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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.851 10.687 30.971 10.586 10.755 10.752 40.505 10.404 40.575 20.000 90.848 10.616 10.761 10.349 10.738 10.978 10.546 30.860 60.926 10.346 10.654 30.384 40.828 10.523 30.699 10.583 30.387 50.822 10.688 10.118 40.474 10.603 40.000 10.832 20.903 10.753 70.140 60.000 70.650 10.109 20.520 10.457 10.497 60.871 30.281 10.192 20.887 20.748 10.168 10.727 20.733 10.740 10.644 10.714 30.190 70.000 30.256 20.449 50.914 10.514 10.759 90.337 10.172 30.692 30.617 10.636 10.325 30.000 10.641 10.782 10.000 40.065 20.000 10.000 30.842 10.903 10.661 10.662 20.612 10.405 20.731 10.566 10.000 30.000 40.000 10.017 90.301 10.088 40.941 10.000 10.077 20.000 70.717 20.790 10.310 90.026 110.264 20.349 10.220 20.397 70.366 10.115 70.000 30.337 10.463 40.000 10.531 10.218 10.593 10.455 10.469 10.708 10.210 10.592 20.108 100.000 10.728 10.682 20.671 40.000 10.000 60.407 10.136 10.022 20.575 10.436 40.259 10.428 10.048 20.000 10.000 10.879 50.000 10.480 10.000 10.133 40.597 10.000 10.690 10.000 10.000 10.009 100.000 90.921 20.000 50.151 10.000 10.000 50.000 10.109 60.494 80.622 20.394 60.073 90.141 70.798 10.528 20.026 10.000 10.551 20.000 20.000 20.134 50.717 40.000 20.000 10.000 10.188 20.000 40.000 20.791 10.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
OA-CNN-L_ScanNet2000.333 50.558 20.269 50.124 70.821 20.703 10.946 30.569 20.662 20.748 50.487 20.455 10.572 40.000 90.789 40.534 50.736 50.271 30.713 20.949 30.498 100.877 20.860 50.332 30.706 10.474 10.788 50.406 70.637 30.495 50.355 60.805 30.592 90.015 100.396 20.602 50.000 10.799 50.876 40.713 110.276 10.000 70.493 70.080 50.448 90.363 20.661 20.833 50.262 30.125 30.823 60.665 50.076 60.720 30.557 50.637 60.517 50.672 80.227 50.000 30.158 70.496 40.843 80.352 60.835 70.000 30.103 90.711 20.527 20.526 40.320 40.000 10.568 30.625 60.067 10.000 60.000 10.001 20.806 30.836 50.621 60.591 40.373 50.314 40.668 40.398 50.003 20.000 40.000 10.016 100.024 20.043 90.906 40.000 10.052 40.000 70.384 60.330 80.342 50.100 60.223 40.183 70.112 40.476 40.313 40.130 60.196 20.112 60.370 80.000 10.234 60.071 60.160 30.403 30.398 80.492 90.197 20.076 80.272 30.000 10.200 110.560 50.735 30.000 10.000 60.000 60.110 40.002 40.021 50.412 50.000 60.000 40.000 70.000 10.000 10.794 60.000 10.445 20.000 10.022 50.509 50.000 10.517 100.000 10.000 10.001 110.245 20.915 40.024 20.089 20.000 10.262 20.000 10.103 80.524 40.392 70.515 20.013 110.251 40.411 90.662 10.001 70.000 10.473 70.000 20.000 20.150 40.699 50.000 20.000 10.000 10.166 40.000 40.024 10.000 60.000 1
CeCo0.340 30.551 50.247 70.181 20.784 70.661 80.939 70.564 30.624 70.721 60.484 30.429 20.575 20.027 50.774 60.503 80.753 20.242 70.656 70.945 40.534 40.865 50.860 50.177 110.616 50.400 20.818 20.579 10.615 50.367 80.408 40.726 90.633 20.162 10.360 30.619 20.000 10.828 30.873 60.924 20.109 80.083 30.564 20.057 110.475 70.266 60.781 10.767 60.257 40.100 70.825 50.663 60.048 100.620 80.551 60.595 90.532 40.692 60.246 30.000 30.213 40.615 10.861 50.376 40.900 20.000 30.102 100.660 40.321 90.547 30.226 70.000 10.311 70.742 20.011 30.006 50.000 10.000 30.546 100.824 60.345 80.665 10.450 30.435 10.683 20.411 40.338 10.000 40.000 10.030 50.000 30.068 60.892 50.000 10.063 30.000 70.257 70.304 90.387 30.079 80.228 30.190 60.000 100.586 10.347 20.133 40.000 30.037 70.377 70.000 10.384 30.006 100.003 70.421 20.410 70.643 30.171 40.121 40.142 80.000 10.510 70.447 60.474 80.000 10.000 60.286 20.083 70.000 50.000 60.603 10.096 30.063 30.000 70.000 10.000 10.898 30.000 10.429 30.000 10.400 10.550 30.000 10.633 40.000 10.000 10.377 30.000 90.916 30.000 50.000 50.000 10.000 50.000 10.102 90.499 60.296 80.463 30.089 40.304 10.740 20.401 100.010 30.000 10.560 10.000 20.000 20.709 10.652 60.000 20.000 10.000 10.143 60.000 40.000 20.609 20.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
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
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
OctFormer ScanNet200permissive0.326 70.539 60.265 60.131 60.806 50.670 70.943 60.535 70.662 20.705 100.423 50.407 30.505 80.003 70.765 70.582 40.686 90.227 100.680 50.943 50.601 10.854 80.892 20.335 20.417 110.357 60.724 70.453 50.632 40.596 20.432 20.783 60.512 110.021 90.244 90.637 10.000 10.787 60.873 60.743 90.000 110.000 70.534 50.110 10.499 40.289 50.626 40.620 90.168 110.204 10.849 40.679 40.117 20.633 60.684 20.650 50.552 20.684 70.312 20.000 30.175 60.429 60.865 30.413 20.837 60.000 30.145 50.626 50.451 40.487 70.513 10.000 10.529 40.613 70.000 40.033 30.000 10.000 30.828 20.871 20.622 50.587 50.411 40.137 80.645 80.343 60.000 30.000 40.000 10.022 70.000 30.026 110.829 80.000 10.022 50.089 30.842 10.253 100.318 80.296 20.178 60.291 30.224 10.584 20.200 80.132 50.000 30.128 50.227 100.000 10.230 70.047 80.149 40.331 70.412 60.618 40.164 50.102 60.522 10.000 10.655 30.378 70.469 90.000 10.000 60.000 60.105 50.000 50.000 60.483 30.000 60.000 40.028 40.000 10.000 10.906 10.000 10.339 90.000 10.000 70.457 60.000 10.612 50.000 10.000 10.408 20.000 90.900 60.000 50.000 50.000 10.029 40.000 10.074 110.455 90.479 30.427 40.079 70.140 80.496 50.414 80.022 20.000 10.471 80.000 20.000 20.000 70.722 30.000 20.000 10.000 10.138 80.000 40.000 20.000 60.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CSC-Pretrainpermissive0.249 110.455 110.171 100.079 110.766 110.659 90.930 110.494 80.542 110.700 110.314 110.215 110.430 110.121 10.697 110.441 100.683 100.235 80.609 110.895 100.476 110.816 100.770 110.186 80.634 40.216 110.734 60.340 100.471 100.307 100.293 110.591 110.542 100.076 50.205 100.464 90.000 10.484 110.832 110.766 60.052 100.000 70.413 100.059 100.418 100.222 100.318 110.609 100.206 90.112 50.743 80.625 80.076 60.579 100.548 70.590 100.371 100.552 110.081 100.003 20.142 80.201 110.638 110.233 100.686 110.000 30.142 60.444 110.375 70.247 110.198 80.000 10.128 110.454 110.019 20.097 10.000 10.000 30.553 90.557 100.373 70.545 100.164 100.014 110.547 100.174 90.000 30.002 20.000 10.037 20.000 30.063 80.664 110.000 10.000 70.130 20.170 80.152 110.335 70.079 80.110 90.175 80.098 60.175 110.166 90.045 110.207 10.014 80.465 30.000 10.001 110.001 110.046 60.299 90.327 100.537 70.033 100.012 110.186 70.000 10.205 100.377 80.463 100.000 10.058 20.000 60.055 90.041 10.000 60.105 100.000 60.000 40.000 70.000 10.000 10.398 90.000 10.308 110.000 10.000 70.319 90.000 10.543 90.000 10.000 10.062 90.004 70.862 100.000 50.000 50.000 10.000 50.000 10.123 30.316 100.225 90.250 90.094 20.180 50.332 100.441 50.000 80.000 10.310 110.000 20.000 20.000 70.592 80.000 20.000 10.000 10.203 10.000 40.000 20.000 60.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
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
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
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 25%head ap 25%common ap 25%tail ap 25%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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
TD3D Scannet200permissive0.379 20.603 20.306 20.190 20.885 10.755 10.800 20.958 10.390 20.260 20.866 20.232 10.979 20.523 30.869 30.559 50.689 21.000 10.795 10.905 20.748 10.173 50.825 10.173 20.970 10.457 10.615 20.456 20.200 10.621 40.906 20.553 10.517 10.510 10.220 20.715 10.706 21.000 10.113 20.792 10.717 20.073 20.635 20.557 10.638 11.000 10.205 50.146 31.000 10.769 50.186 21.000 10.710 50.778 10.415 10.834 40.226 20.021 20.590 20.356 20.817 10.477 51.000 10.000 10.635 10.843 20.427 10.270 40.125 20.000 20.102 31.000 10.125 20.000 20.000 10.000 20.000 30.125 40.370 30.622 50.221 10.196 20.836 10.288 20.000 20.093 20.020 20.294 20.000 10.075 20.667 10.038 10.111 10.250 40.000 40.526 20.495 30.908 10.111 30.259 10.003 30.667 20.045 50.000 20.000 10.400 10.274 30.000 10.274 20.226 20.000 10.520 20.302 50.731 20.103 30.458 10.500 10.000 11.000 10.472 10.792 30.000 10.088 20.061 20.250 10.009 20.250 20.333 30.181 20.396 20.051 20.012 10.000 10.458 40.000 10.424 50.000 10.101 20.390 50.000 10.833 20.000 10.000 10.857 20.222 31.000 10.000 10.003 20.000 10.000 20.000 10.102 20.275 50.400 20.735 20.061 30.433 30.533 30.625 10.000 20.000 10.259 40.000 10.000 10.000 20.500 20.000 10.000 21.000 10.600 10.000 20.250 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 Scannet2000.445 10.653 10.392 10.254 10.844 20.746 20.818 10.888 40.556 10.262 10.890 10.025 21.000 10.608 10.930 10.694 30.721 10.930 50.686 30.966 10.615 40.440 10.725 40.201 10.890 30.414 40.827 10.552 10.158 50.806 10.924 10.042 30.512 20.412 50.226 10.604 30.830 11.000 10.125 10.792 10.815 10.097 10.648 10.551 20.354 41.000 10.630 10.241 21.000 10.853 10.204 10.974 40.841 10.778 10.358 20.927 10.300 10.045 10.640 10.363 10.745 20.710 11.000 10.000 10.330 20.943 10.315 20.600 11.000 10.027 10.080 50.556 50.500 10.409 10.000 10.194 11.000 10.500 10.493 20.761 20.053 40.042 30.780 20.454 10.009 10.333 10.050 10.321 10.000 10.084 10.552 20.008 20.027 20.750 10.500 10.442 30.657 10.765 20.120 20.183 30.021 21.000 10.510 20.016 10.000 10.400 10.619 10.000 10.396 10.290 10.000 10.741 10.699 11.000 10.260 10.017 30.125 50.000 10.792 40.399 41.000 10.000 10.049 30.265 10.063 30.000 31.000 10.335 20.381 10.500 10.250 10.004 20.000 10.727 20.000 10.538 30.000 10.188 10.677 20.000 10.930 10.000 10.000 10.966 10.391 10.908 20.000 10.028 10.000 11.000 10.000 10.152 10.451 20.458 10.971 10.573 10.606 10.167 50.625 10.004 10.000 10.058 50.000 10.000 11.000 11.000 10.000 10.056 10.000 20.200 30.309 10.000 21.000 10.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
LGround Inst.permissive0.314 30.529 30.225 30.155 30.810 30.625 30.798 30.940 20.372 30.217 30.484 30.000 30.927 30.528 20.826 50.694 20.605 31.000 10.731 20.846 30.716 30.350 20.589 50.123 40.857 40.457 20.578 30.376 40.183 20.765 30.800 30.000 40.278 40.500 20.000 30.659 20.569 41.000 10.093 30.000 30.539 30.010 30.578 50.378 40.571 21.000 10.337 30.252 10.530 50.814 30.000 40.744 50.743 30.746 30.346 30.863 30.067 30.000 30.400 30.167 30.667 30.488 41.000 10.000 10.208 40.783 30.166 40.375 20.071 50.000 20.200 10.607 40.000 30.000 20.000 10.000 21.000 10.500 10.517 10.716 40.221 20.000 40.706 30.085 50.000 20.000 30.000 30.077 40.000 10.063 30.278 30.000 30.000 30.500 20.083 30.181 50.515 20.286 30.144 10.219 20.042 10.582 40.400 30.000 20.000 10.000 50.305 20.000 10.000 40.036 30.000 10.413 30.500 20.533 50.250 20.200 20.500 10.000 11.000 10.472 11.000 10.000 10.000 40.000 30.250 10.000 30.000 30.333 30.000 30.000 30.000 30.000 30.000 10.600 30.000 10.594 20.000 10.000 30.500 30.000 10.647 50.000 10.000 10.429 30.333 20.500 50.000 10.000 30.000 10.000 20.000 10.069 30.696 10.050 50.556 30.031 50.042 50.750 10.250 40.000 20.000 10.630 10.000 10.000 10.000 20.500 20.000 10.000 20.000 20.400 20.000 20.000 20.000 20.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.permissive0.280 40.488 40.192 50.124 40.804 40.518 40.772 50.904 30.337 50.191 40.443 40.000 30.861 40.502 40.868 40.669 40.587 40.997 30.467 50.828 50.732 20.342 30.745 30.119 50.918 20.404 50.419 40.398 30.172 30.618 50.743 40.167 20.077 50.500 20.000 30.568 40.506 51.000 10.044 40.000 30.502 40.010 40.593 40.284 50.305 50.903 50.213 40.142 40.981 30.790 40.000 41.000 10.715 40.538 50.346 40.830 50.067 30.000 30.400 30.074 40.333 40.551 21.000 10.000 10.292 30.777 40.118 50.317 30.100 40.000 20.191 20.648 30.000 30.000 20.000 10.000 20.000 30.500 10.213 50.825 10.021 50.333 10.648 50.098 40.000 20.000 30.000 30.077 30.000 10.000 50.150 50.000 30.000 30.000 50.225 20.281 40.447 40.000 50.090 40.148 40.000 40.479 50.542 10.000 20.000 10.200 30.131 50.000 10.250 30.000 40.000 10.159 50.396 40.677 30.021 40.000 40.500 10.000 11.000 10.442 30.125 50.000 10.000 40.000 30.000 40.333 10.000 30.528 10.000 30.000 30.000 30.000 30.000 10.200 50.000 10.516 40.000 10.000 30.500 30.000 10.833 20.000 10.000 10.286 40.083 40.750 30.000 10.000 30.000 10.000 20.000 10.059 50.445 30.200 30.535 40.070 20.167 40.385 40.375 30.000 20.000 10.333 30.000 10.000 10.000 20.500 20.000 10.000 20.000 20.200 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.permissive0.275 50.466 50.218 40.110 50.783 50.383 50.783 40.829 50.367 40.168 50.305 50.000 30.661 50.413 50.869 20.719 10.546 50.997 30.685 40.841 40.555 50.277 40.768 20.132 30.779 50.448 30.364 50.212 50.161 40.768 20.692 50.000 40.395 30.500 20.000 30.450 50.591 31.000 10.020 50.000 30.423 50.007 50.625 30.420 30.505 31.000 10.353 20.119 50.571 40.819 20.014 31.000 10.774 20.689 40.311 50.866 20.067 30.000 30.400 30.000 50.278 50.501 31.000 10.000 10.162 50.584 50.286 30.206 50.125 20.000 20.084 40.649 20.000 30.000 20.000 10.000 20.000 30.125 40.312 40.727 30.221 20.000 40.667 40.114 30.000 20.000 30.000 30.065 50.000 10.004 40.278 30.000 30.000 30.500 20.000 40.571 10.000 50.250 40.019 50.145 50.000 40.667 20.200 40.000 20.000 10.200 30.258 40.000 10.000 40.000 40.000 10.369 40.429 30.613 40.000 50.000 40.500 10.000 10.500 50.333 50.500 40.000 10.106 10.000 30.000 40.000 30.000 30.333 30.000 30.000 30.000 30.000 30.000 10.918 10.000 10.638 10.000 10.000 30.750 10.000 10.833 20.000 10.000 10.143 50.000 50.750 30.000 10.000 30.000 10.000 20.000 10.063 40.377 40.200 30.222 50.055 40.500 20.677 20.250 40.000 20.000 10.500 20.000 10.000 10.000 20.500 20.000 10.000 20.000 20.115 50.000 20.000 20.000 20.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mix3Dpermissive0.781 30.964 20.855 10.843 150.781 60.858 100.575 50.831 300.685 120.714 20.979 10.594 60.310 250.801 10.892 140.841 20.819 30.723 40.940 120.887 50.725 21
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
VMNetpermissive0.746 200.870 180.838 20.858 40.729 280.850 180.501 340.874 140.587 510.658 170.956 240.564 200.299 290.765 140.900 100.716 340.812 100.631 360.939 130.858 250.709 28
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
IPCA0.731 290.890 140.837 30.864 20.726 290.873 40.530 240.824 340.489 840.647 180.978 40.609 40.336 150.624 470.733 550.758 180.776 350.570 620.949 70.877 120.728 17
PNE0.755 120.786 410.835 40.834 200.758 130.849 190.570 70.836 290.648 240.668 150.978 40.581 140.367 60.683 320.856 270.804 50.801 180.678 160.961 40.889 40.716 26
P. Hermosilla: Point Neighborhood Embeddings.
OA-CNN-L_ScanNet200.756 110.783 430.826 50.858 40.776 70.837 310.548 140.896 100.649 230.675 110.962 140.586 110.335 160.771 90.802 460.770 140.787 310.691 120.936 160.880 100.761 9
ResLFE_HDS0.772 50.939 40.824 60.854 60.771 80.840 280.564 90.900 70.686 110.677 100.961 150.537 280.348 110.769 100.903 80.785 90.815 50.676 190.939 130.880 100.772 7
O-CNNpermissive0.762 100.924 80.823 70.844 140.770 90.852 160.577 30.847 250.711 20.640 250.958 190.592 70.217 700.762 150.888 150.758 180.813 90.726 20.932 210.868 180.744 12
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
ConDaFormer0.755 120.927 60.822 80.836 180.801 10.849 190.516 290.864 190.651 220.680 90.958 190.584 130.282 390.759 170.855 290.728 260.802 140.678 160.880 560.873 170.756 10
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
SparseConvNet0.725 300.647 880.821 90.846 120.721 300.869 50.533 210.754 550.603 440.614 330.955 270.572 170.325 200.710 310.870 190.724 290.823 20.628 370.934 180.865 210.683 36
LargeKernel3D0.739 260.909 100.820 100.806 360.740 240.852 160.545 150.826 320.594 490.643 210.955 270.541 270.263 540.723 300.858 260.775 130.767 410.678 160.933 190.848 340.694 33
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
CU-Hybrid Net0.764 80.924 80.819 110.840 160.757 150.853 140.580 20.848 230.709 30.643 210.958 190.587 100.295 310.753 210.884 180.758 180.815 50.725 30.927 230.867 190.743 13
Virtual MVFusion0.746 200.771 510.819 110.848 110.702 340.865 80.397 820.899 80.699 50.664 160.948 520.588 90.330 180.746 250.851 330.764 160.796 220.704 90.935 170.866 200.728 17
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNetcopyleft0.749 160.909 100.818 130.811 310.752 180.839 300.485 440.842 260.673 150.644 200.957 230.528 340.305 270.773 80.859 240.788 70.818 40.693 110.916 300.856 270.723 22
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MinkowskiNetpermissive0.736 270.859 220.818 130.832 220.709 320.840 280.521 270.853 210.660 200.643 210.951 420.544 260.286 370.731 280.893 130.675 520.772 370.683 150.874 630.852 320.727 19
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
Swin3Dpermissive0.779 40.861 200.818 130.836 180.790 20.875 30.576 40.905 50.704 40.739 10.969 90.611 20.349 100.756 190.958 10.702 420.805 130.708 70.916 300.898 20.801 2
PTv3 ScanNet0.794 10.941 30.813 160.851 70.782 50.890 20.597 10.916 10.696 70.713 30.979 10.635 10.384 20.793 20.907 60.821 30.790 290.696 100.967 30.903 10.805 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024
MatchingNet0.724 320.812 360.812 170.810 320.735 260.834 350.495 400.860 200.572 580.602 410.954 330.512 390.280 410.757 180.845 350.725 280.780 330.606 470.937 150.851 330.700 32
PointTransformer++0.725 300.727 730.811 180.819 260.765 110.841 270.502 330.814 390.621 340.623 310.955 270.556 230.284 380.620 490.866 210.781 100.757 510.648 280.932 210.862 220.709 28
PointTransformerV20.752 140.742 650.809 190.872 10.758 130.860 90.552 120.891 110.610 380.687 50.960 160.559 220.304 280.766 130.926 30.767 150.797 210.644 300.942 100.876 150.722 23
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
OctFormerpermissive0.766 60.925 70.808 200.849 90.786 40.846 240.566 80.876 130.690 90.674 120.960 160.576 150.226 640.753 210.904 70.777 110.815 50.722 50.923 260.877 120.776 6
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
LRPNet0.742 240.816 340.806 210.807 340.752 180.828 410.575 50.839 280.699 50.637 260.954 330.520 370.320 220.755 200.834 370.760 170.772 370.676 190.915 320.862 220.717 24
MSP0.748 180.623 910.804 220.859 30.745 230.824 450.501 340.912 30.690 90.685 70.956 240.567 180.320 220.768 120.918 40.720 310.802 140.676 190.921 270.881 90.779 5
StratifiedFormerpermissive0.747 190.901 130.803 230.845 130.757 150.846 240.512 300.825 330.696 70.645 190.956 240.576 150.262 550.744 260.861 230.742 220.770 400.705 80.899 420.860 240.734 14
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Retro-FPN0.744 220.842 260.800 240.767 520.740 240.836 330.541 170.914 20.672 160.626 290.958 190.552 250.272 460.777 60.886 170.696 430.801 180.674 220.941 110.858 250.717 24
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
PonderV20.785 20.978 10.800 240.833 210.788 30.853 140.545 150.910 40.713 10.705 40.979 10.596 50.390 10.769 100.832 390.821 30.792 280.730 10.975 10.897 30.785 3
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
ClickSeg_Semantic0.703 360.774 490.800 240.793 430.760 120.847 230.471 480.802 430.463 910.634 270.968 110.491 450.271 480.726 290.910 50.706 380.815 50.551 740.878 570.833 400.570 74
EQ-Net0.743 230.620 920.799 270.849 90.730 270.822 470.493 410.897 90.664 170.681 80.955 270.562 210.378 30.760 160.903 80.738 230.801 180.673 230.907 340.877 120.745 11
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
One Thing One Click0.701 380.825 310.796 280.723 590.716 310.832 370.433 720.816 360.634 290.609 360.969 90.418 800.344 120.559 650.833 380.715 350.808 120.560 680.902 390.847 350.680 37
OccuSeg+Semantic0.764 80.758 570.796 280.839 170.746 220.907 10.562 100.850 220.680 140.672 130.978 40.610 30.335 160.777 60.819 420.847 10.830 10.691 120.972 20.885 70.727 19
One-Thing-One-Click0.693 400.743 640.794 300.655 820.684 390.822 470.497 390.719 650.622 330.617 320.977 70.447 670.339 140.750 240.664 710.703 410.790 290.596 520.946 90.855 290.647 47
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PPT-SpUNet-Joint0.766 60.932 50.794 300.829 230.751 200.854 120.540 190.903 60.630 310.672 130.963 130.565 190.357 80.788 30.900 100.737 240.802 140.685 140.950 60.887 50.780 4
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
DMF-Net0.752 140.906 120.793 320.802 380.689 370.825 430.556 110.867 150.681 130.602 410.960 160.555 240.365 70.779 50.859 240.747 210.795 250.717 60.917 290.856 270.764 8
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
RPN0.736 270.776 470.790 330.851 70.754 170.854 120.491 430.866 170.596 480.686 60.955 270.536 290.342 130.624 470.869 200.787 80.802 140.628 370.927 230.875 160.704 30
PointConvFormer0.749 160.793 390.790 330.807 340.750 210.856 110.524 250.881 120.588 500.642 240.977 70.591 80.274 440.781 40.929 20.804 50.796 220.642 310.947 80.885 70.715 27
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
PointMetaBase0.714 340.835 270.785 350.821 240.684 390.846 240.531 230.865 180.614 350.596 450.953 360.500 420.246 600.674 330.888 150.692 440.764 430.624 390.849 780.844 390.675 38
PointContrast_LA_SEM0.683 480.757 580.784 360.786 440.639 540.824 450.408 770.775 480.604 430.541 570.934 840.532 320.269 500.552 680.777 480.645 680.793 260.640 320.913 330.824 450.671 39
DGNet0.684 450.712 770.784 360.782 480.658 440.835 340.499 380.823 350.641 260.597 440.950 460.487 470.281 400.575 590.619 750.647 650.764 430.620 420.871 690.846 370.688 35
PointASNLpermissive0.666 540.703 790.781 380.751 580.655 460.830 380.471 480.769 500.474 870.537 590.951 420.475 520.279 420.635 420.698 650.675 520.751 530.553 730.816 850.806 560.703 31
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
joint point-basedpermissive0.634 700.614 930.778 390.667 790.633 570.825 430.420 750.804 410.467 890.561 520.951 420.494 430.291 340.566 620.458 910.579 870.764 430.559 700.838 800.814 520.598 66
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
contrastBoundarypermissive0.705 350.769 540.775 400.809 330.687 380.820 500.439 700.812 400.661 190.591 470.945 600.515 380.171 880.633 440.856 270.720 310.796 220.668 240.889 490.847 350.689 34
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
SPH3D-GCNpermissive0.610 810.858 230.772 410.489 1030.532 830.792 720.404 800.643 830.570 610.507 720.935 800.414 810.046 1080.510 780.702 630.602 790.705 710.549 760.859 750.773 810.534 87
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
PicassoNet-IIpermissive0.692 410.732 690.772 410.786 440.677 410.866 70.517 280.848 230.509 770.626 290.952 400.536 290.225 660.545 710.704 620.689 490.810 110.564 670.903 380.854 310.729 16
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
O3DSeg0.668 530.822 320.771 430.496 1020.651 490.833 360.541 170.761 520.555 660.611 340.966 120.489 460.370 50.388 960.580 780.776 120.751 530.570 620.956 50.817 510.646 48
SALANet0.670 520.816 340.770 440.768 510.652 480.807 610.451 570.747 570.659 210.545 560.924 900.473 530.149 980.571 610.811 440.635 710.746 560.623 400.892 470.794 650.570 74
PD-Net0.638 660.797 380.769 450.641 900.590 670.820 500.461 540.537 970.637 280.536 600.947 540.388 870.206 740.656 360.668 690.647 650.732 610.585 590.868 710.793 670.473 100
SAT0.742 240.860 210.765 460.819 260.769 100.848 210.533 210.829 310.663 180.631 280.955 270.586 110.274 440.753 210.896 120.729 250.760 470.666 250.921 270.855 290.733 15
JSENetpermissive0.699 390.881 170.762 470.821 240.667 430.800 660.522 260.792 460.613 360.607 380.935 800.492 440.205 750.576 580.853 310.691 460.758 490.652 270.872 660.828 430.649 46
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
FPConvpermissive0.639 650.785 420.760 480.713 650.603 620.798 670.392 840.534 980.603 440.524 650.948 520.457 590.250 580.538 720.723 580.598 810.696 740.614 430.872 660.799 600.567 77
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointConvpermissive0.666 540.781 440.759 490.699 670.644 530.822 470.475 460.779 470.564 630.504 730.953 360.428 740.203 770.586 560.754 510.661 580.753 520.588 580.902 390.813 540.642 49
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
INS-Conv-semantic0.717 330.751 600.759 490.812 300.704 330.868 60.537 200.842 260.609 400.608 370.953 360.534 310.293 320.616 500.864 220.719 330.793 260.640 320.933 190.845 380.663 42
KP-FCNN0.684 450.847 250.758 510.784 460.647 500.814 570.473 470.772 490.605 420.594 460.935 800.450 650.181 860.587 540.805 450.690 470.785 320.614 430.882 530.819 500.632 53
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 450.728 720.757 520.776 490.690 350.804 640.464 530.816 360.577 570.587 480.945 600.508 410.276 430.671 340.710 600.663 570.750 550.589 570.881 540.832 420.653 45
VI-PointConv0.676 500.770 530.754 530.783 470.621 580.814 570.552 120.758 530.571 600.557 530.954 330.529 330.268 520.530 740.682 660.675 520.719 640.603 490.888 500.833 400.665 41
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
Feature_GeometricNetpermissive0.690 420.884 160.754 530.795 410.647 500.818 540.422 740.802 430.612 370.604 390.945 600.462 570.189 830.563 640.853 310.726 270.765 420.632 350.904 360.821 490.606 61
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
dtc_net0.625 780.703 790.751 550.794 420.535 820.848 210.480 450.676 770.528 710.469 800.944 660.454 610.004 1110.464 880.636 730.704 400.758 490.548 770.924 250.787 740.492 94
MVF-GNN0.658 570.558 990.751 550.655 820.690 350.722 910.453 560.867 150.579 560.576 490.893 1020.523 350.293 320.733 270.571 800.692 440.659 860.606 470.875 600.804 580.668 40
Feature-Geometry Netpermissive0.685 440.866 190.748 570.819 260.645 520.794 690.450 600.802 430.587 510.604 390.945 600.464 560.201 780.554 670.840 360.723 300.732 610.602 500.907 340.822 480.603 64
ROSMRF3D0.673 510.789 400.748 570.763 540.635 560.814 570.407 790.747 570.581 550.573 500.950 460.484 480.271 480.607 510.754 510.649 620.774 360.596 520.883 520.823 460.606 61
SegGroup_sempermissive0.627 770.818 330.747 590.701 660.602 630.764 830.385 880.629 840.490 820.508 700.931 870.409 820.201 780.564 630.725 570.618 740.692 750.539 820.873 640.794 650.548 84
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3DSM_DMMF0.631 720.626 900.745 600.801 390.607 610.751 870.506 310.729 640.565 620.491 750.866 1050.434 690.197 810.595 520.630 740.709 370.705 710.560 680.875 600.740 900.491 95
RFCR0.702 370.889 150.745 600.813 290.672 420.818 540.493 410.815 380.623 320.610 350.947 540.470 540.249 590.594 530.848 340.705 390.779 340.646 290.892 470.823 460.611 57
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
wsss-transformer0.600 830.634 890.743 620.697 690.601 640.781 750.437 710.585 900.493 810.446 850.933 850.394 850.011 1100.654 370.661 720.603 780.733 600.526 850.832 810.761 850.480 97
HPGCNN0.656 590.698 810.743 620.650 840.564 760.820 500.505 320.758 530.631 300.479 770.945 600.480 500.226 640.572 600.774 490.690 470.735 590.614 430.853 770.776 800.597 67
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
FusionNet0.688 430.704 780.741 640.754 560.656 450.829 390.501 340.741 600.609 400.548 550.950 460.522 360.371 40.633 440.756 500.715 350.771 390.623 400.861 740.814 520.658 43
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
FusionAwareConv0.630 750.604 950.741 640.766 530.590 670.747 880.501 340.734 620.503 790.527 630.919 940.454 610.323 210.550 700.420 950.678 510.688 770.544 780.896 440.795 640.627 55
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
SAFNet-segpermissive0.654 600.752 590.734 660.664 800.583 710.815 560.399 810.754 550.639 270.535 610.942 700.470 540.309 260.665 350.539 820.650 610.708 690.635 340.857 760.793 670.642 49
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 610.778 450.731 670.699 670.577 720.829 390.446 620.736 610.477 860.523 670.945 600.454 610.269 500.484 860.749 540.618 740.738 570.599 510.827 820.792 700.621 56
Superpoint Network0.683 480.851 240.728 680.800 400.653 470.806 620.468 500.804 410.572 580.602 410.946 570.453 640.239 630.519 760.822 400.689 490.762 460.595 540.895 450.827 440.630 54
MVPNetpermissive0.641 620.831 280.715 690.671 770.590 670.781 750.394 830.679 750.642 250.553 540.937 770.462 570.256 560.649 380.406 960.626 720.691 760.666 250.877 580.792 700.608 60
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
Supervoxel-CNN0.635 690.656 860.711 700.719 620.613 600.757 860.444 670.765 510.534 690.566 510.928 880.478 510.272 460.636 410.531 840.664 560.645 900.508 880.864 730.792 700.611 57
PPCNN++permissive0.663 560.746 620.708 710.722 600.638 550.820 500.451 570.566 930.599 460.541 570.950 460.510 400.313 240.648 390.819 420.616 760.682 790.590 560.869 700.810 550.656 44
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
ROSMRF0.580 870.772 500.707 720.681 730.563 770.764 830.362 910.515 990.465 900.465 820.936 790.427 760.207 730.438 900.577 790.536 910.675 820.486 930.723 970.779 770.524 90
PointConv-SFPN0.641 620.776 470.703 730.721 610.557 790.826 420.451 570.672 780.563 640.483 760.943 690.425 770.162 930.644 400.726 560.659 590.709 680.572 610.875 600.786 750.559 80
DCM-Net0.658 570.778 450.702 740.806 360.619 590.813 600.468 500.693 730.494 800.524 650.941 720.449 660.298 300.510 780.821 410.675 520.727 630.568 650.826 830.803 590.637 51
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
PointMRNet0.640 640.717 760.701 750.692 700.576 730.801 650.467 520.716 660.563 640.459 830.953 360.429 730.169 900.581 570.854 300.605 770.710 660.550 750.894 460.793 670.575 72
DPC0.592 850.720 740.700 760.602 950.480 910.762 850.380 890.713 690.585 540.437 880.940 740.369 900.288 350.434 920.509 880.590 850.639 930.567 660.772 910.755 870.592 69
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
DVVNet0.562 910.648 870.700 760.770 500.586 700.687 960.333 950.650 800.514 760.475 790.906 980.359 910.223 680.340 990.442 940.422 1020.668 840.501 890.708 980.779 770.534 87
SConv0.636 680.830 290.697 780.752 570.572 750.780 770.445 640.716 660.529 700.530 620.951 420.446 680.170 890.507 810.666 700.636 700.682 790.541 810.886 510.799 600.594 68
SQN_0.1%0.569 890.676 830.696 790.657 810.497 870.779 780.424 730.548 950.515 750.376 950.902 1010.422 780.357 80.379 970.456 920.596 820.659 860.544 780.685 1000.665 1030.556 82
SIConv0.625 780.830 290.694 800.757 550.563 770.772 810.448 610.647 820.520 730.509 690.949 500.431 720.191 820.496 830.614 760.647 650.672 830.535 840.876 590.783 760.571 73
GMLPs0.538 930.495 1040.693 810.647 860.471 930.793 700.300 980.477 1000.505 780.358 980.903 1000.327 960.081 1050.472 870.529 850.448 1000.710 660.509 860.746 930.737 910.554 83
LAP-D0.594 840.720 740.692 820.637 910.456 950.773 800.391 860.730 630.587 510.445 870.940 740.381 880.288 350.434 920.453 930.591 830.649 880.581 600.777 890.749 890.610 59
PointNet2-SFPN0.631 720.771 510.692 820.672 750.524 840.837 310.440 690.706 710.538 680.446 850.944 660.421 790.219 690.552 680.751 530.591 830.737 580.543 800.901 410.768 820.557 81
PointSPNet0.637 670.734 680.692 820.714 640.576 730.797 680.446 620.743 590.598 470.437 880.942 700.403 830.150 970.626 460.800 470.649 620.697 730.557 710.846 790.777 790.563 78
PanopticFusion-label0.529 940.491 1050.688 850.604 940.386 1000.632 1030.225 1080.705 720.434 970.293 1040.815 1060.348 940.241 620.499 820.669 680.507 930.649 880.442 1020.796 870.602 1070.561 79
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
PointMTL0.632 710.731 700.688 850.675 740.591 660.784 740.444 670.565 940.610 380.492 740.949 500.456 600.254 570.587 540.706 610.599 800.665 850.612 460.868 710.791 730.579 71
APCF-Net0.631 720.742 650.687 870.672 750.557 790.792 720.408 770.665 790.545 670.508 700.952 400.428 740.186 840.634 430.702 630.620 730.706 700.555 720.873 640.798 620.581 70
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
HPEIN0.618 800.729 710.668 880.647 860.597 650.766 820.414 760.680 740.520 730.525 640.946 570.432 700.215 710.493 840.599 770.638 690.617 950.570 620.897 430.806 560.605 63
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
AttAN0.609 820.760 560.667 890.649 850.521 850.793 700.457 550.648 810.528 710.434 900.947 540.401 840.153 960.454 890.721 590.648 640.717 650.536 830.904 360.765 830.485 96
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
TextureNetpermissive0.566 900.672 850.664 900.671 770.494 890.719 920.445 640.678 760.411 1000.396 930.935 800.356 920.225 660.412 940.535 830.565 890.636 940.464 960.794 880.680 1000.568 76
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
Weakly-Openseg v30.489 990.749 610.664 900.646 880.496 880.559 1080.122 1110.577 910.257 1110.364 970.805 1070.198 1090.096 1030.510 780.496 900.361 1060.563 990.359 1090.777 890.644 1040.532 89
Pointnet++ & Featurepermissive0.557 920.735 670.661 920.686 710.491 900.744 890.392 840.539 960.451 930.375 960.946 570.376 890.205 750.403 950.356 990.553 900.643 910.497 900.824 840.756 860.515 91
CCRFNet0.589 860.766 550.659 930.683 720.470 940.740 900.387 870.620 860.490 820.476 780.922 920.355 930.245 610.511 770.511 870.571 880.643 910.493 920.872 660.762 840.600 65
3DWSSS0.425 1070.525 1020.647 940.522 1000.324 1070.488 1110.077 1120.712 700.353 1040.401 920.636 1120.281 1020.176 870.340 990.565 810.175 1120.551 1020.398 1060.370 1120.602 1070.361 105
Tangent Convolutionspermissive0.438 1060.437 1090.646 950.474 1040.369 1020.645 1010.353 920.258 1080.282 1090.279 1050.918 950.298 1000.147 990.283 1030.294 1010.487 950.562 1000.427 1040.619 1050.633 1050.352 106
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PCNN0.498 980.559 980.644 960.560 990.420 990.711 940.229 1060.414 1010.436 960.352 1000.941 720.324 970.155 950.238 1060.387 980.493 940.529 1050.509 860.813 860.751 880.504 93
Online SegFusion0.515 960.607 940.644 960.579 970.434 970.630 1040.353 920.628 850.440 950.410 910.762 1100.307 980.167 910.520 750.403 970.516 920.565 980.447 1000.678 1010.701 970.514 92
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
DenSeR0.628 760.800 370.625 980.719 620.545 810.806 620.445 640.597 870.448 940.519 680.938 760.481 490.328 190.489 850.499 890.657 600.759 480.592 550.881 540.797 630.634 52
DGCNN_reproducecopyleft0.446 1030.474 1070.623 990.463 1050.366 1030.651 1000.310 960.389 1040.349 1050.330 1010.937 770.271 1030.126 1000.285 1020.224 1050.350 1080.577 970.445 1010.625 1040.723 940.394 103
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
SurfaceConvPF0.442 1040.505 1030.622 1000.380 1100.342 1060.654 990.227 1070.397 1030.367 1030.276 1060.924 900.240 1060.198 800.359 980.262 1020.366 1040.581 960.435 1030.640 1030.668 1010.398 102
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PointCNN with RGBpermissive0.458 1010.577 970.611 1010.356 1110.321 1080.715 930.299 1000.376 1050.328 1070.319 1020.944 660.285 1010.164 920.216 1090.229 1040.484 960.545 1030.456 980.755 920.709 960.475 99
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
SD-DETR0.576 880.746 620.609 1020.445 1070.517 860.643 1020.366 900.714 680.456 920.468 810.870 1040.432 700.264 530.558 660.674 670.586 860.688 770.482 940.739 950.733 920.537 86
3DMV, FTSDF0.501 970.558 990.608 1030.424 1090.478 920.690 950.246 1040.586 890.468 880.450 840.911 960.394 850.160 940.438 900.212 1060.432 1010.541 1040.475 950.742 940.727 930.477 98
FCPNpermissive0.447 1020.679 820.604 1040.578 980.380 1010.682 970.291 1010.106 1110.483 850.258 1090.920 930.258 1050.025 1090.231 1080.325 1000.480 970.560 1010.463 970.725 960.666 1020.231 111
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
subcloud_weak0.516 950.676 830.591 1050.609 920.442 960.774 790.335 940.597 870.422 990.357 990.932 860.341 950.094 1040.298 1010.528 860.473 980.676 810.495 910.602 1060.721 950.349 107
PNET20.442 1040.548 1010.548 1060.597 960.363 1040.628 1050.300 980.292 1060.374 1020.307 1030.881 1030.268 1040.186 840.238 1060.204 1080.407 1030.506 1090.449 990.667 1020.620 1060.462 101
3DMV0.484 1000.484 1060.538 1070.643 890.424 980.606 1070.310 960.574 920.433 980.378 940.796 1080.301 990.214 720.537 730.208 1070.472 990.507 1080.413 1050.693 990.602 1070.539 85
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SPLAT Netcopyleft0.393 1080.472 1080.511 1080.606 930.311 1090.656 980.245 1050.405 1020.328 1070.197 1100.927 890.227 1080.000 1130.001 1130.249 1030.271 1110.510 1060.383 1080.593 1070.699 980.267 109
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 1090.297 1110.491 1090.432 1080.358 1050.612 1060.274 1020.116 1100.411 1000.265 1070.904 990.229 1070.079 1060.250 1040.185 1090.320 1090.510 1060.385 1070.548 1080.597 1100.394 103
PointNet++permissive0.339 1100.584 960.478 1100.458 1060.256 1110.360 1120.250 1030.247 1090.278 1100.261 1080.677 1110.183 1100.117 1010.212 1100.145 1110.364 1050.346 1120.232 1120.548 1080.523 1110.252 110
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ScanNetpermissive0.306 1120.203 1120.366 1110.501 1010.311 1090.524 1100.211 1090.002 1130.342 1060.189 1110.786 1090.145 1120.102 1020.245 1050.152 1100.318 1100.348 1110.300 1110.460 1110.437 1120.182 112
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SSC-UNetpermissive0.308 1110.353 1100.290 1120.278 1120.166 1120.553 1090.169 1100.286 1070.147 1120.148 1120.908 970.182 1110.064 1070.023 1120.018 1130.354 1070.363 1100.345 1100.546 1100.685 990.278 108
ERROR0.054 1130.000 1130.041 1130.172 1130.030 1130.062 1130.001 1130.035 1120.004 1130.051 1130.143 1130.019 1130.003 1120.041 1110.050 1120.003 1130.054 1130.018 1130.005 1130.264 1130.082 113


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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
ExtMask3D0.867 81.000 11.000 10.756 440.816 140.940 70.795 70.760 260.862 150.888 70.739 120.763 70.774 91.000 10.929 110.878 170.879 71.000 10.819 7
OneFormer3Dcopyleft0.896 11.000 11.000 10.913 40.858 40.951 30.786 90.837 130.916 70.908 20.778 40.803 20.750 101.000 10.976 20.926 40.882 50.995 390.849 1
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
SIM3D0.842 141.000 10.998 30.608 570.717 410.908 140.818 40.699 360.798 280.908 10.760 60.733 130.793 71.000 10.912 130.831 410.883 41.000 10.792 10
CSC-Pretrained0.791 261.000 10.996 40.829 230.767 240.889 220.600 240.819 170.770 350.594 420.620 350.541 360.700 151.000 10.941 40.889 110.763 251.000 10.526 51
SPFormerpermissive0.851 111.000 10.994 50.806 300.774 220.942 60.637 210.849 110.859 170.889 50.720 150.730 140.665 191.000 10.911 190.868 270.873 131.000 10.796 9
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
HAISpermissive0.803 231.000 10.994 50.820 240.759 270.855 320.554 330.882 60.827 240.615 370.676 230.638 230.646 311.000 10.912 130.797 540.767 230.994 400.726 22
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
MTML0.731 411.000 10.992 70.779 390.609 530.746 510.308 520.867 70.601 530.607 390.539 480.519 400.550 461.000 10.824 370.869 250.729 341.000 10.616 39
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
RPGN0.806 221.000 10.992 70.789 320.723 390.891 200.650 200.810 200.832 220.665 310.699 200.658 200.700 151.000 10.881 250.832 400.774 220.997 330.613 41
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Spherical Mask(CtoF)0.875 31.000 10.991 90.873 120.850 50.946 50.691 180.752 270.926 40.889 60.759 80.794 40.820 21.000 10.912 130.900 70.878 91.000 10.769 14
MAFT0.860 101.000 10.990 100.810 280.829 90.949 40.809 50.688 390.836 200.904 30.751 110.796 30.741 111.000 10.864 310.848 360.837 171.000 10.828 3
Mask3D0.870 71.000 10.985 110.782 370.818 130.938 80.760 100.749 280.923 50.877 80.760 70.785 50.820 21.000 10.912 130.864 290.878 90.983 450.825 4
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SSEC0.820 201.000 10.983 120.924 30.826 100.817 430.415 470.899 40.793 300.673 290.731 140.636 240.653 201.000 10.939 60.804 510.878 81.000 10.780 12
TopoSeg0.832 171.000 10.981 130.933 20.819 120.826 400.524 380.841 120.811 250.681 270.759 90.687 180.727 120.981 360.911 190.883 130.853 161.000 10.756 19
UniPerception0.884 21.000 10.979 140.872 130.869 20.892 190.806 60.890 50.835 210.892 40.755 100.811 10.779 80.955 390.951 30.876 180.914 10.997 330.840 2
GICN0.788 281.000 10.978 150.867 140.781 210.833 370.527 370.824 150.806 260.549 500.596 380.551 320.700 151.000 10.853 320.935 20.733 331.000 10.651 32
Queryformer0.874 51.000 10.978 160.809 290.876 10.936 90.702 150.716 320.920 60.875 90.766 50.772 60.818 41.000 10.995 10.916 50.892 21.000 10.767 15
TD3Dpermissive0.875 31.000 10.976 170.877 100.783 200.970 10.889 10.828 140.945 30.803 140.713 160.720 160.709 131.000 10.936 90.934 30.873 121.000 10.791 11
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
SoftGroup++0.874 51.000 10.972 180.947 10.839 80.898 180.556 320.913 20.881 130.756 160.828 20.748 100.821 11.000 10.937 80.937 10.887 31.000 10.821 5
SoftGrouppermissive0.865 91.000 10.969 190.860 150.860 30.913 130.558 290.899 30.911 80.760 150.828 10.736 120.802 60.981 360.919 120.875 190.877 111.000 10.820 6
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
IPCA-Inst0.851 111.000 10.968 200.884 90.842 70.862 310.693 170.812 190.888 120.677 280.783 30.698 170.807 51.000 10.911 190.865 280.865 141.000 10.757 18
Mask-Group0.792 251.000 10.968 210.812 250.766 250.864 270.460 410.815 180.888 110.598 410.651 280.639 220.600 390.918 420.941 40.896 90.721 361.000 10.723 23
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
PBNetpermissive0.825 191.000 10.963 220.837 200.843 60.865 260.822 20.647 420.878 140.733 180.639 310.683 190.650 221.000 10.853 320.870 240.820 201.000 10.744 20
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023
SphereSeg0.835 151.000 10.963 230.891 70.794 170.954 20.822 30.710 330.961 20.721 200.693 220.530 390.653 211.000 10.867 300.857 320.859 150.991 420.771 13
Box2Mask0.803 231.000 10.962 240.874 110.707 440.887 230.686 190.598 470.961 10.715 220.694 210.469 440.700 151.000 10.912 130.902 60.753 290.997 330.637 35
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask3D_evaluation0.843 131.000 10.955 250.847 170.795 160.932 100.750 120.780 240.891 100.818 110.737 130.633 260.703 141.000 10.902 230.870 230.820 190.941 530.805 8
ISBNetpermissive0.835 151.000 10.950 260.731 460.819 110.918 110.790 80.740 290.851 190.831 100.661 240.742 110.650 221.000 10.937 70.814 490.836 181.000 10.765 16
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
NeuralBF0.718 441.000 10.945 270.901 50.754 290.817 420.460 410.700 350.772 330.688 250.568 430.000 660.500 530.981 360.606 570.872 210.740 321.000 10.614 40
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
AOIA0.767 341.000 10.937 280.810 270.740 350.906 150.550 340.800 230.706 420.577 470.624 330.544 350.596 440.857 450.879 290.880 150.750 300.992 410.658 31
DANCENET0.786 291.000 10.936 290.783 350.737 360.852 340.742 130.647 420.765 370.811 120.624 340.579 290.632 361.000 10.909 220.898 80.696 410.944 490.601 44
Dyco3Dcopyleft0.761 371.000 10.935 300.893 60.752 320.863 290.600 240.588 480.742 390.641 330.633 320.546 340.550 460.857 450.789 460.853 330.762 260.987 430.699 27
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
3D-MPA0.737 401.000 10.933 310.785 330.794 180.831 380.279 550.588 480.695 450.616 360.559 450.556 310.650 221.000 10.809 410.875 200.696 421.000 10.608 43
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
DKNet0.815 211.000 10.930 320.844 180.765 260.915 120.534 360.805 210.805 270.807 130.654 250.763 80.650 221.000 10.794 440.881 140.766 241.000 10.758 17
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
DENet0.786 291.000 10.929 330.736 450.750 330.720 560.755 110.934 10.794 290.590 430.561 440.537 370.650 221.000 10.882 240.804 520.789 211.000 10.719 24
DualGroup0.782 311.000 10.927 340.811 260.772 230.853 330.631 230.805 210.773 320.613 380.611 360.610 270.650 220.835 530.881 250.879 160.750 311.000 10.675 30
SSEN0.724 431.000 10.926 350.781 380.661 480.845 350.596 270.529 540.764 380.653 320.489 550.461 450.500 530.859 440.765 470.872 220.761 271.000 10.577 45
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Sparse R-CNN0.714 451.000 10.926 360.694 490.699 460.890 210.636 220.516 550.693 460.743 170.588 400.369 510.601 380.594 590.800 420.886 120.676 470.986 440.546 48
OccuSeg+instance0.742 381.000 10.923 370.785 330.745 340.867 250.557 300.578 510.729 400.670 300.644 300.488 420.577 451.000 10.794 440.830 420.620 541.000 10.550 47
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
INS-Conv-instance0.762 361.000 10.923 370.765 400.785 190.905 160.600 240.655 410.646 490.683 260.647 290.530 380.650 221.000 10.824 370.830 420.693 440.944 490.644 34
SegGroup_inspermissive0.637 541.000 10.923 390.593 590.561 570.746 520.143 640.504 560.766 360.485 580.442 560.372 500.530 490.714 540.815 400.775 560.673 481.000 10.431 57
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
GraphCut0.832 171.000 10.922 400.724 480.798 150.902 170.701 160.856 90.859 160.715 210.706 170.748 90.640 331.000 10.934 100.862 300.880 61.000 10.729 21
Occipital-SCS0.688 481.000 10.913 410.730 470.737 370.743 530.442 440.855 100.655 480.546 510.546 470.263 540.508 520.889 430.568 580.771 570.705 390.889 590.625 37
UNet-backbone0.605 561.000 10.909 420.764 410.603 540.704 570.415 460.301 610.548 580.461 590.394 570.267 530.386 590.857 450.649 550.817 460.504 580.959 470.356 61
Region-18class0.497 600.250 670.902 430.689 500.540 580.747 500.276 560.610 450.268 660.489 570.348 580.000 660.243 660.220 650.663 530.814 480.459 620.928 570.496 55
PointGroup0.778 321.000 10.900 440.798 310.715 420.863 280.493 390.706 340.895 90.569 480.701 180.576 300.639 341.000 10.880 270.851 340.719 370.997 330.709 26
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]
PE0.776 331.000 10.900 450.860 150.728 380.869 240.400 480.857 80.774 310.568 490.701 190.602 280.646 310.933 410.843 350.890 100.691 450.997 330.709 25
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RWSeg0.739 391.000 10.899 460.759 420.753 310.823 410.282 530.691 380.658 470.582 460.594 390.547 330.628 371.000 10.795 430.868 260.728 351.000 10.692 28
DD-UNet+Group0.764 351.000 10.897 470.837 190.753 300.830 390.459 430.824 150.699 440.629 350.653 260.438 470.650 221.000 10.880 270.858 310.690 461.000 10.650 33
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
PCJC0.684 511.000 10.895 480.757 430.659 490.862 300.189 620.739 300.606 520.712 230.581 410.515 410.650 220.857 450.357 630.785 550.631 510.889 590.635 36
3D-BoNet0.687 491.000 10.887 490.836 210.587 560.643 630.550 340.620 440.724 410.522 550.501 530.243 550.512 511.000 10.751 490.807 500.661 490.909 580.612 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
OSIS0.725 421.000 10.885 500.653 540.657 500.801 440.576 280.695 370.828 230.698 240.534 490.457 460.500 530.857 450.831 360.841 380.627 521.000 10.619 38
SPG_WSIS0.678 521.000 10.880 510.836 210.701 450.727 550.273 570.607 460.706 430.541 530.515 520.174 580.600 390.857 450.716 510.846 370.711 381.000 10.506 52
SALoss-ResNet0.695 461.000 10.855 520.579 600.589 550.735 540.484 400.588 480.856 180.634 340.571 420.298 520.500 531.000 10.824 370.818 450.702 400.935 560.545 49
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)
PanopticFusion-inst0.693 471.000 10.852 530.655 530.616 520.788 460.334 500.763 250.771 340.457 600.555 460.652 210.518 500.857 450.765 470.732 600.631 500.944 490.577 46
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SSTNetpermissive0.789 271.000 10.840 540.888 80.717 400.835 360.717 140.684 400.627 500.724 190.652 270.727 150.600 391.000 10.912 130.822 440.757 281.000 10.691 29
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
One_Thing_One_Clickpermissive0.675 531.000 10.823 550.782 360.621 510.766 480.211 590.736 310.560 570.586 440.522 500.636 250.453 570.641 570.853 320.850 350.694 430.997 330.411 58
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
ClickSeg_Instance0.685 501.000 10.818 560.600 580.715 430.795 450.557 300.533 530.591 550.601 400.519 510.429 490.638 350.938 400.706 520.817 470.624 530.944 490.502 53
MASCpermissive0.615 550.711 620.802 570.540 610.757 280.777 470.029 650.577 520.588 560.521 560.600 370.436 480.534 480.697 550.616 560.838 390.526 560.980 460.534 50
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.558 571.000 10.773 580.614 560.503 600.691 590.200 600.412 570.498 610.546 520.311 620.103 620.600 390.857 450.382 600.799 530.445 640.938 550.371 59
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SemRegionNet-20cls0.470 631.000 10.727 590.447 640.481 610.678 600.024 660.380 590.518 590.440 610.339 590.128 600.350 600.429 620.212 660.711 620.465 610.833 630.290 65
Hier3Dcopyleft0.540 591.000 10.727 590.626 550.467 630.693 580.200 600.412 570.480 620.528 540.318 610.077 650.600 390.688 560.382 600.768 580.472 600.941 530.350 62
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp0.474 621.000 10.727 590.433 650.481 620.673 610.022 670.380 590.517 600.436 620.338 600.128 600.343 610.429 620.291 650.728 610.473 590.833 630.300 64
ASIS0.422 640.333 660.707 620.676 510.401 640.650 620.350 490.177 650.594 540.376 630.202 640.077 640.404 580.571 600.197 670.674 640.447 630.500 660.260 66
3D-BEVIS0.401 650.667 630.687 630.419 660.137 670.587 650.188 630.235 620.359 640.211 660.093 670.080 630.311 620.571 600.382 600.754 590.300 660.874 610.357 60
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
R-PointNet0.544 580.500 650.655 640.661 520.663 470.765 490.432 450.214 640.612 510.584 450.499 540.204 570.286 630.429 620.655 540.650 650.539 550.950 480.499 54
Sgpn_scannet0.390 660.556 640.636 650.493 620.353 650.539 660.271 580.160 660.450 630.359 650.178 650.146 590.250 650.143 660.347 640.698 630.436 650.667 650.331 63
Sem_Recon_ins0.484 610.764 610.608 660.470 630.521 590.637 640.311 510.218 630.348 650.365 640.223 630.222 560.258 640.629 580.734 500.596 660.509 570.858 620.444 56
MaskRCNN 2d->3d Proj0.261 670.903 600.081 670.008 670.233 660.175 670.280 540.106 670.150 670.203 670.175 660.480 430.218 670.143 660.542 590.404 670.153 670.393 670.049 67


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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 20.512 10.422 170.898 10.723 10.714 10.954 20.454 10.509 10.773 10.895 10.756 10.820 10.653 10.935 10.891 10.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 220.648 40.463 30.549 20.742 70.676 20.628 20.961 10.420 20.379 60.684 70.381 170.732 30.723 30.599 20.827 150.851 20.634 7
BPNet_2Dcopyleft0.670 20.822 30.795 30.836 20.659 30.481 20.451 130.769 40.656 30.567 40.931 30.395 60.390 50.700 40.534 40.689 100.770 20.574 30.865 80.831 30.675 5
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MVF-GNN(2D)0.636 30.606 130.794 40.434 160.688 10.337 70.464 120.798 30.632 50.589 30.908 80.420 20.329 120.743 20.594 20.738 20.676 50.527 40.906 20.818 60.715 3
FAN_NV_RVC0.586 100.510 200.764 50.079 250.620 80.330 100.494 80.753 50.573 90.556 50.884 150.405 40.303 160.718 30.452 120.672 130.658 70.509 50.898 50.813 80.727 2
RFBNet0.592 90.616 100.758 60.659 50.581 110.330 100.469 110.655 170.543 140.524 80.924 40.355 120.336 110.572 160.479 90.671 140.648 90.480 100.814 180.814 70.614 10
MCA-Net0.595 80.533 190.756 70.746 40.590 100.334 90.506 70.670 140.587 80.500 120.905 100.366 100.352 90.601 120.506 70.669 160.648 90.501 70.839 140.769 140.516 20
EMSANet0.600 70.716 40.746 80.395 180.614 90.382 50.523 40.713 100.571 110.503 100.922 60.404 50.397 40.655 80.400 150.626 200.663 60.469 130.900 40.827 40.577 13
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
DMMF_3d0.605 60.651 90.744 90.782 30.637 50.387 40.536 30.732 80.590 70.540 60.856 200.359 110.306 150.596 130.539 30.627 190.706 40.497 80.785 200.757 180.476 21
EMSAFormer0.564 150.581 150.736 100.564 100.546 150.219 220.517 50.675 130.486 190.427 210.904 110.352 130.320 130.589 140.528 50.708 70.464 230.413 210.847 130.786 100.611 11
CMX0.613 50.681 80.725 110.502 120.634 60.297 170.478 100.830 20.651 40.537 70.924 40.375 70.315 140.686 60.451 130.714 50.543 200.504 60.894 60.823 50.688 4
DCRedNet0.583 110.682 70.723 120.542 110.510 190.310 140.451 130.668 150.549 130.520 90.920 70.375 70.446 20.528 190.417 140.670 150.577 170.478 110.862 90.806 90.628 9
AdapNet++copyleft0.503 200.613 110.722 130.418 170.358 250.337 70.370 220.479 230.443 210.368 230.907 90.207 220.213 240.464 230.525 60.618 210.657 80.450 160.788 190.721 220.408 24
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
SSMAcopyleft0.577 130.695 50.716 140.439 140.563 130.314 130.444 150.719 90.551 120.503 100.887 140.346 150.348 100.603 110.353 190.709 60.600 140.457 140.901 30.786 100.599 12
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
MSeg1080_RVCpermissive0.485 220.505 210.709 150.092 240.427 220.241 210.411 190.654 180.385 250.457 180.861 190.053 250.279 180.503 210.481 80.645 170.626 120.365 230.748 230.725 210.529 19
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
MIX6D_RVC0.582 120.695 50.687 160.225 200.632 70.328 120.550 10.748 60.623 60.494 150.890 130.350 140.254 220.688 50.454 110.716 40.597 160.489 90.881 70.768 150.575 14
segfomer with 6d0.542 180.594 140.687 160.146 230.579 120.308 150.515 60.703 120.472 200.498 130.868 170.369 90.282 170.589 140.390 160.701 90.556 190.416 200.860 110.759 170.539 18
UNIV_CNP_RVC_UE0.566 140.569 180.686 180.435 150.524 160.294 180.421 180.712 110.543 140.463 170.872 160.320 160.363 80.611 100.477 100.686 110.627 110.443 170.862 90.775 130.639 6
FuseNetpermissive0.535 190.570 170.681 190.182 210.512 180.290 190.431 160.659 160.504 180.495 140.903 120.308 180.428 30.523 200.365 180.676 120.621 130.470 120.762 210.779 120.541 16
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
SN_RN152pyrx8_RVCcopyleft0.546 160.572 160.663 200.638 70.518 170.298 160.366 230.633 200.510 170.446 190.864 180.296 190.267 190.542 180.346 200.704 80.575 180.431 180.853 120.766 160.630 8
UDSSEG_RVC0.545 170.610 120.661 210.588 80.556 140.268 200.482 90.642 190.572 100.475 160.836 220.312 170.367 70.630 90.189 220.639 180.495 220.452 150.826 160.756 190.541 16
3DMV (2d proj)0.498 210.481 230.612 220.579 90.456 210.343 60.384 200.623 210.525 160.381 220.845 210.254 210.264 210.557 170.182 230.581 230.598 150.429 190.760 220.661 240.446 23
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ILC-PSPNet0.475 230.490 220.581 230.289 190.507 200.067 250.379 210.610 220.417 230.435 200.822 240.278 200.267 190.503 210.228 210.616 220.533 210.375 220.820 170.729 200.560 15
ScanNet (2d proj)permissive0.330 250.293 240.521 240.657 60.361 240.161 240.250 240.004 250.440 220.183 250.836 220.125 240.060 250.319 250.132 240.417 240.412 240.344 240.541 250.427 250.109 25
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
Enet (reimpl)0.376 240.264 250.452 250.452 130.365 230.181 230.143 250.456 240.409 240.346 240.769 250.164 230.218 230.359 240.123 250.403 250.381 250.313 250.571 240.685 230.472 22
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
DMMF0.003 260.000 260.005 260.000 260.000 260.037 260.001 260.000 260.001 260.005 260.003 260.000 260.000 260.000 260.000 260.000 260.002 260.001 260.000 260.006 260.000 26


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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
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EMSANet (Instance)0.241 10.401 10.439 10.085 10.242 10.220 10.081 10.289 10.117 20.121 10.182 10.126 10.346 10.181 10.181 20.358 10.156 10.675 20.131 1
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
UniDet_RVC0.205 20.381 20.323 20.037 20.226 20.177 20.063 20.277 20.120 10.067 20.131 20.074 30.317 20.080 20.235 10.289 20.141 20.678 10.080 2
MaskRCNN_ScanNetpermissive0.119 30.129 30.212 30.002 30.112 30.148 30.014 30.205 30.044 30.066 30.078 30.095 20.142 30.030 30.128 30.139 30.080 30.459 30.057 3
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17


This table lists the benchmark results for the scene type classification scenario.




Method Infoavg recallapartmentbathroombedroom / hotelbookstore / libraryconference roomcopy/mail roomhallwaykitchenlaundry roomliving room / loungemiscofficestorage / basement / garage
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LAST-PCL-type0.780 10.250 31.000 11.000 11.000 11.000 11.000 10.500 21.000 10.500 20.889 10.000 21.000 11.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, and Jian Zhang: Language-Assisted 3D Scene Understanding. arxiv23.12
3DASPP-SCE0.691 30.500 10.938 30.824 41.000 11.000 10.500 31.000 10.857 30.500 20.556 40.000 20.812 30.500 2
multi-taskpermissive0.700 20.500 11.000 10.882 30.500 31.000 11.000 10.500 21.000 11.000 10.778 20.000 20.938 20.000 3
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
SE-ResNeXt-SSMA0.498 40.000 50.812 40.941 20.500 30.500 40.500 30.500 20.429 50.500 20.667 30.500 10.625 40.000 3
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 50.250 30.812 40.529 50.500 30.500 40.000 50.500 20.571 40.000 50.556 40.000 20.375 50.000 3