Presenting the ScanNet200 Benchmark

We present the ScanNet200 benchmark, which studies an order of magnitude more class categories than previous version of ScanNet. The scene geometry is shared within the two tasks, but the parsing of surface annotation allows for a larger vocabulary and more realistic setting for in the wild 3D understanding methods.

The ScanNet200 benchmark includes both finer-grained categories as well as a large number of previously unaddressed classes. This induces a much more challenging setting regarding the diversity of naturally observed semantic classes seen in the raw ScanNet RGB-D observations, where the data also reflects naturally encountered class imbalances. The difference in category frequencies between ScanNet and ScanNet200 can be seen in the Figure above.

ScanNet200 Benchmark

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




Method Infoavg iouhead ioucommon ioutail iouwallchairfloortabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
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PTv3 ScanNet2000.393 10.592 10.330 10.216 10.851 10.687 40.971 10.586 10.755 10.752 50.505 10.404 40.575 20.000 100.848 10.616 20.761 10.349 10.738 10.978 10.546 40.860 60.926 10.346 20.654 30.384 40.828 10.523 30.699 10.583 30.387 60.822 10.688 10.118 40.474 10.603 40.000 10.832 30.903 10.753 80.140 60.000 70.650 20.109 20.520 10.457 10.497 70.871 30.281 20.192 20.887 20.748 10.168 10.727 30.733 10.740 10.644 10.714 30.190 80.000 30.256 20.449 60.914 10.514 10.759 100.337 10.172 40.692 40.617 10.636 10.325 40.000 10.641 10.782 10.000 40.065 20.000 10.000 30.842 10.903 10.661 20.662 20.612 10.405 20.731 10.566 10.000 30.000 50.000 10.017 100.301 10.088 40.941 10.000 10.077 20.000 80.717 30.790 10.310 90.026 120.264 20.349 10.220 20.397 80.366 10.115 80.000 30.337 10.463 50.000 10.531 20.218 10.593 10.455 10.469 10.708 20.210 10.592 20.108 110.000 10.728 10.682 20.671 40.000 10.000 70.407 10.136 20.022 20.575 10.436 40.259 20.428 10.048 30.000 10.000 10.879 50.000 10.480 20.000 10.133 50.597 10.000 10.690 10.000 10.000 10.009 110.000 100.921 20.000 50.151 20.000 10.000 50.000 10.109 60.494 90.622 20.394 60.073 100.141 70.798 10.528 30.026 10.000 10.551 30.000 20.000 20.134 50.717 50.000 20.000 10.000 10.188 30.000 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)
BFANet ScanNet200permissive0.360 20.553 40.293 20.193 20.827 20.689 20.970 20.528 80.661 40.753 40.436 50.378 50.469 100.042 50.810 20.654 10.760 20.266 50.659 70.973 20.574 20.849 90.897 20.382 10.546 80.372 60.698 90.491 50.617 50.526 50.436 10.764 90.476 120.101 50.409 20.585 70.000 10.835 10.901 20.810 50.102 90.000 70.688 10.096 30.483 60.264 70.612 60.591 110.358 10.161 30.863 30.707 20.128 20.814 10.669 30.629 70.563 20.651 100.258 30.000 30.194 60.494 50.806 90.394 40.953 20.000 30.233 10.757 20.508 40.556 30.476 20.000 10.573 30.741 30.000 40.000 60.000 10.000 30.000 120.852 40.678 10.616 30.460 30.338 30.710 20.534 20.000 30.025 20.000 10.043 20.000 30.056 90.493 120.000 10.000 70.109 30.785 20.590 30.298 100.282 30.143 80.262 40.053 80.526 40.337 30.215 10.000 30.135 50.510 30.000 10.596 10.043 90.511 20.321 90.459 20.772 10.124 80.060 90.266 40.000 10.574 60.568 50.653 60.000 10.093 10.298 20.239 10.000 50.516 20.129 90.284 10.000 40.431 10.000 10.000 10.848 60.000 10.492 10.000 10.376 20.522 50.000 10.469 120.000 10.000 10.330 40.151 50.875 100.000 50.254 10.000 10.000 50.000 10.088 100.661 10.481 30.255 90.105 10.139 90.666 30.641 20.000 80.000 10.614 10.000 20.000 20.000 70.921 10.000 20.000 10.000 10.497 10.000 40.000 20.000 60.000 1
PonderV2 ScanNet2000.346 30.552 50.270 50.175 40.810 50.682 50.950 30.560 40.641 70.761 10.398 80.357 70.570 50.113 20.804 40.603 40.750 40.283 20.681 40.952 30.548 30.874 30.852 80.290 70.700 20.356 80.792 30.445 70.545 80.436 70.351 80.787 50.611 50.050 70.290 90.519 90.000 10.825 50.888 30.842 30.259 20.100 20.558 40.070 90.497 50.247 90.457 80.889 20.248 60.106 70.817 80.691 40.094 50.729 20.636 40.620 90.503 80.660 90.243 50.000 30.212 50.590 30.860 60.400 30.881 40.000 30.202 20.622 70.408 60.499 70.261 70.000 10.385 60.636 60.000 40.000 60.000 10.000 30.433 110.843 50.660 40.574 90.481 20.336 40.677 40.486 30.000 30.030 10.000 10.034 50.000 30.080 50.869 70.000 10.000 70.000 80.540 50.727 20.232 120.115 60.186 50.193 60.000 110.403 70.326 40.103 90.000 30.290 30.392 70.000 10.346 50.062 70.424 30.375 40.431 40.667 30.115 90.082 70.239 50.000 10.504 90.606 40.584 70.000 10.002 50.186 50.104 70.000 50.394 30.384 60.083 50.000 40.007 60.000 10.000 10.880 40.000 10.377 70.000 10.263 30.565 20.000 10.608 60.000 10.000 10.304 50.009 60.924 10.000 50.000 60.000 10.000 50.000 10.128 20.584 20.475 50.412 50.076 90.269 30.621 40.509 40.010 30.000 10.491 70.063 10.000 20.472 30.880 20.000 20.000 10.000 10.179 40.125 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.
OA-CNN-L_ScanNet2000.333 60.558 20.269 60.124 80.821 30.703 10.946 40.569 20.662 20.748 60.487 20.455 10.572 40.000 100.789 50.534 60.736 60.271 30.713 20.949 40.498 110.877 20.860 60.332 40.706 10.474 10.788 50.406 80.637 30.495 60.355 70.805 30.592 90.015 110.396 30.602 50.000 10.799 60.876 50.713 120.276 10.000 70.493 80.080 60.448 100.363 20.661 20.833 50.262 40.125 40.823 70.665 60.076 70.720 40.557 60.637 60.517 60.672 80.227 60.000 30.158 80.496 40.843 80.352 70.835 80.000 30.103 100.711 30.527 20.526 50.320 50.000 10.568 40.625 70.067 10.000 60.000 10.001 20.806 30.836 60.621 70.591 50.373 60.314 50.668 50.398 60.003 20.000 50.000 10.016 110.024 20.043 100.906 40.000 10.052 40.000 80.384 70.330 90.342 50.100 70.223 40.183 80.112 40.476 50.313 50.130 70.196 20.112 70.370 90.000 10.234 70.071 60.160 40.403 30.398 90.492 100.197 20.076 80.272 30.000 10.200 120.560 60.735 30.000 10.000 70.000 70.110 50.002 40.021 60.412 50.000 70.000 40.000 80.000 10.000 10.794 70.000 10.445 30.000 10.022 60.509 60.000 10.517 100.000 10.000 10.001 120.245 20.915 40.024 20.089 30.000 10.262 20.000 10.103 80.524 50.392 80.515 20.013 120.251 40.411 100.662 10.001 70.000 10.473 80.000 20.000 20.150 40.699 60.000 20.000 10.000 10.166 50.000 40.024 10.000 60.000 1
OctFormer ScanNet200permissive0.326 80.539 70.265 70.131 70.806 60.670 80.943 70.535 70.662 20.705 110.423 60.407 30.505 80.003 80.765 80.582 50.686 100.227 110.680 50.943 60.601 10.854 80.892 30.335 30.417 120.357 70.724 70.453 60.632 40.596 20.432 30.783 60.512 110.021 100.244 100.637 10.000 10.787 70.873 70.743 100.000 120.000 70.534 60.110 10.499 40.289 50.626 40.620 90.168 120.204 10.849 50.679 50.117 30.633 70.684 20.650 50.552 30.684 70.312 20.000 30.175 70.429 70.865 30.413 20.837 70.000 30.145 60.626 60.451 50.487 80.513 10.000 10.529 50.613 80.000 40.033 30.000 10.000 30.828 20.871 20.622 60.587 60.411 50.137 90.645 90.343 70.000 30.000 50.000 10.022 80.000 30.026 120.829 80.000 10.022 50.089 40.842 10.253 110.318 80.296 20.178 60.291 30.224 10.584 20.200 90.132 60.000 30.128 60.227 110.000 10.230 80.047 80.149 50.331 70.412 70.618 50.164 50.102 60.522 10.000 10.655 30.378 80.469 100.000 10.000 70.000 70.105 60.000 50.000 70.483 30.000 70.000 40.028 50.000 10.000 10.906 10.000 10.339 100.000 10.000 80.457 70.000 10.612 50.000 10.000 10.408 20.000 100.900 60.000 50.000 60.000 10.029 40.000 10.074 120.455 100.479 40.427 40.079 80.140 80.496 60.414 90.022 20.000 10.471 90.000 20.000 20.000 70.722 40.000 20.000 10.000 10.138 90.000 40.000 20.000 60.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-F.T.0.332 70.556 30.270 40.123 90.816 40.682 50.946 40.549 50.657 60.756 30.459 40.376 60.550 60.001 90.807 30.616 20.727 70.267 40.691 30.942 70.530 70.872 40.874 50.330 50.542 90.374 50.792 30.400 90.673 20.572 40.433 20.793 40.623 40.008 120.351 50.594 60.000 10.783 80.876 50.833 40.213 30.000 70.537 50.091 40.519 20.304 40.620 50.942 10.264 30.124 50.855 40.695 30.086 60.646 60.506 110.658 40.535 40.715 20.314 10.000 30.241 30.608 20.897 20.359 60.858 60.000 30.076 120.611 80.392 70.509 60.378 30.000 10.579 20.565 110.000 40.000 60.000 10.000 30.755 40.806 80.661 20.572 100.350 70.181 70.660 70.300 90.000 30.000 50.000 10.023 70.000 30.042 110.930 20.000 10.000 70.077 50.584 40.392 70.339 60.185 50.171 70.308 20.006 100.563 30.256 60.150 20.000 30.002 110.345 100.000 10.045 90.197 20.063 60.323 80.453 30.600 60.163 60.037 100.349 20.000 10.672 20.679 30.753 10.000 10.000 70.000 70.117 30.000 50.000 70.291 80.000 70.000 40.039 40.000 10.000 10.899 20.000 10.374 80.000 10.000 80.545 40.000 10.634 30.000 10.000 10.074 80.223 30.914 50.000 50.021 40.000 10.000 50.000 10.112 40.498 80.649 10.383 70.095 20.135 110.449 80.432 70.008 50.000 10.518 50.000 20.000 20.000 70.796 30.000 20.000 10.000 10.138 90.000 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-Pretrainpermissive0.249 120.455 120.171 110.079 120.766 120.659 100.930 120.494 90.542 120.700 120.314 120.215 120.430 120.121 10.697 120.441 110.683 110.235 90.609 120.895 110.476 120.816 110.770 120.186 90.634 40.216 120.734 60.340 110.471 110.307 110.293 120.591 120.542 100.076 60.205 110.464 100.000 10.484 120.832 120.766 70.052 110.000 70.413 110.059 110.418 110.222 110.318 120.609 100.206 100.112 60.743 90.625 90.076 70.579 110.548 80.590 110.371 110.552 120.081 110.003 20.142 90.201 120.638 120.233 110.686 120.000 30.142 70.444 120.375 80.247 120.198 90.000 10.128 120.454 120.019 20.097 10.000 10.000 30.553 90.557 110.373 80.545 110.164 110.014 120.547 110.174 100.000 30.002 30.000 10.037 30.000 30.063 80.664 110.000 10.000 70.130 20.170 90.152 120.335 70.079 90.110 100.175 90.098 60.175 120.166 100.045 120.207 10.014 90.465 40.000 10.001 120.001 120.046 70.299 100.327 110.537 80.033 110.012 120.186 80.000 10.205 110.377 90.463 110.000 10.058 30.000 70.055 100.041 10.000 70.105 110.000 70.000 40.000 80.000 10.000 10.398 100.000 10.308 120.000 10.000 80.319 100.000 10.543 90.000 10.000 10.062 100.004 80.862 110.000 50.000 60.000 10.000 50.000 10.123 30.316 110.225 100.250 100.094 30.180 50.332 110.441 60.000 80.000 10.310 120.000 20.000 20.000 70.592 90.000 20.000 10.000 10.203 20.000 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
CeCo0.340 40.551 60.247 80.181 30.784 80.661 90.939 80.564 30.624 80.721 70.484 30.429 20.575 20.027 60.774 70.503 90.753 30.242 80.656 80.945 50.534 50.865 50.860 60.177 120.616 50.400 20.818 20.579 10.615 60.367 90.408 50.726 100.633 20.162 10.360 40.619 20.000 10.828 40.873 70.924 20.109 80.083 30.564 30.057 120.475 80.266 60.781 10.767 60.257 50.100 80.825 60.663 70.048 110.620 90.551 70.595 100.532 50.692 60.246 40.000 30.213 40.615 10.861 50.376 50.900 30.000 30.102 110.660 50.321 100.547 40.226 80.000 10.311 80.742 20.011 30.006 50.000 10.000 30.546 100.824 70.345 90.665 10.450 40.435 10.683 30.411 50.338 10.000 50.000 10.030 60.000 30.068 60.892 50.000 10.063 30.000 80.257 80.304 100.387 30.079 90.228 30.190 70.000 110.586 10.347 20.133 50.000 30.037 80.377 80.000 10.384 40.006 110.003 80.421 20.410 80.643 40.171 40.121 40.142 90.000 10.510 80.447 70.474 90.000 10.000 70.286 30.083 80.000 50.000 70.603 10.096 40.063 30.000 80.000 10.000 10.898 30.000 10.429 40.000 10.400 10.550 30.000 10.633 40.000 10.000 10.377 30.000 100.916 30.000 50.000 60.000 10.000 50.000 10.102 90.499 70.296 90.463 30.089 50.304 10.740 20.401 110.010 30.000 10.560 20.000 20.000 20.709 10.652 70.000 20.000 10.000 10.143 70.000 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
Minkowski 34Dpermissive0.253 110.463 110.154 120.102 110.771 110.650 110.932 100.483 110.571 110.710 100.331 110.250 110.492 90.044 40.703 110.419 120.606 120.227 110.621 110.865 120.531 60.771 120.813 90.291 60.484 100.242 110.612 120.282 120.440 120.351 100.299 100.622 110.593 80.027 90.293 80.310 120.000 10.757 90.858 100.737 110.150 50.164 10.368 120.084 50.381 120.142 120.357 100.720 70.214 90.092 110.724 110.596 120.056 100.655 50.525 90.581 120.352 120.594 110.056 120.000 30.014 120.224 110.772 100.205 120.720 110.000 30.159 50.531 110.163 120.294 110.136 120.000 10.169 110.589 100.000 40.000 60.000 10.002 10.663 50.466 120.265 120.582 70.337 80.016 110.559 100.084 120.000 30.000 50.000 10.036 40.000 30.125 30.670 100.000 10.102 10.071 60.164 100.406 60.386 40.046 110.068 120.159 100.117 30.284 110.111 110.094 110.000 30.000 120.197 120.000 10.044 100.013 100.002 90.228 120.307 120.588 70.025 120.545 30.134 100.000 10.655 30.302 100.282 120.000 10.060 20.000 70.035 120.000 50.000 70.097 120.000 70.000 40.005 70.000 10.000 10.096 120.000 10.334 110.000 10.000 80.274 110.000 10.513 110.000 10.000 10.280 60.194 40.897 70.000 50.000 60.000 10.000 50.000 10.108 70.279 120.189 110.141 120.059 110.272 20.307 120.445 50.003 60.000 10.353 110.000 20.026 10.000 70.581 100.001 10.000 10.000 10.093 120.002 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 50.533 80.279 30.155 50.801 70.689 20.946 40.539 60.660 50.759 20.380 90.333 90.583 10.000 100.788 60.529 70.740 50.261 70.679 60.940 80.525 80.860 60.883 40.226 80.613 60.397 30.720 80.512 40.565 70.620 10.417 40.775 80.629 30.158 20.298 70.579 80.000 10.835 10.883 40.927 10.114 70.079 40.511 70.073 80.508 30.312 30.629 30.861 40.192 110.098 100.908 10.636 80.032 120.563 120.514 100.664 30.505 70.697 50.225 70.000 30.264 10.411 80.860 60.321 80.960 10.058 20.109 90.776 10.526 30.557 20.303 60.000 10.339 70.712 40.000 40.014 40.000 10.000 30.638 70.856 30.641 50.579 80.107 120.119 100.661 60.416 40.000 30.000 50.000 10.007 120.000 30.067 70.910 30.000 10.000 70.000 80.463 60.448 50.294 110.324 10.293 10.211 50.108 50.448 60.068 120.141 40.000 30.330 20.699 10.000 10.256 60.192 30.000 100.355 50.418 50.209 120.146 70.679 10.101 120.000 10.503 100.687 10.671 40.000 10.000 70.174 60.117 30.000 50.122 50.515 20.104 30.259 20.312 20.000 10.000 10.765 80.000 10.369 90.000 10.183 40.422 90.000 10.646 20.000 10.000 10.565 10.001 90.125 120.010 30.002 50.000 10.487 10.000 10.075 110.548 30.420 60.233 110.082 70.138 100.430 90.427 80.000 80.000 10.549 40.000 20.000 20.074 60.409 110.000 20.000 10.000 10.152 60.051 20.000 20.598 30.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
AWCS0.305 90.508 90.225 90.142 60.782 90.634 120.937 90.489 100.578 90.721 70.364 100.355 80.515 70.023 70.764 90.523 80.707 90.264 60.633 90.922 90.507 100.886 10.804 100.179 100.436 110.300 90.656 110.529 20.501 100.394 80.296 110.820 20.603 60.131 30.179 120.619 20.000 10.707 110.865 90.773 60.171 40.010 60.484 90.063 100.463 90.254 80.332 110.649 80.220 80.100 80.729 100.613 100.071 90.582 100.628 50.702 20.424 100.749 10.137 100.000 30.142 90.360 90.863 40.305 90.877 50.000 30.173 30.606 90.337 90.478 90.154 100.000 10.253 90.664 50.000 40.000 60.000 10.000 30.626 80.782 90.302 110.602 40.185 100.282 60.651 80.317 80.000 30.000 50.000 10.022 80.000 30.154 10.876 60.000 10.014 60.063 70.029 120.553 40.467 20.084 80.124 90.157 110.049 90.373 90.252 70.097 100.000 30.219 40.542 20.000 10.392 30.172 50.000 100.339 60.417 60.533 90.093 100.115 50.195 70.000 10.516 70.288 110.741 20.000 10.001 60.233 40.056 90.000 50.159 40.334 70.077 60.000 40.000 80.000 10.000 10.749 90.000 10.411 50.000 10.008 70.452 80.000 10.595 70.000 10.000 10.220 70.006 70.894 80.006 40.000 60.000 10.000 50.000 10.112 40.504 60.404 70.551 10.093 40.129 120.484 70.381 120.000 80.000 10.396 100.000 20.000 20.620 20.402 120.000 20.000 10.000 10.142 80.000 40.000 20.512 40.000 1
LGroundpermissive0.272 100.485 100.184 100.106 100.778 100.676 70.932 100.479 120.572 100.718 90.399 70.265 100.453 110.085 30.745 100.446 100.726 80.232 100.622 100.901 100.512 90.826 100.786 110.178 110.549 70.277 100.659 100.381 100.518 90.295 120.323 90.777 70.599 70.028 80.321 60.363 110.000 10.708 100.858 100.746 90.063 100.022 50.457 100.077 70.476 70.243 100.402 90.397 120.233 70.077 120.720 120.610 110.103 40.629 80.437 120.626 80.446 90.702 40.190 80.005 10.058 110.322 100.702 110.244 100.768 90.000 30.134 80.552 100.279 110.395 100.147 110.000 10.207 100.612 90.000 40.000 60.000 10.000 30.658 60.566 100.323 100.525 120.229 90.179 80.467 120.154 110.000 30.002 30.000 10.051 10.000 30.127 20.703 90.000 10.000 70.216 10.112 110.358 80.547 10.187 40.092 110.156 120.055 70.296 100.252 70.143 30.000 30.014 90.398 60.000 10.028 110.173 40.000 100.265 110.348 100.415 110.179 30.019 110.218 60.000 10.597 50.274 120.565 80.000 10.012 40.000 70.039 110.022 20.000 70.117 100.000 70.000 40.000 80.000 10.000 10.324 110.000 10.384 60.000 10.000 80.251 120.000 10.566 80.000 10.000 10.066 90.404 10.886 90.199 10.000 60.000 10.059 30.000 10.136 10.540 40.127 120.295 80.085 60.143 60.514 50.413 100.000 80.000 10.498 60.000 20.000 20.000 70.623 80.000 20.000 10.000 10.132 110.000 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 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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 by
TD3D Scannet200permissive0.211 20.332 20.177 20.103 20.662 10.413 20.463 20.705 10.192 30.145 10.266 20.215 10.452 40.209 20.222 50.219 50.315 20.893 10.380 20.617 20.439 20.047 40.646 10.080 20.610 30.253 10.237 20.293 20.135 10.379 50.494 20.048 10.252 20.451 20.184 20.483 10.395 20.852 10.083 20.551 20.278 20.036 20.337 20.266 20.544 10.963 10.079 50.039 10.740 20.604 20.000 20.586 10.283 20.282 20.059 20.633 30.028 20.004 20.559 20.309 20.420 20.028 51.000 10.000 10.456 10.411 10.372 10.060 40.046 40.000 20.040 40.694 10.083 20.000 20.000 10.000 20.000 30.083 40.252 20.260 50.200 10.160 10.669 20.111 20.000 20.000 10.006 20.169 20.000 10.007 10.296 20.032 10.074 10.139 30.000 20.321 20.031 10.108 20.088 20.157 10.000 10.231 50.026 50.000 20.000 10.356 20.052 20.000 10.240 10.147 10.000 10.015 20.046 30.144 30.073 30.414 10.222 40.000 10.806 10.343 30.486 30.000 10.008 10.038 20.083 10.002 10.028 20.074 20.032 20.150 20.039 20.008 10.000 10.250 40.000 10.125 40.000 10.052 20.260 30.000 10.143 50.000 10.000 10.543 20.207 20.404 10.000 10.003 20.000 10.000 20.000 10.037 20.093 40.272 20.342 10.039 40.281 20.249 30.224 10.000 20.000 10.074 10.000 10.000 10.000 20.278 20.000 10.000 20.889 10.323 10.000 20.014 10.000 20.000 1
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
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
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 30.813 190.851 70.782 60.890 20.597 10.916 20.696 80.713 30.979 10.635 10.384 20.793 20.907 80.821 40.790 310.696 110.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV20.785 20.978 10.800 270.833 230.788 40.853 160.545 160.910 50.713 10.705 40.979 10.596 70.390 10.769 120.832 410.821 40.792 300.730 10.975 10.897 40.785 4
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
Mix3Dpermissive0.781 30.964 20.855 10.843 160.781 70.858 120.575 60.831 330.685 140.714 20.979 10.594 80.310 260.801 10.892 160.841 20.819 40.723 40.940 130.887 60.725 24
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 40.861 200.818 140.836 200.790 30.875 40.576 50.905 60.704 50.739 10.969 100.611 20.349 100.756 220.958 10.702 450.805 140.708 70.916 320.898 30.801 2
TTT-KD0.773 50.646 910.818 140.809 350.774 90.878 30.581 20.943 10.687 120.704 50.978 40.607 50.336 150.775 80.912 60.838 30.823 20.694 120.967 30.899 20.794 3
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
ResLFE_HDS0.772 60.939 40.824 60.854 60.771 100.840 300.564 100.900 80.686 130.677 120.961 160.537 310.348 110.769 120.903 100.785 100.815 60.676 220.939 140.880 110.772 8
OctFormerpermissive0.766 70.925 70.808 230.849 90.786 50.846 260.566 90.876 150.690 100.674 140.960 170.576 170.226 670.753 240.904 90.777 120.815 60.722 50.923 270.877 130.776 7
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint0.766 70.932 50.794 330.829 250.751 220.854 140.540 200.903 70.630 340.672 150.963 140.565 210.357 80.788 30.900 120.737 250.802 150.685 170.950 70.887 60.780 5
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OccuSeg+Semantic0.764 90.758 580.796 310.839 180.746 250.907 10.562 110.850 250.680 160.672 150.978 40.610 30.335 170.777 60.819 450.847 10.830 10.691 140.972 20.885 80.727 22
CU-Hybrid Net0.764 90.924 80.819 120.840 170.757 170.853 160.580 30.848 260.709 30.643 240.958 210.587 120.295 330.753 240.884 200.758 190.815 60.725 30.927 240.867 220.743 15
O-CNNpermissive0.762 110.924 80.823 70.844 150.770 110.852 180.577 40.847 280.711 20.640 280.958 210.592 90.217 730.762 170.888 170.758 190.813 100.726 20.932 220.868 210.744 14
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DTC0.757 120.843 260.820 100.847 120.791 20.862 100.511 330.870 170.707 40.652 200.954 350.604 60.279 440.760 180.942 20.734 260.766 440.701 100.884 540.874 190.736 16
OA-CNN-L_ScanNet200.756 130.783 440.826 50.858 40.776 80.837 330.548 150.896 110.649 260.675 130.962 150.586 130.335 170.771 110.802 490.770 150.787 330.691 140.936 170.880 110.761 10
PNE0.755 140.786 420.835 40.834 220.758 150.849 210.570 80.836 320.648 270.668 170.978 40.581 160.367 60.683 350.856 290.804 60.801 190.678 190.961 50.889 50.716 29
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer0.755 140.927 60.822 80.836 200.801 10.849 210.516 300.864 220.651 250.680 110.958 210.584 150.282 410.759 200.855 310.728 280.802 150.678 190.880 590.873 200.756 12
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
DMF-Net0.752 160.906 120.793 350.802 410.689 400.825 460.556 120.867 180.681 150.602 440.960 170.555 270.365 70.779 50.859 260.747 220.795 270.717 60.917 310.856 300.764 9
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointTransformerV20.752 160.742 660.809 220.872 10.758 150.860 110.552 130.891 130.610 410.687 60.960 170.559 250.304 290.766 150.926 40.767 160.797 230.644 330.942 110.876 160.722 26
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 180.793 400.790 360.807 370.750 240.856 130.524 260.881 140.588 530.642 270.977 80.591 100.274 470.781 40.929 30.804 60.796 240.642 340.947 90.885 80.715 30
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 180.909 100.818 140.811 330.752 200.839 320.485 470.842 290.673 180.644 230.957 250.528 370.305 280.773 90.859 260.788 80.818 50.693 130.916 320.856 300.723 25
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 200.623 940.804 250.859 30.745 260.824 480.501 370.912 40.690 100.685 80.956 260.567 200.320 230.768 140.918 50.720 330.802 150.676 220.921 290.881 100.779 6
StratifiedFormerpermissive0.747 210.901 130.803 260.845 140.757 170.846 260.512 320.825 360.696 80.645 220.956 260.576 170.262 580.744 290.861 250.742 230.770 420.705 80.899 440.860 270.734 17
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNetpermissive0.746 220.870 180.838 20.858 40.729 310.850 200.501 370.874 160.587 540.658 190.956 260.564 220.299 310.765 160.900 120.716 360.812 110.631 390.939 140.858 280.709 31
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion0.746 220.771 520.819 120.848 110.702 370.865 90.397 850.899 90.699 60.664 180.948 550.588 110.330 190.746 280.851 350.764 170.796 240.704 90.935 180.866 230.728 20
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
DiffSeg3D20.745 240.725 750.814 180.837 190.751 220.831 400.514 310.896 110.674 170.684 90.960 170.564 220.303 300.773 90.820 440.713 390.798 220.690 160.923 270.875 170.757 11
Retro-FPN0.744 250.842 270.800 270.767 550.740 270.836 350.541 180.914 30.672 190.626 320.958 210.552 280.272 490.777 60.886 190.696 460.801 190.674 250.941 120.858 280.717 27
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
EQ-Net0.743 260.620 950.799 300.849 90.730 300.822 500.493 440.897 100.664 200.681 100.955 290.562 240.378 30.760 180.903 100.738 240.801 190.673 260.907 360.877 130.745 13
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 270.816 350.806 240.807 370.752 200.828 440.575 60.839 310.699 60.637 290.954 350.520 400.320 230.755 230.834 390.760 180.772 390.676 220.915 340.862 250.717 27
SAT0.742 270.860 210.765 490.819 280.769 120.848 230.533 220.829 340.663 210.631 310.955 290.586 130.274 470.753 240.896 140.729 270.760 500.666 280.921 290.855 320.733 18
LargeKernel3D0.739 290.909 100.820 100.806 390.740 270.852 180.545 160.826 350.594 520.643 240.955 290.541 300.263 570.723 330.858 280.775 140.767 430.678 190.933 200.848 370.694 36
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 300.776 480.790 360.851 70.754 190.854 140.491 460.866 200.596 510.686 70.955 290.536 320.342 130.624 500.869 220.787 90.802 150.628 400.927 240.875 170.704 33
MinkowskiNetpermissive0.736 300.859 220.818 140.832 240.709 350.840 300.521 280.853 240.660 230.643 240.951 450.544 290.286 390.731 310.893 150.675 550.772 390.683 180.874 660.852 350.727 22
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 320.890 140.837 30.864 20.726 320.873 50.530 250.824 370.489 870.647 210.978 40.609 40.336 150.624 500.733 580.758 190.776 370.570 650.949 80.877 130.728 20
PointTransformer++0.725 330.727 740.811 210.819 280.765 130.841 290.502 360.814 420.621 370.623 340.955 290.556 260.284 400.620 520.866 230.781 110.757 540.648 310.932 220.862 250.709 31
SparseConvNet0.725 330.647 900.821 90.846 130.721 330.869 60.533 220.754 580.603 470.614 360.955 290.572 190.325 210.710 340.870 210.724 310.823 20.628 400.934 190.865 240.683 39
MatchingNet0.724 350.812 370.812 200.810 340.735 290.834 370.495 430.860 230.572 610.602 440.954 350.512 420.280 430.757 210.845 370.725 300.780 350.606 500.937 160.851 360.700 35
INS-Conv-semantic0.717 360.751 610.759 520.812 320.704 360.868 70.537 210.842 290.609 430.608 400.953 390.534 340.293 340.616 530.864 240.719 350.793 280.640 350.933 200.845 410.663 45
PointMetaBase0.714 370.835 280.785 380.821 260.684 420.846 260.531 240.865 210.614 380.596 480.953 390.500 450.246 630.674 360.888 170.692 470.764 460.624 420.849 810.844 420.675 41
contrastBoundarypermissive0.705 380.769 550.775 430.809 350.687 410.820 530.439 730.812 430.661 220.591 500.945 630.515 410.171 910.633 470.856 290.720 330.796 240.668 270.889 510.847 380.689 37
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 390.774 500.800 270.793 460.760 140.847 250.471 510.802 460.463 940.634 300.968 120.491 480.271 510.726 320.910 70.706 410.815 60.551 770.878 600.833 430.570 77
RFCR0.702 400.889 150.745 630.813 310.672 450.818 570.493 440.815 410.623 350.610 380.947 570.470 570.249 620.594 560.848 360.705 420.779 360.646 320.892 490.823 490.611 60
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click0.701 410.825 320.796 310.723 620.716 340.832 390.433 750.816 390.634 320.609 390.969 100.418 830.344 120.559 680.833 400.715 370.808 130.560 710.902 410.847 380.680 40
JSENetpermissive0.699 420.881 170.762 500.821 260.667 460.800 690.522 270.792 490.613 390.607 410.935 830.492 470.205 780.576 610.853 330.691 490.758 520.652 300.872 690.828 460.649 49
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
One-Thing-One-Click0.693 430.743 650.794 330.655 850.684 420.822 500.497 420.719 680.622 360.617 350.977 80.447 700.339 140.750 270.664 740.703 440.790 310.596 550.946 100.855 320.647 50
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 440.732 700.772 440.786 470.677 440.866 80.517 290.848 260.509 800.626 320.952 430.536 320.225 690.545 740.704 650.689 520.810 120.564 700.903 400.854 340.729 19
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 450.884 160.754 560.795 440.647 530.818 570.422 770.802 460.612 400.604 420.945 630.462 600.189 860.563 670.853 330.726 290.765 450.632 380.904 380.821 520.606 64
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 460.704 800.741 670.754 590.656 480.829 420.501 370.741 630.609 430.548 580.950 490.522 390.371 40.633 470.756 530.715 370.771 410.623 430.861 770.814 550.658 46
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 470.866 190.748 600.819 280.645 550.794 720.450 630.802 460.587 540.604 420.945 630.464 590.201 810.554 700.840 380.723 320.732 640.602 530.907 360.822 510.603 67
KP-FCNN0.684 480.847 250.758 540.784 490.647 530.814 600.473 500.772 520.605 450.594 490.935 830.450 680.181 890.587 570.805 480.690 500.785 340.614 460.882 560.819 530.632 56
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 480.728 730.757 550.776 520.690 380.804 670.464 560.816 390.577 600.587 510.945 630.508 440.276 460.671 370.710 630.663 600.750 580.589 600.881 570.832 450.653 48
DGNet0.684 480.712 790.784 390.782 510.658 470.835 360.499 410.823 380.641 290.597 470.950 490.487 500.281 420.575 620.619 780.647 680.764 460.620 450.871 720.846 400.688 38
PointContrast_LA_SEM0.683 510.757 590.784 390.786 470.639 570.824 480.408 800.775 510.604 460.541 600.934 870.532 350.269 530.552 710.777 510.645 710.793 280.640 350.913 350.824 480.671 42
Superpoint Network0.683 510.851 240.728 710.800 430.653 500.806 650.468 530.804 440.572 610.602 440.946 600.453 670.239 660.519 790.822 420.689 520.762 490.595 570.895 470.827 470.630 57
VI-PointConv0.676 530.770 540.754 560.783 500.621 610.814 600.552 130.758 560.571 630.557 560.954 350.529 360.268 550.530 770.682 690.675 550.719 670.603 520.888 520.833 430.665 44
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 540.789 410.748 600.763 570.635 590.814 600.407 820.747 600.581 580.573 530.950 490.484 510.271 510.607 540.754 540.649 650.774 380.596 550.883 550.823 490.606 64
SALANet0.670 550.816 350.770 470.768 540.652 510.807 640.451 600.747 600.659 240.545 590.924 930.473 560.149 1010.571 640.811 470.635 740.746 590.623 430.892 490.794 680.570 77
O3DSeg0.668 560.822 330.771 460.496 1050.651 520.833 380.541 180.761 550.555 690.611 370.966 130.489 490.370 50.388 990.580 810.776 130.751 560.570 650.956 60.817 540.646 51
PointConvpermissive0.666 570.781 450.759 520.699 700.644 560.822 500.475 490.779 500.564 660.504 760.953 390.428 770.203 800.586 590.754 540.661 610.753 550.588 610.902 410.813 570.642 52
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 570.703 810.781 410.751 610.655 490.830 410.471 510.769 530.474 900.537 620.951 450.475 550.279 440.635 450.698 680.675 550.751 560.553 760.816 880.806 590.703 34
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 590.746 630.708 740.722 630.638 580.820 530.451 600.566 960.599 490.541 600.950 490.510 430.313 250.648 420.819 450.616 790.682 820.590 590.869 730.810 580.656 47
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
MVF-GNN0.658 600.558 1020.751 580.655 850.690 380.722 940.453 590.867 180.579 590.576 520.893 1050.523 380.293 340.733 300.571 830.692 470.659 890.606 500.875 630.804 610.668 43
DCM-Net0.658 600.778 460.702 770.806 390.619 620.813 630.468 530.693 760.494 830.524 680.941 750.449 690.298 320.510 810.821 430.675 550.727 660.568 680.826 860.803 620.637 54
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 620.698 830.743 650.650 870.564 790.820 530.505 350.758 560.631 330.479 800.945 630.480 530.226 670.572 630.774 520.690 500.735 620.614 460.853 800.776 830.597 70
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 630.752 600.734 690.664 830.583 740.815 590.399 840.754 580.639 300.535 640.942 730.470 570.309 270.665 380.539 850.650 640.708 720.635 370.857 790.793 700.642 52
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 640.778 460.731 700.699 700.577 750.829 420.446 650.736 640.477 890.523 700.945 630.454 640.269 530.484 890.749 570.618 770.738 600.599 540.827 850.792 730.621 59
PointConv-SFPN0.641 650.776 480.703 760.721 640.557 820.826 450.451 600.672 810.563 670.483 790.943 720.425 800.162 960.644 430.726 590.659 620.709 710.572 640.875 630.786 780.559 83
MVPNetpermissive0.641 650.831 290.715 720.671 800.590 700.781 780.394 860.679 780.642 280.553 570.937 800.462 600.256 590.649 410.406 990.626 750.691 790.666 280.877 610.792 730.608 63
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 670.717 780.701 780.692 730.576 760.801 680.467 550.716 690.563 670.459 860.953 390.429 760.169 930.581 600.854 320.605 800.710 690.550 780.894 480.793 700.575 75
FPConvpermissive0.639 680.785 430.760 510.713 680.603 650.798 700.392 870.534 1010.603 470.524 680.948 550.457 620.250 610.538 750.723 610.598 840.696 770.614 460.872 690.799 630.567 80
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 690.797 390.769 480.641 930.590 700.820 530.461 570.537 1000.637 310.536 630.947 570.388 900.206 770.656 390.668 720.647 680.732 640.585 620.868 740.793 700.473 103
PointSPNet0.637 700.734 690.692 850.714 670.576 760.797 710.446 650.743 620.598 500.437 910.942 730.403 860.150 1000.626 490.800 500.649 650.697 760.557 740.846 820.777 820.563 81
SConv0.636 710.830 300.697 810.752 600.572 780.780 800.445 670.716 690.529 730.530 650.951 450.446 710.170 920.507 840.666 730.636 730.682 820.541 840.886 530.799 630.594 71
Supervoxel-CNN0.635 720.656 880.711 730.719 650.613 630.757 890.444 700.765 540.534 720.566 540.928 910.478 540.272 490.636 440.531 870.664 590.645 930.508 910.864 760.792 730.611 60
joint point-basedpermissive0.634 730.614 960.778 420.667 820.633 600.825 460.420 780.804 440.467 920.561 550.951 450.494 460.291 360.566 650.458 940.579 900.764 460.559 730.838 830.814 550.598 69
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 740.731 710.688 880.675 770.591 690.784 770.444 700.565 970.610 410.492 770.949 530.456 630.254 600.587 570.706 640.599 830.665 880.612 490.868 740.791 760.579 74
3DSM_DMMF0.631 750.626 930.745 630.801 420.607 640.751 900.506 340.729 670.565 650.491 780.866 1080.434 720.197 840.595 550.630 770.709 400.705 740.560 710.875 630.740 930.491 98
PointNet2-SFPN0.631 750.771 520.692 850.672 780.524 870.837 330.440 720.706 740.538 710.446 880.944 690.421 820.219 720.552 710.751 560.591 860.737 610.543 830.901 430.768 850.557 84
APCF-Net0.631 750.742 660.687 900.672 780.557 820.792 750.408 800.665 820.545 700.508 730.952 430.428 770.186 870.634 460.702 660.620 760.706 730.555 750.873 670.798 650.581 73
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 780.604 980.741 670.766 560.590 700.747 910.501 370.734 650.503 820.527 660.919 970.454 640.323 220.550 730.420 980.678 540.688 800.544 810.896 460.795 670.627 58
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 790.800 380.625 1010.719 650.545 840.806 650.445 670.597 900.448 970.519 710.938 790.481 520.328 200.489 880.499 920.657 630.759 510.592 580.881 570.797 660.634 55
SegGroup_sempermissive0.627 800.818 340.747 620.701 690.602 660.764 860.385 910.629 870.490 850.508 730.931 900.409 850.201 810.564 660.725 600.618 770.692 780.539 850.873 670.794 680.548 87
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 810.830 300.694 830.757 580.563 800.772 840.448 640.647 850.520 760.509 720.949 530.431 750.191 850.496 860.614 790.647 680.672 860.535 870.876 620.783 790.571 76
dtc_net0.625 810.703 810.751 580.794 450.535 850.848 230.480 480.676 800.528 740.469 830.944 690.454 640.004 1140.464 910.636 760.704 430.758 520.548 800.924 260.787 770.492 97
HPEIN0.618 830.729 720.668 910.647 890.597 680.766 850.414 790.680 770.520 760.525 670.946 600.432 730.215 740.493 870.599 800.638 720.617 980.570 650.897 450.806 590.605 66
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCNpermissive0.610 840.858 230.772 440.489 1060.532 860.792 750.404 830.643 860.570 640.507 750.935 830.414 840.046 1110.510 810.702 660.602 820.705 740.549 790.859 780.773 840.534 90
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 850.760 570.667 920.649 880.521 880.793 730.457 580.648 840.528 740.434 930.947 570.401 870.153 990.454 920.721 620.648 670.717 680.536 860.904 380.765 860.485 99
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 860.634 920.743 650.697 720.601 670.781 780.437 740.585 930.493 840.446 880.933 880.394 880.011 1130.654 400.661 750.603 810.733 630.526 880.832 840.761 880.480 100
LAP-D0.594 870.720 760.692 850.637 940.456 980.773 830.391 890.730 660.587 540.445 900.940 770.381 910.288 370.434 950.453 960.591 860.649 910.581 630.777 920.749 920.610 62
DPC0.592 880.720 760.700 790.602 980.480 940.762 880.380 920.713 720.585 570.437 910.940 770.369 930.288 370.434 950.509 910.590 880.639 960.567 690.772 940.755 900.592 72
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 890.766 560.659 960.683 750.470 970.740 930.387 900.620 890.490 850.476 810.922 950.355 960.245 640.511 800.511 900.571 910.643 940.493 950.872 690.762 870.600 68
ROSMRF0.580 900.772 510.707 750.681 760.563 800.764 860.362 940.515 1020.465 930.465 850.936 820.427 790.207 760.438 930.577 820.536 940.675 850.486 960.723 1000.779 800.524 93
SD-DETR0.576 910.746 630.609 1050.445 1100.517 890.643 1050.366 930.714 710.456 950.468 840.870 1070.432 730.264 560.558 690.674 700.586 890.688 800.482 970.739 980.733 950.537 89
SQN_0.1%0.569 920.676 850.696 820.657 840.497 900.779 810.424 760.548 980.515 780.376 980.902 1040.422 810.357 80.379 1000.456 950.596 850.659 890.544 810.685 1030.665 1060.556 85
TextureNetpermissive0.566 930.672 870.664 930.671 800.494 920.719 950.445 670.678 790.411 1030.396 960.935 830.356 950.225 690.412 970.535 860.565 920.636 970.464 990.794 910.680 1030.568 79
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet0.562 940.648 890.700 790.770 530.586 730.687 990.333 980.650 830.514 790.475 820.906 1010.359 940.223 710.340 1020.442 970.422 1050.668 870.501 920.708 1010.779 800.534 90
Pointnet++ & Featurepermissive0.557 950.735 680.661 950.686 740.491 930.744 920.392 870.539 990.451 960.375 990.946 600.376 920.205 780.403 980.356 1020.553 930.643 940.497 930.824 870.756 890.515 94
GMLPs0.538 960.495 1070.693 840.647 890.471 960.793 730.300 1010.477 1030.505 810.358 1010.903 1030.327 990.081 1080.472 900.529 880.448 1030.710 690.509 890.746 960.737 940.554 86
PanopticFusion-label0.529 970.491 1080.688 880.604 970.386 1030.632 1060.225 1110.705 750.434 1000.293 1070.815 1090.348 970.241 650.499 850.669 710.507 960.649 910.442 1050.796 900.602 1100.561 82
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 980.676 850.591 1080.609 950.442 990.774 820.335 970.597 900.422 1020.357 1020.932 890.341 980.094 1070.298 1040.528 890.473 1010.676 840.495 940.602 1090.721 980.349 110
Online SegFusion0.515 990.607 970.644 990.579 1000.434 1000.630 1070.353 950.628 880.440 980.410 940.762 1130.307 1010.167 940.520 780.403 1000.516 950.565 1010.447 1030.678 1040.701 1000.514 95
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF0.501 1000.558 1020.608 1060.424 1120.478 950.690 980.246 1070.586 920.468 910.450 870.911 990.394 880.160 970.438 930.212 1090.432 1040.541 1070.475 980.742 970.727 960.477 101
PCNN0.498 1010.559 1010.644 990.560 1020.420 1020.711 970.229 1090.414 1040.436 990.352 1030.941 750.324 1000.155 980.238 1090.387 1010.493 970.529 1080.509 890.813 890.751 910.504 96
Weakly-Openseg v30.489 1020.749 620.664 930.646 910.496 910.559 1110.122 1140.577 940.257 1140.364 1000.805 1100.198 1120.096 1060.510 810.496 930.361 1090.563 1020.359 1120.777 920.644 1070.532 92
3DMV0.484 1030.484 1090.538 1100.643 920.424 1010.606 1100.310 990.574 950.433 1010.378 970.796 1110.301 1020.214 750.537 760.208 1100.472 1020.507 1110.413 1080.693 1020.602 1100.539 88
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1040.577 1000.611 1040.356 1140.321 1110.715 960.299 1030.376 1080.328 1100.319 1050.944 690.285 1040.164 950.216 1120.229 1070.484 990.545 1060.456 1010.755 950.709 990.475 102
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1050.679 840.604 1070.578 1010.380 1040.682 1000.291 1040.106 1140.483 880.258 1120.920 960.258 1080.025 1120.231 1110.325 1030.480 1000.560 1040.463 1000.725 990.666 1050.231 114
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1060.474 1100.623 1020.463 1080.366 1060.651 1030.310 990.389 1070.349 1080.330 1040.937 800.271 1060.126 1030.285 1050.224 1080.350 1110.577 1000.445 1040.625 1070.723 970.394 106
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET20.442 1070.548 1040.548 1090.597 990.363 1070.628 1080.300 1010.292 1090.374 1050.307 1060.881 1060.268 1070.186 870.238 1090.204 1110.407 1060.506 1120.449 1020.667 1050.620 1090.462 104
SurfaceConvPF0.442 1070.505 1060.622 1030.380 1130.342 1090.654 1020.227 1100.397 1060.367 1060.276 1090.924 930.240 1090.198 830.359 1010.262 1050.366 1070.581 990.435 1060.640 1060.668 1040.398 105
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1090.437 1120.646 980.474 1070.369 1050.645 1040.353 950.258 1110.282 1120.279 1080.918 980.298 1030.147 1020.283 1060.294 1040.487 980.562 1030.427 1070.619 1080.633 1080.352 109
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1100.525 1050.647 970.522 1030.324 1100.488 1140.077 1150.712 730.353 1070.401 950.636 1150.281 1050.176 900.340 1020.565 840.175 1150.551 1050.398 1090.370 1150.602 1100.361 108
SPLAT Netcopyleft0.393 1110.472 1110.511 1110.606 960.311 1120.656 1010.245 1080.405 1050.328 1100.197 1130.927 920.227 1110.000 1160.001 1160.249 1060.271 1140.510 1090.383 1110.593 1100.699 1010.267 112
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 1120.297 1140.491 1120.432 1110.358 1080.612 1090.274 1050.116 1130.411 1030.265 1100.904 1020.229 1100.079 1090.250 1070.185 1120.320 1120.510 1090.385 1100.548 1110.597 1130.394 106
PointNet++permissive0.339 1130.584 990.478 1130.458 1090.256 1140.360 1150.250 1060.247 1120.278 1130.261 1110.677 1140.183 1130.117 1040.212 1130.145 1140.364 1080.346 1150.232 1150.548 1110.523 1140.252 113
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 1140.353 1130.290 1150.278 1150.166 1150.553 1120.169 1130.286 1100.147 1150.148 1150.908 1000.182 1140.064 1100.023 1150.018 1160.354 1100.363 1130.345 1130.546 1130.685 1020.278 111
ScanNetpermissive0.306 1150.203 1150.366 1140.501 1040.311 1120.524 1130.211 1120.002 1160.342 1090.189 1140.786 1120.145 1150.102 1050.245 1080.152 1130.318 1130.348 1140.300 1140.460 1140.437 1150.182 115
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 1160.000 1160.041 1160.172 1160.030 1160.062 1160.001 1160.035 1150.004 1160.051 1160.143 1160.019 1160.003 1150.041 1140.050 1150.003 1160.054 1160.018 1160.005 1160.264 1160.082 116


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




Method Infoavg 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 50.654 90.555 40.434 80.769 50.271 80.604 80.447 40.505 50.549 10.698 20.716 20.775 110.480 60.747 40.575 70.925 90.436 4
EV3D0.615 20.946 50.652 100.555 40.433 90.773 20.271 90.604 80.447 40.506 40.544 40.698 20.716 20.775 110.480 60.747 40.572 90.925 90.435 5
SIM3D0.614 30.952 40.654 80.539 70.422 120.768 70.302 50.688 20.419 70.476 110.513 110.703 10.717 10.743 200.460 140.770 10.565 110.914 130.446 2
ExtMask3D0.598 40.852 140.692 40.433 260.461 50.791 10.264 100.488 310.493 10.508 30.528 100.594 90.706 50.791 60.483 40.734 80.595 20.911 150.437 3
MAFT0.596 50.889 120.721 10.448 190.460 60.768 60.251 110.558 190.408 80.504 60.539 60.616 70.618 90.858 30.482 50.684 160.551 130.931 80.450 1
UniPerception0.588 60.963 30.667 60.493 110.472 40.750 100.229 140.528 240.468 30.498 80.542 50.643 50.530 180.661 330.463 110.695 150.599 10.972 10.420 6
MG-Former0.587 70.852 140.639 120.454 180.393 160.758 90.338 20.572 150.480 20.527 20.491 170.671 40.527 190.867 10.485 30.601 260.590 50.938 70.390 10
InsSSM0.586 81.000 10.593 160.440 220.480 20.771 30.345 10.437 350.444 60.495 90.548 30.579 120.621 80.720 240.409 180.712 100.593 30.960 30.395 8
Queryformer0.583 90.926 80.702 20.393 320.504 10.733 160.276 70.527 250.373 130.479 100.534 80.533 180.697 60.720 250.436 160.745 60.592 40.958 40.363 17
PBNetpermissive0.573 100.926 80.575 210.619 10.472 30.736 140.239 130.487 320.383 120.459 140.506 140.533 170.585 110.767 130.404 190.717 90.559 120.969 20.381 13
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
TST3D0.569 110.778 220.675 50.598 20.451 70.727 170.280 60.476 340.395 90.472 120.457 230.583 100.580 130.777 80.462 130.735 70.547 150.919 120.333 23
Mask3D0.566 120.926 80.597 150.408 290.420 130.737 130.239 120.598 110.386 110.458 150.549 10.568 150.716 20.601 390.480 60.646 200.575 70.922 110.364 16
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
OneFormer3Dcopyleft0.566 120.781 210.697 30.562 30.431 100.770 40.331 30.400 410.373 140.529 10.504 150.568 140.475 240.732 220.470 90.762 20.550 140.871 300.379 14
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
ISBNetpermissive0.559 140.939 70.655 70.383 350.426 110.763 80.180 160.534 230.386 100.499 70.509 130.621 60.427 340.704 280.467 100.649 190.571 100.948 50.401 7
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
GraphCut0.552 151.000 10.611 140.438 230.392 170.714 180.139 190.598 120.327 170.389 180.510 120.598 80.427 350.754 160.463 120.761 30.588 60.903 180.329 24
SPFormerpermissive0.549 160.745 250.640 110.484 120.395 150.739 120.311 40.566 170.335 160.468 130.492 160.555 160.478 230.747 180.436 150.712 110.540 160.893 220.343 22
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet0.532 170.815 180.624 130.517 80.377 190.749 110.107 210.509 280.304 190.437 160.475 180.581 110.539 160.775 100.339 240.640 220.506 190.901 190.385 12
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst0.520 180.889 120.551 250.548 60.418 140.665 280.064 300.585 130.260 270.277 320.471 200.500 190.644 70.785 70.369 200.591 290.511 170.878 270.362 18
SoftGroup++0.513 190.704 310.578 200.398 310.363 250.704 190.061 310.647 50.297 240.378 210.537 70.343 220.614 100.828 50.295 290.710 130.505 210.875 290.394 9
SSTNetpermissive0.506 200.738 280.549 260.497 100.316 300.693 220.178 170.377 440.198 330.330 230.463 220.576 130.515 200.857 40.494 10.637 230.457 250.943 60.290 33
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DANCENET0.504 210.926 80.579 170.472 140.367 220.626 380.165 180.432 360.221 290.408 170.449 250.411 200.564 140.746 190.421 170.707 140.438 280.846 380.288 34
SoftGrouppermissive0.504 210.667 380.579 180.372 370.381 180.694 210.072 270.677 30.303 200.387 190.531 90.319 260.582 120.754 150.318 250.643 210.492 220.907 170.388 11
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3Dpermissive0.489 230.852 140.511 350.434 240.322 290.735 150.101 240.512 270.355 150.349 220.468 210.283 300.514 210.676 320.268 340.671 170.510 180.908 160.329 25
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
OccuSeg+instance0.486 240.802 200.536 280.428 270.369 210.702 200.205 150.331 490.301 210.379 200.474 190.327 230.437 290.862 20.485 20.601 270.394 360.846 400.273 37
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg0.479 250.704 310.564 220.467 160.366 230.633 360.068 280.554 200.262 260.328 240.447 260.323 240.534 170.722 230.288 310.614 240.482 230.912 140.358 20
DualGroup0.469 260.815 180.552 240.398 300.374 200.683 240.130 200.539 220.310 180.327 250.407 290.276 310.447 280.535 430.342 230.659 180.455 260.900 210.301 29
SSEC0.465 270.667 380.578 190.502 90.362 260.641 350.035 400.605 70.291 250.323 260.451 240.296 280.417 380.677 310.245 380.501 470.506 200.900 200.366 15
HAISpermissive0.457 280.704 310.561 230.457 170.364 240.673 250.046 390.547 210.194 340.308 270.426 270.288 290.454 270.711 260.262 350.563 370.434 300.889 240.344 21
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group0.436 290.630 460.508 380.480 130.310 320.624 400.065 290.638 60.174 350.256 360.384 330.194 430.428 320.759 140.289 300.574 340.400 340.849 370.291 32
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance0.435 300.716 300.495 400.355 390.331 270.689 230.102 230.394 430.208 320.280 300.395 310.250 340.544 150.741 210.309 270.536 430.391 370.842 430.258 41
Mask-Group0.434 310.778 220.516 330.471 150.330 280.658 290.029 420.526 260.249 280.256 350.400 300.309 270.384 420.296 590.368 210.575 330.425 310.877 280.362 19
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask0.433 320.741 260.463 450.433 250.283 350.625 390.103 220.298 540.125 440.260 340.424 280.322 250.472 250.701 290.363 220.711 120.309 530.882 250.272 39
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN0.428 330.630 460.508 370.367 380.249 420.658 300.016 500.673 40.131 420.234 390.383 340.270 320.434 300.748 170.274 330.609 250.406 330.842 420.267 40
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet0.413 340.741 260.520 300.237 500.284 340.523 490.097 250.691 10.138 390.209 490.229 510.238 370.390 400.707 270.310 260.448 540.470 240.892 230.310 27
PointGroup0.407 350.639 450.496 390.415 280.243 440.645 340.021 470.570 160.114 450.211 470.359 360.217 410.428 330.660 340.256 360.562 380.341 450.860 330.291 31
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-Pretrained0.405 360.738 280.465 440.331 430.205 480.655 310.051 350.601 100.092 490.211 480.329 390.198 420.459 260.775 90.195 450.524 450.400 350.878 260.184 50
PE0.396 370.667 380.467 430.446 210.243 430.624 410.022 460.577 140.106 460.219 420.340 370.239 360.487 220.475 500.225 400.541 420.350 430.818 450.273 38
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3Dcopyleft0.395 380.642 440.518 320.447 200.259 410.666 270.050 360.251 590.166 360.231 400.362 350.232 380.331 450.535 420.229 390.587 300.438 290.850 350.317 26
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS0.392 390.778 220.530 290.220 520.278 360.567 460.083 260.330 500.299 220.270 330.310 420.143 490.260 490.624 370.277 320.568 360.361 410.865 320.301 28
AOIA0.387 400.704 310.515 340.385 340.225 470.669 260.005 570.482 330.126 430.181 520.269 480.221 400.426 360.478 490.218 410.592 280.371 390.851 340.242 43
SSEN0.384 410.852 140.494 410.192 530.226 460.648 330.022 450.398 420.299 230.277 310.317 410.231 390.194 560.514 460.196 430.586 310.444 270.843 410.184 49
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Mask3D_evaluation0.382 420.593 480.520 310.390 330.314 310.600 420.018 490.287 570.151 380.281 290.387 320.169 470.429 310.654 350.172 490.578 320.384 380.670 560.278 36
PCJC0.375 430.704 310.542 270.284 470.197 500.649 320.006 540.426 370.138 400.242 370.304 430.183 460.388 410.629 360.141 560.546 410.344 440.738 510.283 35
ClickSeg_Instance0.366 440.654 420.375 490.184 540.302 330.592 440.050 370.300 530.093 480.283 280.277 450.249 350.426 370.615 380.299 280.504 460.367 400.832 440.191 48
SphereSeg0.357 450.651 430.411 470.345 400.264 400.630 370.059 320.289 560.212 300.240 380.336 380.158 480.305 460.557 400.159 520.455 530.341 460.726 530.294 30
3D-MPA0.355 460.457 580.484 420.299 450.277 370.591 450.047 380.332 470.212 310.217 430.278 440.193 440.413 390.410 530.195 440.574 350.352 420.849 360.213 46
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF0.353 470.593 480.511 360.375 360.264 390.597 430.008 520.332 480.160 370.229 410.274 470.000 700.206 530.678 300.155 530.485 490.422 320.816 460.254 42
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
RWSeg0.348 480.475 550.456 460.320 440.275 380.476 510.020 480.491 300.056 560.212 460.320 400.261 330.302 470.520 440.182 470.557 390.285 550.867 310.197 47
GICN0.341 490.580 500.371 500.344 410.198 490.469 520.052 340.564 180.093 470.212 450.212 530.127 510.347 440.537 410.206 420.525 440.329 480.729 520.241 44
One_Thing_One_Clickpermissive0.326 500.472 560.361 510.232 510.183 510.555 470.000 630.498 290.038 580.195 500.226 520.362 210.168 570.469 510.251 370.553 400.335 470.846 390.117 58
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS0.320 510.679 370.352 520.334 420.229 450.436 530.025 430.412 400.058 540.161 570.240 500.085 530.262 480.496 480.187 460.467 510.328 490.775 470.231 45
Sparse R-CNN0.292 520.704 310.213 620.153 560.154 530.551 480.053 330.212 600.132 410.174 540.274 460.070 550.363 430.441 520.176 480.424 560.234 570.758 490.161 54
MTML0.282 530.577 510.380 480.182 550.107 590.430 540.001 600.422 380.057 550.179 530.162 560.070 560.229 510.511 470.161 500.491 480.313 500.650 590.162 52
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet0.262 540.667 380.335 530.067 630.123 570.427 550.022 440.280 580.058 530.216 440.211 540.039 590.142 590.519 450.106 600.338 600.310 520.721 540.138 55
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.254 550.463 570.249 610.113 570.167 520.412 570.000 620.374 450.073 500.173 550.243 490.130 500.228 520.368 550.160 510.356 580.208 580.711 550.136 56
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet0.253 560.519 530.324 560.251 490.137 560.345 620.031 410.419 390.069 510.162 560.131 580.052 570.202 550.338 570.147 550.301 630.303 540.651 580.178 51
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_WSIS0.251 570.380 600.274 590.289 460.144 540.413 560.000 630.311 510.065 520.113 590.130 590.029 620.204 540.388 540.108 590.459 520.311 510.769 480.127 57
SegGroup_inspermissive0.246 580.556 520.335 540.062 650.115 580.490 500.000 630.297 550.018 620.186 510.142 570.083 540.233 500.216 610.153 540.469 500.251 560.744 500.083 61
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst0.214 590.250 650.330 550.275 480.103 600.228 68