160.841 20.819 40.723 40.940 130.887 60.725 23
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 140.836 190.790 30.875 40.576 50.905 60.704 50.739 10.969 100.611 20.349 100.756 210.958 10.702 440.805 140.708 70.916 310.898 30.801 2
TTT-KD0.773 50.646 900.818 140.809 340.774 90.878 30.581 20.943 10.687 120.704 50.978 40.607 50.336 150.775 80.912 60.838 30.823 20.694 120.967 30.899 20.794 3
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS0.772 60.939 40.824 60.854 60.771 100.840 300.564 100.900 80.686 130.677 110.961 160.537 300.348 110.769 110.903 100.785 100.815 60.676 210.939 140.880 110.772 8
PPT-SpUNet-Joint0.766 70.932 50.794 320.829 240.751 220.854 140.540 200.903 70.630 330.672 140.963 140.565 210.357 80.788 30.900 120.737 250.802 150.685 160.950 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
OctFormerpermissive0.766 70.925 70.808 220.849 90.786 50.846 260.566 90.876 140.690 100.674 130.960 170.576 170.226 660.753 230.904 90.777 120.815 60.722 50.923 270.877 130.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CU-Hybrid Net0.764 90.924 80.819 120.840 170.757 170.853 160.580 30.848 250.709 30.643 230.958 200.587 120.295 320.753 230.884 200.758 190.815 60.725 30.927 240.867 210.743 14
OccuSeg+Semantic0.764 90.758 580.796 300.839 180.746 240.907 10.562 110.850 240.680 160.672 140.978 40.610 30.335 170.777 60.819 440.847 10.830 10.691 140.972 20.885 80.727 21
O-CNNpermissive0.762 110.924 80.823 70.844 150.770 110.852 180.577 40.847 270.711 20.640 270.958 200.592 90.217 720.762 160.888 170.758 190.813 100.726 20.932 220.868 200.744 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
DTC0.757 120.843 260.820 100.847 120.791 20.862 100.511 320.870 160.707 40.652 190.954 340.604 60.279 430.760 170.942 20.734 260.766 430.701 100.884 530.874 180.736 15
OA-CNN-L_ScanNet200.756 130.783 440.826 50.858 40.776 80.837 330.548 150.896 110.649 250.675 120.962 150.586 130.335 170.771 100.802 480.770 150.787 320.691 140.936 170.880 110.761 10
ConDaFormer0.755 140.927 60.822 80.836 190.801 10.849 210.516 300.864 210.651 240.680 100.958 200.584 150.282 400.759 190.855 310.728 280.802 150.678 180.880 580.873 190.756 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
PNE0.755 140.786 420.835 40.834 210.758 150.849 210.570 80.836 310.648 260.668 160.978 40.581 160.367 60.683 340.856 290.804 60.801 190.678 180.961 50.889 50.716 28
P. Hermosilla: Point Neighborhood Embeddings.
DMF-Net0.752 160.906 120.793 340.802 400.689 390.825 450.556 120.867 170.681 150.602 430.960 170.555 260.365 70.779 50.859 260.747 220.795 260.717 60.917 300.856 290.764 9
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointTransformerV20.752 160.742 660.809 210.872 10.758 150.860 110.552 130.891 120.610 400.687 60.960 170.559 240.304 290.766 140.926 40.767 160.797 220.644 320.942 110.876 160.722 25
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 180.793 400.790 350.807 360.750 230.856 130.524 260.881 130.588 520.642 260.977 80.591 100.274 460.781 40.929 30.804 60.796 230.642 330.947 90.885 80.715 29
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 180.909 100.818 140.811 320.752 200.839 320.485 460.842 280.673 170.644 220.957 240.528 360.305 280.773 90.859 260.788 80.818 50.693 130.916 310.856 290.723 24
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 200.623 930.804 240.859 30.745 250.824 470.501 360.912 40.690 100.685 80.956 250.567 200.320 230.768 130.918 50.720 330.802 150.676 210.921 280.881 100.779 6
StratifiedFormerpermissive0.747 210.901 130.803 250.845 140.757 170.846 260.512 310.825 350.696 80.645 210.956 250.576 170.262 570.744 280.861 250.742 230.770 410.705 80.899 430.860 260.734 16
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNetpermissive0.746 220.870 180.838 20.858 40.729 300.850 200.501 360.874 150.587 530.658 180.956 250.564 220.299 300.765 150.900 120.716 360.812 110.631 380.939 140.858 270.709 30
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion0.746 220.771 520.819 120.848 110.702 360.865 90.397 840.899 90.699 60.664 170.948 540.588 110.330 190.746 270.851 350.764 170.796 230.704 90.935 180.866 220.728 19
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN0.744 240.842 270.800 260.767 540.740 260.836 350.541 180.914 30.672 180.626 310.958 200.552 270.272 480.777 60.886 190.696 450.801 190.674 240.941 120.858 270.717 26
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-Net0.743 250.620 940.799 290.849 90.730 290.822 490.493 430.897 100.664 190.681 90.955 280.562 230.378 30.760 170.903 100.738 240.801 190.673 250.907 350.877 130.745 12
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 260.860 210.765 480.819 270.769 120.848 230.533 220.829 330.663 200.631 300.955 280.586 130.274 460.753 230.896 140.729 270.760 490.666 270.921 280.855 310.733 17
LRPNet0.742 260.816 350.806 230.807 360.752 200.828 430.575 60.839 300.699 60.637 280.954 340.520 390.320 230.755 220.834 390.760 180.772 380.676 210.915 330.862 240.717 26
LargeKernel3D0.739 280.909 100.820 100.806 380.740 260.852 180.545 160.826 340.594 510.643 230.955 280.541 290.263 560.723 320.858 280.775 140.767 420.678 180.933 200.848 360.694 35
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
MinkowskiNetpermissive0.736 290.859 220.818 140.832 230.709 340.840 300.521 280.853 230.660 220.643 230.951 440.544 280.286 380.731 300.893 150.675 540.772 380.683 170.874 650.852 340.727 21
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
RPN0.736 290.776 480.790 350.851 70.754 190.854 140.491 450.866 190.596 500.686 70.955 280.536 310.342 130.624 490.869 220.787 90.802 150.628 390.927 240.875 170.704 32
IPCA0.731 310.890 140.837 30.864 20.726 310.873 50.530 250.824 360.489 860.647 200.978 40.609 40.336 150.624 490.733 570.758 190.776 360.570 640.949 80.877 130.728 19
SparseConvNet0.725 320.647 890.821 90.846 130.721 320.869 60.533 220.754 570.603 460.614 350.955 280.572 190.325 210.710 330.870 210.724 310.823 20.628 390.934 190.865 230.683 38
PointTransformer++0.725 320.727 740.811 200.819 270.765 130.841 290.502 350.814 410.621 360.623 330.955 280.556 250.284 390.620 510.866 230.781 110.757 530.648 300.932 220.862 240.709 30
MatchingNet0.724 340.812 370.812 190.810 330.735 280.834 370.495 420.860 220.572 600.602 430.954 340.512 410.280 420.757 200.845 370.725 300.780 340.606 490.937 160.851 350.700 34
INS-Conv-semantic0.717 350.751 610.759 510.812 310.704 350.868 70.537 210.842 280.609 420.608 390.953 380.534 330.293 330.616 520.864 240.719 350.793 270.640 340.933 200.845 400.663 44
PointMetaBase0.714 360.835 280.785 370.821 250.684 410.846 260.531 240.865 200.614 370.596 470.953 380.500 440.246 620.674 350.888 170.692 460.764 450.624 410.849 800.844 410.675 40
contrastBoundarypermissive0.705 370.769 550.775 420.809 340.687 400.820 520.439 720.812 420.661 210.591 490.945 620.515 400.171 900.633 460.856 290.720 330.796 230.668 260.889 500.847 370.689 36
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 380.774 500.800 260.793 450.760 140.847 250.471 500.802 450.463 930.634 290.968 120.491 470.271 500.726 310.910 70.706 400.815 60.551 760.878 590.833 420.570 76
RFCR0.702 390.889 150.745 620.813 300.672 440.818 560.493 430.815 400.623 340.610 370.947 560.470 560.249 610.594 550.848 360.705 410.779 350.646 310.892 480.823 480.611 59
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 Click0.701 400.825 320.796 300.723 610.716 330.832 390.433 740.816 380.634 310.609 380.969 100.418 820.344 120.559 670.833 400.715 370.808 130.560 700.902 400.847 370.680 39
JSENetpermissive0.699 410.881 170.762 490.821 250.667 450.800 680.522 270.792 480.613 380.607 400.935 820.492 460.205 770.576 600.853 330.691 480.758 510.652 290.872 680.828 450.649 48
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-Click0.693 420.743 650.794 320.655 840.684 410.822 490.497 410.719 670.622 350.617 340.977 80.447 690.339 140.750 260.664 730.703 430.790 300.596 540.946 100.855 310.647 49
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 430.732 700.772 430.786 460.677 430.866 80.517 290.848 250.509 790.626 310.952 420.536 310.225 680.545 730.704 640.689 510.810 120.564 690.903 390.854 330.729 18
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 440.884 160.754 550.795 430.647 520.818 560.422 760.802 450.612 390.604 410.945 620.462 590.189 850.563 660.853 330.726 290.765 440.632 370.904 370.821 510.606 63
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 450.704 790.741 660.754 580.656 470.829 410.501 360.741 620.609 420.548 570.950 480.522 380.371 40.633 460.756 520.715 370.771 400.623 420.861 760.814 540.658 45
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 460.866 190.748 590.819 270.645 540.794 710.450 620.802 450.587 530.604 410.945 620.464 580.201 800.554 690.840 380.723 320.732 630.602 520.907 350.822 500.603 66
KP-FCNN0.684 470.847 250.758 530.784 480.647 520.814 590.473 490.772 510.605 440.594 480.935 820.450 670.181 880.587 560.805 470.690 490.785 330.614 450.882 550.819 520.632 55
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 470.728 730.757 540.776 510.690 370.804 660.464 550.816 380.577 590.587 500.945 620.508 430.276 450.671 360.710 620.663 590.750 570.589 590.881 560.832 440.653 47
DGNet0.684 470.712 780.784 380.782 500.658 460.835 360.499 400.823 370.641 280.597 460.950 480.487 490.281 410.575 610.619 770.647 670.764 450.620 440.871 710.846 390.688 37
PointContrast_LA_SEM0.683 500.757 590.784 380.786 460.639 560.824 470.408 790.775 500.604 450.541 590.934 860.532 340.269 520.552 700.777 500.645 700.793 270.640 340.913 340.824 470.671 41
Superpoint Network0.683 500.851 240.728 700.800 420.653 490.806 640.468 520.804 430.572 600.602 430.946 590.453 660.239 650.519 780.822 420.689 510.762 480.595 560.895 460.827 460.630 56
VI-PointConv0.676 520.770 540.754 550.783 490.621 600.814 590.552 130.758 550.571 620.557 550.954 340.529 350.268 540.530 760.682 680.675 540.719 660.603 510.888 510.833 420.665 43
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 530.789 410.748 590.763 560.635 580.814 590.407 810.747 590.581 570.573 520.950 480.484 500.271 500.607 530.754 530.649 640.774 370.596 540.883 540.823 480.606 63
SALANet0.670 540.816 350.770 460.768 530.652 500.807 630.451 590.747 590.659 230.545 580.924 920.473 550.149 1000.571 630.811 460.635 730.746 580.623 420.892 480.794 670.570 76
O3DSeg0.668 550.822 330.771 450.496 1040.651 510.833 380.541 180.761 540.555 680.611 360.966 130.489 480.370 50.388 980.580 800.776 130.751 550.570 640.956 60.817 530.646 50
PointConvpermissive0.666 560.781 450.759 510.699 690.644 550.822 490.475 480.779 490.564 650.504 750.953 380.428 760.203 790.586 580.754 530.661 600.753 540.588 600.902 400.813 560.642 51
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 560.703 800.781 400.751 600.655 480.830 400.471 500.769 520.474 890.537 610.951 440.475 540.279 430.635 440.698 670.675 540.751 550.553 750.816 870.806 580.703 33
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++permissive0.663 580.746 630.708 730.722 620.638 570.820 520.451 590.566 950.599 480.541 590.950 480.510 420.313 250.648 410.819 440.616 780.682 810.590 580.869 720.810 570.656 46
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 590.558 1010.751 570.655 840.690 370.722 930.453 580.867 170.579 580.576 510.893 1040.523 370.293 330.733 290.571 820.692 460.659 880.606 490.875 620.804 600.668 42
DCM-Net0.658 590.778 460.702 760.806 380.619 610.813 620.468 520.693 750.494 820.524 670.941 740.449 680.298 310.510 800.821 430.675 540.727 650.568 670.826 850.803 610.637 53
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 610.698 820.743 640.650 860.564 780.820 520.505 340.758 550.631 320.479 790.945 620.480 520.226 660.572 620.774 510.690 490.735 610.614 450.853 790.776 820.597 69
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 620.752 600.734 680.664 820.583 730.815 580.399 830.754 570.639 290.535 630.942 720.470 560.309 270.665 370.539 840.650 630.708 710.635 360.857 780.793 690.642 51
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 630.778 460.731 690.699 690.577 740.829 410.446 640.736 630.477 880.523 690.945 620.454 630.269 520.484 880.749 560.618 760.738 590.599 530.827 840.792 720.621 58
PointConv-SFPN0.641 640.776 480.703 750.721 630.557 810.826 440.451 590.672 800.563 660.483 780.943 710.425 790.162 950.644 420.726 580.659 610.709 700.572 630.875 620.786 770.559 82
MVPNetpermissive0.641 640.831 290.715 710.671 790.590 690.781 770.394 850.679 770.642 270.553 560.937 790.462 590.256 580.649 400.406 980.626 740.691 780.666 270.877 600.792 720.608 62
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 660.717 770.701 770.692 720.576 750.801 670.467 540.716 680.563 660.459 850.953 380.429 750.169 920.581 590.854 320.605 790.710 680.550 770.894 470.793 690.575 74
FPConvpermissive0.639 670.785 430.760 500.713 670.603 640.798 690.392 860.534 1000.603 460.524 670.948 540.457 610.250 600.538 740.723 600.598 830.696 760.614 450.872 680.799 620.567 79
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 680.797 390.769 470.641 920.590 690.820 520.461 560.537 990.637 300.536 620.947 560.388 890.206 760.656 380.668 710.647 670.732 630.585 610.868 730.793 690.473 102
PointSPNet0.637 690.734 690.692 840.714 660.576 750.797 700.446 640.743 610.598 490.437 900.942 720.403 850.150 990.626 480.800 490.649 640.697 750.557 730.846 810.777 810.563 80
SConv0.636 700.830 300.697 800.752 590.572 770.780 790.445 660.716 680.529 720.530 640.951 440.446 700.170 910.507 830.666 720.636 720.682 810.541 830.886 520.799 620.594 70
Supervoxel-CNN0.635 710.656 870.711 720.719 640.613 620.757 880.444 690.765 530.534 710.566 530.928 900.478 530.272 480.636 430.531 860.664 580.645 920.508 900.864 750.792 720.611 59
joint point-basedpermissive0.634 720.614 950.778 410.667 810.633 590.825 450.420 770.804 430.467 910.561 540.951 440.494 450.291 350.566 640.458 930.579 890.764 450.559 720.838 820.814 540.598 68
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 730.731 710.688 870.675 760.591 680.784 760.444 690.565 960.610 400.492 760.949 520.456 620.254 590.587 560.706 630.599 820.665 870.612 480.868 730.791 750.579 73
3DSM_DMMF0.631 740.626 920.745 620.801 410.607 630.751 890.506 330.729 660.565 640.491 770.866 1070.434 710.197 830.595 540.630 760.709 390.705 730.560 700.875 620.740 920.491 97
PointNet2-SFPN0.631 740.771 520.692 840.672 770.524 860.837 330.440 710.706 730.538 700.446 870.944 680.421 810.219 710.552 700.751 550.591 850.737 600.543 820.901 420.768 840.557 83
APCF-Net0.631 740.742 660.687 890.672 770.557 810.792 740.408 790.665 810.545 690.508 720.952 420.428 760.186 860.634 450.702 650.620 750.706 720.555 740.873 660.798 640.581 72
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 770.604 970.741 660.766 550.590 690.747 900.501 360.734 640.503 810.527 650.919 960.454 630.323 220.550 720.420 970.678 530.688 790.544 800.896 450.795 660.627 57
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 780.800 380.625 1000.719 640.545 830.806 640.445 660.597 890.448 960.519 700.938 780.481 510.328 200.489 870.499 910.657 620.759 500.592 570.881 560.797 650.634 54
SegGroup_sempermissive0.627 790.818 340.747 610.701 680.602 650.764 850.385 900.629 860.490 840.508 720.931 890.409 840.201 800.564 650.725 590.618 760.692 770.539 840.873 660.794 670.548 86
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 800.830 300.694 820.757 570.563 790.772 830.448 630.647 840.520 750.509 710.949 520.431 740.191 840.496 850.614 780.647 670.672 850.535 860.876 610.783 780.571 75
dtc_net0.625 800.703 800.751 570.794 440.535 840.848 230.480 470.676 790.528 730.469 820.944 680.454 630.004 1130.464 900.636 750.704 420.758 510.548 790.924 260.787 760.492 96
HPEIN0.618 820.729 720.668 900.647 880.597 670.766 840.414 780.680 760.520 750.525 660.946 590.432 720.215 730.493 860.599 790.638 710.617 970.570 640.897 440.806 580.605 65
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-GCNpermissive0.610 830.858 230.772 430.489 1050.532 850.792 740.404 820.643 850.570 630.507 740.935 820.414 830.046 1100.510 800.702 650.602 810.705 730.549 780.859 770.773 830.534 89
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 840.760 570.667 910.649 870.521 870.793 720.457 570.648 830.528 730.434 920.947 560.401 860.153 980.454 910.721 610.648 660.717 670.536 850.904 370.765 850.485 98
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 850.634 910.743 640.697 710.601 660.781 770.437 730.585 920.493 830.446 870.933 870.394 870.011 1120.654 390.661 740.603 800.733 620.526 870.832 830.761 870.480 99
LAP-D0.594 860.720 750.692 840.637 930.456 970.773 820.391 880.730 650.587 530.445 890.940 760.381 900.288 360.434 940.453 950.591 850.649 900.581 620.777 910.749 910.610 61
DPC0.592 870.720 750.700 780.602 970.480 930.762 870.380 910.713 710.585 560.437 900.940 760.369 920.288 360.434 940.509 900.590 870.639 950.567 680.772 930.755 890.592 71
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 880.766 560.659 950.683 740.470 960.740 920.387 890.620 880.490 840.476 800.922 940.355 950.245 630.511 790.511 890.571 900.643 930.493 940.872 680.762 860.600 67
ROSMRF0.580 890.772 510.707 740.681 750.563 790.764 850.362 930.515 1010.465 920.465 840.936 810.427 780.207 750.438 920.577 810.536 930.675 840.486 950.723 990.779 790.524 92
SD-DETR0.576 900.746 630.609 1040.445 1090.517 880.643 1040.366 920.714 700.456 940.468 830.870 1060.432 720.264 550.558 680.674 690.586 880.688 790.482 960.739 970.733 940.537 88
SQN_0.1%0.569 910.676 840.696 810.657 830.497 890.779 800.424 750.548 970.515 770.376 970.902 1030.422 800.357 80.379 990.456 940.596 840.659 880.544 800.685 1020.665 1050.556 84
TextureNetpermissive0.566 920.672 860.664 920.671 790.494 910.719 940.445 660.678 780.411 1020.396 950.935 820.356 940.225 680.412 960.535 850.565 910.636 960.464 980.794 900.680 1020.568 78
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
DVVNet0.562 930.648 880.700 780.770 520.586 720.687 980.333 970.650 820.514 780.475 810.906 1000.359 930.223 700.340 1010.442 960.422 1040.668 860.501 910.708 1000.779 790.534 89
Pointnet++ & Featurepermissive0.557 940.735 680.661 940.686 730.491 920.744 910.392 860.539 980.451 950.375 980.946 590.376 910.205 770.403 970.356 1010.553 920.643 930.497 920.824 860.756 880.515 93
GMLPs0.538 950.495 1060.693 830.647 880.471 950.793 720.300 1000.477 1020.505 800.358 1000.903 1020.327 980.081 1070.472 890.529 870.448 1020.710 680.509 880.746 950.737 930.554 85
PanopticFusion-label0.529 960.491 1070.688 870.604 960.386 1020.632 1050.225 1100.705 740.434 990.293 1060.815 1080.348 960.241 640.499 840.669 700.507 950.649 900.442 1040.796 890.602 1090.561 81
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_weak0.516 970.676 840.591 1070.609 940.442 980.774 810.335 960.597 890.422 1010.357 1010.932 880.341 970.094 1060.298 1030.528 880.473 1000.676 830.495 930.602 1080.721 970.349 109
Online SegFusion0.515 980.607 960.644 980.579 990.434 990.630 1060.353 940.628 870.440 970.410 930.762 1120.307 1000.167 930.520 770.403 990.516 940.565 1000.447 1020.678 1030.701 990.514 94
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, FTSDF0.501 990.558 1010.608 1050.424 1110.478 940.690 970.246 1060.586 910.468 900.450 860.911 980.394 870.160 960.438 920.212 1080.432 1030.541 1060.475 970.742 960.727 950.477 100
PCNN0.498 1000.559 1000.644 980.560 1010.420 1010.711 960.229 1080.414 1030.436 980.352 1020.941 740.324 990.155 970.238 1080.387 1000.493 960.529 1070.509 880.813 880.751 900.504 95
Weakly-Openseg v30.489 1010.749 620.664 920.646 900.496 900.559 1100.122 1130.577 930.257 1130.364 990.805 1090.198 1110.096 1050.510 800.496 920.361 1080.563 1010.359 1110.777 910.644 1060.532 91
3DMV0.484 1020.484 1080.538 1090.643 910.424 1000.606 1090.310 980.574 940.433 1000.378 960.796 1100.301 1010.214 740.537 750.208 1090.472 1010.507 1100.413 1070.693 1010.602 1090.539 87
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1030.577 990.611 1030.356 1130.321 1100.715 950.299 1020.376 1070.328 1090.319 1040.944 680.285 1030.164 940.216 1110.229 1060.484 980.545 1050.456 1000.755 940.709 980.475 101
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1040.679 830.604 1060.578 1000.380 1030.682 990.291 1030.106 1130.483 870.258 1110.920 950.258 1070.025 1110.231 1100.325 1020.480 990.560 1030.463 990.725 980.666 1040.231 113
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1050.474 1090.623 1010.463 1070.366 1050.651 1020.310 980.389 1060.349 1070.330 1030.937 790.271 1050.126 1020.285 1040.224 1070.350 1100.577 990.445 1030.625 1060.723 960.394 105
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 1060.548 1030.548 1080.597 980.363 1060.628 1070.300 1000.292 1080.374 1040.307 1050.881 1050.268 1060.186 860.238 1080.204 1100.407 1050.506 1110.449 1010.667 1040.620 1080.462 103
SurfaceConvPF0.442 1060.505 1050.622 1020.380 1120.342 1080.654 1010.227 1090.397 1050.367 1050.276 1080.924 920.240 1080.198 820.359 1000.262 1040.366 1060.581 980.435 1050.640 1050.668 1030.398 104
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1080.437 1110.646 970.474 1060.369 1040.645 1030.353 940.258 1100.282 1110.279 1070.918 970.298 1020.147 1010.283 1050.294 1030.487 970.562 1020.427 1060.619 1070.633 1070.352 108
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1090.525 1040.647 960.522 1020.324 1090.488 1130.077 1140.712 720.353 1060.401 940.636 1140.281 1040.176 890.340 1010.565 830.175 1140.551 1040.398 1080.370 1140.602 1090.361 107
SPLAT Netcopyleft0.393 1100.472 1100.511 1100.606 950.311 1110.656 1000.245 1070.405 1040.328 1090.197 1120.927 910.227 1100.000 1150.001 1150.249 1050.271 1130.510 1080.383 1100.593 1090.699 1000.267 111
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 1110.297 1130.491 1110.432 1100.358 1070.612 1080.274 1040.116 1120.411 1020.265 1090.904 1010.229 1090.079 1080.250 1060.185 1110.320 1110.510 1080.385 1090.548 1100.597 1120.394 105
PointNet++permissive0.339 1120.584 980.478 1120.458 1080.256 1130.360 1140.250 1050.247 1110.278 1120.261 1100.677 1130.183 1120.117 1030.212 1120.145 1130.364 1070.346 1140.232 1140.548 1100.523 1130.252 112
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 1130.353 1120.290 1140.278 1140.166 1140.553 1110.169 1120.286 1090.147 1140.148 1140.908 990.182 1130.064 1090.023 1140.018 1150.354 1090.363 1120.345 1120.546 1120.685 1010.278 110
ScanNetpermissive0.306 1140.203 1140.366 1130.501 1030.311 1110.524 1120.211 1110.002 1150.342 1080.189 1130.786 1110.145 1140.102 1040.245 1070.152 1120.318 1120.348 1130.300 1130.460 1130.437 1140.182 114
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 1150.000 1150.041 1150.172 1150.030 1150.062 1150.001 1150.035 1140.004 1150.051 1150.143 1150.019 1150.003 1140.041 1130.050 1140.003 1150.054 1150.018 1150.005 1150.264 1150.082 115


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Spherical Mask(CtoF)0.812 11.000 10.973 30.852 110.718 40.917 40.574 40.677 240.748 90.729 70.715 50.795 10.809 11.000 10.831 20.854 70.787 71.000 10.638 3
OneFormer3Dcopyleft0.801 21.000 10.973 20.909 50.698 100.928 20.582 30.668 290.685 140.780 20.687 90.698 130.702 111.000 10.794 70.900 20.784 90.986 460.635 4
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
UniPerception0.800 31.000 10.930 50.872 90.727 30.862 180.454 130.764 130.820 10.746 50.706 70.750 20.772 70.926 390.764 120.818 220.826 10.997 340.660 2
InsSSM0.799 41.000 10.915 70.710 350.729 20.925 30.664 10.670 270.770 50.766 30.739 20.737 40.700 121.000 10.792 80.829 160.815 30.997 340.625 7
TST3D0.795 51.000 10.929 60.918 40.709 70.884 130.596 20.704 210.769 60.734 60.644 150.699 120.751 91.000 10.794 60.876 40.757 160.997 340.550 26
ExtMask3D0.789 61.000 10.988 10.756 280.706 80.912 50.429 140.647 340.806 40.755 40.673 100.689 140.772 81.000 10.789 90.852 80.811 41.000 10.617 10
Queryformer0.787 71.000 10.933 40.601 440.754 10.886 110.558 60.661 310.767 70.665 130.716 40.639 190.808 31.000 10.844 10.897 30.804 51.000 10.624 8
MAFT0.786 81.000 10.894 120.807 170.694 120.893 80.486 90.674 250.740 100.786 10.704 80.727 60.739 101.000 10.707 180.849 100.756 171.000 10.685 1
Mask3D0.780 91.000 10.786 360.716 330.696 110.885 120.500 70.714 190.810 30.672 120.715 50.679 150.809 11.000 10.831 20.833 140.787 71.000 10.602 14
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 100.903 500.903 90.806 180.609 260.886 100.568 50.815 60.705 130.711 80.655 120.652 180.685 171.000 10.789 100.809 240.776 121.000 10.583 19
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 111.000 10.803 290.937 10.684 130.865 150.213 290.870 20.664 170.571 190.758 10.702 100.807 41.000 10.653 250.902 10.792 61.000 10.626 6
SoftGrouppermissive0.761 121.000 10.808 250.845 120.716 50.862 170.243 260.824 40.655 190.620 140.734 30.699 110.791 60.981 330.716 160.844 110.769 131.000 10.594 17
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
ISBNetpermissive0.757 131.000 10.904 80.731 310.678 140.895 60.458 110.644 360.670 160.710 90.620 200.732 50.650 201.000 10.756 130.778 270.779 101.000 10.614 11
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
SIM3D0.751 141.000 10.611 580.797 210.620 220.888 90.497 80.671 260.754 80.708 100.655 110.744 30.674 181.000 10.801 50.817 230.661 261.000 10.626 5
TD3Dpermissive0.751 141.000 10.774 370.867 100.621 210.934 10.404 150.706 200.812 20.605 170.633 180.626 200.690 161.000 10.640 270.820 190.777 111.000 10.612 12
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
PBNetpermissive0.747 161.000 10.818 210.837 140.713 60.844 200.457 120.647 340.711 120.614 150.617 220.657 170.650 201.000 10.692 190.822 180.765 151.000 10.595 16
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
GraphCut0.732 171.000 10.788 340.724 320.642 190.859 190.248 250.787 110.618 220.596 180.653 140.722 80.583 411.000 10.766 110.861 50.825 21.000 10.504 32
IPCA-Inst0.731 181.000 10.788 350.884 80.698 90.788 360.252 240.760 140.646 200.511 270.637 170.665 160.804 51.000 10.644 260.778 280.747 191.000 10.561 23
TopoSeg0.725 191.000 10.806 280.933 20.668 160.758 400.272 230.734 180.630 210.549 230.654 130.606 210.697 150.966 360.612 310.839 120.754 181.000 10.573 20
DKNet0.718 201.000 10.814 220.782 220.619 230.872 140.224 270.751 160.569 260.677 110.585 260.724 70.633 310.981 330.515 410.819 200.736 201.000 10.617 9
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 211.000 10.850 140.924 30.648 170.747 430.162 310.862 30.572 250.520 250.624 190.549 240.649 291.000 10.560 360.706 430.768 141.000 10.591 18
HAISpermissive0.699 221.000 10.849 150.820 150.675 150.808 300.279 210.757 150.465 320.517 260.596 240.559 230.600 351.000 10.654 240.767 300.676 240.994 420.560 24
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 231.000 10.697 530.888 70.556 330.803 310.387 160.626 380.417 370.556 220.585 270.702 90.600 351.000 10.824 40.720 420.692 221.000 10.509 31
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 241.000 10.799 310.811 160.622 200.817 250.376 170.805 90.590 240.487 310.568 300.525 280.650 200.835 490.600 320.829 150.655 281.000 10.526 28
DANCENET0.680 251.000 10.807 260.733 300.600 270.768 390.375 180.543 460.538 270.610 160.599 230.498 290.632 330.981 330.739 150.856 60.633 340.882 570.454 41
SphereSeg0.680 251.000 10.856 130.744 290.618 240.893 70.151 320.651 330.713 110.537 240.579 290.430 380.651 191.000 10.389 520.744 370.697 210.991 440.601 15
Box2Mask0.677 271.000 10.847 160.771 240.509 420.816 260.277 220.558 450.482 290.562 210.640 160.448 340.700 121.000 10.666 200.852 90.578 410.997 340.488 36
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 281.000 10.758 450.682 370.576 310.842 210.477 100.504 520.524 280.567 200.585 280.451 330.557 431.000 10.751 140.797 250.563 441.000 10.467 40
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 291.000 10.822 200.764 270.616 250.815 270.139 360.694 230.597 230.459 350.566 310.599 220.600 350.516 590.715 170.819 210.635 321.000 10.603 13
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 301.000 10.760 430.667 390.581 290.863 160.323 190.655 320.477 300.473 330.549 330.432 370.650 201.000 10.655 230.738 380.585 400.944 490.472 39
CSC-Pretrained0.648 311.000 10.810 230.768 250.523 400.813 280.143 350.819 50.389 400.422 440.511 370.443 350.650 201.000 10.624 290.732 390.634 331.000 10.375 48
PE0.645 321.000 10.773 390.798 200.538 350.786 370.088 440.799 100.350 440.435 420.547 340.545 250.646 300.933 380.562 350.761 330.556 490.997 340.501 34
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 331.000 10.758 440.582 500.539 340.826 240.046 490.765 120.372 420.436 410.588 250.539 270.650 201.000 10.577 330.750 350.653 300.997 340.495 35
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 341.000 10.841 170.893 60.531 370.802 320.115 410.588 430.448 340.438 390.537 360.430 390.550 440.857 410.534 390.764 320.657 270.987 450.568 21
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 351.000 10.895 110.800 190.480 460.676 480.144 340.737 170.354 430.447 360.400 500.365 450.700 121.000 10.569 340.836 130.599 361.000 10.473 38
PointGroup0.636 361.000 10.765 400.624 410.505 440.797 330.116 400.696 220.384 410.441 370.559 320.476 310.596 381.000 10.666 200.756 340.556 480.997 340.513 30
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+Group0.635 370.667 520.797 330.714 340.562 320.774 380.146 330.810 80.429 360.476 320.546 350.399 410.633 311.000 10.632 280.722 410.609 351.000 10.514 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
Mask3D_evaluation0.631 381.000 10.829 190.606 430.646 180.836 220.068 450.511 500.462 330.507 280.619 210.389 430.610 341.000 10.432 470.828 170.673 250.788 610.552 25
DENet0.629 391.000 10.797 320.608 420.589 280.627 520.219 280.882 10.310 460.402 490.383 520.396 420.650 201.000 10.663 220.543 600.691 231.000 10.568 22
3D-MPA0.611 401.000 10.833 180.765 260.526 390.756 410.136 380.588 430.470 310.438 400.432 460.358 470.650 200.857 410.429 480.765 310.557 471.000 10.430 43
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 411.000 10.801 300.599 450.535 360.728 450.286 200.436 560.679 150.491 290.433 440.256 490.404 560.857 410.620 300.724 400.510 541.000 10.539 27
AOIA0.601 421.000 10.761 420.687 360.485 450.828 230.008 560.663 300.405 390.405 480.425 470.490 300.596 380.714 520.553 380.779 260.597 370.992 430.424 45
PCJC0.578 431.000 10.810 240.583 490.449 490.813 290.042 500.603 410.341 450.490 300.465 410.410 400.650 200.835 490.264 580.694 470.561 450.889 540.504 33
SSEN0.575 441.000 10.761 410.473 520.477 470.795 340.066 460.529 480.658 180.460 340.461 420.380 440.331 580.859 400.401 510.692 490.653 291.000 10.348 50
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 450.528 620.708 520.626 400.580 300.745 440.063 470.627 370.240 500.400 500.497 380.464 320.515 451.000 10.475 430.745 360.571 421.000 10.429 44
NeuralBF0.555 460.667 520.896 100.843 130.517 410.751 420.029 510.519 490.414 380.439 380.465 400.000 680.484 470.857 410.287 560.693 480.651 311.000 10.485 37
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML0.549 471.000 10.807 270.588 480.327 540.647 500.004 580.815 70.180 530.418 450.364 540.182 520.445 501.000 10.442 460.688 500.571 431.000 10.396 46
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
ClickSeg_Instance0.539 481.000 10.621 560.300 550.530 380.698 460.127 390.533 470.222 510.430 430.400 490.365 450.574 420.938 370.472 440.659 520.543 500.944 490.347 51
One_Thing_One_Clickpermissive0.529 490.667 520.718 480.777 230.399 500.683 470.000 610.669 280.138 560.391 510.374 530.539 260.360 570.641 560.556 370.774 290.593 380.997 340.251 56
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN0.515 501.000 10.538 620.282 560.468 480.790 350.173 300.345 580.429 350.413 470.484 390.176 530.595 400.591 570.522 400.668 510.476 550.986 470.327 52
Occipital-SCS0.512 511.000 10.716 490.509 510.506 430.611 530.092 430.602 420.177 540.346 540.383 510.165 540.442 510.850 480.386 530.618 560.543 510.889 540.389 47
3D-BoNet0.488 521.000 10.672 550.590 470.301 560.484 630.098 420.620 390.306 470.341 550.259 580.125 560.434 530.796 510.402 500.499 620.513 530.909 530.439 42
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst0.478 530.667 520.712 510.595 460.259 590.550 590.000 610.613 400.175 550.250 600.434 430.437 360.411 550.857 410.485 420.591 590.267 650.944 490.359 49
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS0.470 540.667 520.685 540.677 380.372 520.562 570.000 610.482 530.244 490.316 570.298 550.052 630.442 520.857 410.267 570.702 440.559 461.000 10.287 54
SALoss-ResNet0.459 551.000 10.737 470.159 660.259 580.587 550.138 370.475 540.217 520.416 460.408 480.128 550.315 590.714 520.411 490.536 610.590 390.873 580.304 53
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.447 560.528 620.555 600.381 530.382 510.633 510.002 590.509 510.260 480.361 530.432 450.327 480.451 490.571 580.367 540.639 540.386 560.980 480.276 55
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 570.667 520.773 380.185 630.317 550.656 490.000 610.407 570.134 570.381 520.267 570.217 510.476 480.714 520.452 450.629 550.514 521.000 10.222 59
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 581.000 10.432 650.245 580.190 600.577 560.013 550.263 600.033 630.320 560.240 590.075 590.422 540.857 410.117 630.699 450.271 640.883 560.235 58
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 590.667 520.542 610.264 570.157 630.550 580.000 610.205 630.009 650.270 590.218 600.075 590.500 460.688 550.007 690.698 460.301 610.459 660.200 60
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 600.667 520.715 500.233 590.189 610.479 640.008 560.218 610.067 620.201 620.173 610.107 570.123 640.438 600.150 600.615 570.355 570.916 520.093 68
R-PointNet0.306 610.500 640.405 660.311 540.348 530.589 540.054 480.068 660.126 580.283 580.290 560.028 640.219 620.214 630.331 550.396 660.275 620.821 600.245 57
Region-18class0.284 620.250 680.751 460.228 610.270 570.521 600.000 610.468 550.008 670.205 610.127 620.000 680.068 660.070 670.262 590.652 530.323 590.740 620.173 61
SemRegionNet-20cls0.250 630.333 650.613 570.229 600.163 620.493 610.000 610.304 590.107 590.147 650.100 640.052 620.231 600.119 650.039 650.445 640.325 580.654 630.141 64
tmp0.248 640.667 520.437 640.188 620.153 640.491 620.000 610.208 620.094 610.153 640.099 650.057 610.217 630.119 650.039 650.466 630.302 600.640 640.140 65
3D-BEVIS0.248 640.667 520.566 590.076 670.035 690.394 670.027 530.035 680.098 600.099 670.030 680.025 650.098 650.375 620.126 620.604 580.181 670.854 590.171 62
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sem_Recon_ins0.227 660.764 510.486 630.069 680.098 660.426 660.017 540.067 670.015 640.172 630.100 630.096 580.054 680.183 640.135 610.366 670.260 660.614 650.168 63
ASIS0.199 670.333 650.253 680.167 650.140 650.438 650.000 610.177 640.008 660.121 660.069 660.004 670.231 610.429 610.036 670.445 650.273 630.333 680.119 67
Sgpn_scannet0.143 680.208 690.390 670.169 640.065 670.275 680.029 520.069 650.000 680.087 680.043 67