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

ScanNet200 3D Semantic Label 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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PonderV2 ScanNet2000.346 50.552 70.270 70.175 80.810 70.682 90.950 40.560 60.641 90.761 30.398 120.357 90.570 70.113 20.804 50.603 60.750 60.283 40.681 60.952 50.548 50.874 40.852 120.290 110.700 20.356 100.792 40.445 110.545 120.436 110.351 120.787 90.611 70.050 80.290 130.519 120.000 10.825 90.888 40.842 30.259 30.100 20.558 60.070 110.497 70.247 130.457 100.889 30.248 80.106 90.817 120.691 60.094 60.729 60.636 60.620 110.503 100.660 120.243 60.000 30.212 60.590 40.860 70.400 50.881 80.000 60.202 20.622 90.408 100.499 80.261 100.000 10.385 90.636 100.000 40.000 90.000 10.000 50.433 150.843 60.660 60.574 110.481 30.336 40.677 80.486 50.000 30.030 30.000 10.034 50.000 30.080 60.869 90.000 10.000 90.000 100.540 90.727 30.232 160.115 100.186 90.193 70.000 130.403 110.326 60.103 130.000 40.290 30.392 80.000 30.346 90.062 90.424 40.375 60.431 50.667 40.115 130.082 110.239 60.000 10.504 130.606 80.584 110.000 10.002 80.186 80.104 90.000 70.394 40.384 70.083 80.000 70.007 80.000 10.000 40.880 40.000 10.377 90.000 10.263 50.565 20.000 10.608 80.000 10.000 10.304 70.009 90.924 30.000 90.000 100.000 10.000 70.000 10.128 30.584 30.475 60.412 70.076 90.269 30.621 50.509 80.010 70.000 10.491 100.063 10.000 20.472 40.880 20.000 20.000 10.000 10.179 40.125 20.000 30.441 80.000 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.
IMFSegNet0.334 80.532 120.251 100.179 60.799 100.683 80.940 100.555 70.631 110.740 100.406 90.336 120.560 80.062 40.795 70.518 110.733 90.274 50.646 120.947 80.458 160.848 130.862 90.305 90.649 40.284 120.713 120.495 70.626 70.527 80.363 90.820 50.574 120.010 140.411 40.597 60.000 10.842 30.873 90.704 130.246 40.000 70.495 100.041 150.486 90.305 60.444 110.604 140.134 150.055 150.852 80.633 120.076 80.792 40.612 80.573 160.484 110.668 110.216 110.000 30.197 80.518 50.784 120.344 110.908 60.283 30.190 40.599 120.439 90.496 100.569 20.000 10.392 80.776 20.000 40.064 40.000 10.000 50.710 80.756 120.508 100.512 150.159 140.034 130.773 10.363 90.000 30.000 70.000 10.032 60.000 30.029 150.648 150.000 10.000 90.000 100.830 50.595 40.274 140.228 70.206 70.188 110.000 130.425 90.237 110.123 110.000 40.277 50.214 140.003 10.610 20.044 110.124 90.320 130.408 110.594 90.196 60.213 60.139 120.000 10.615 60.618 60.839 30.000 10.014 50.260 40.080 110.025 20.000 80.139 110.135 50.035 60.000 100.000 10.793 10.799 80.000 10.357 120.000 10.369 40.359 120.000 10.512 140.000 10.000 10.120 110.424 10.903 80.027 50.091 50.000 10.245 40.000 10.073 140.457 130.340 110.191 140.021 140.009 160.322 140.608 50.060 30.000 10.494 90.000 20.000 20.068 100.624 100.000 20.000 10.000 10.139 100.047 40.000 30.561 50.000 1
GSTran0.334 90.533 110.250 110.179 70.799 100.684 70.940 100.554 80.633 100.741 90.405 100.337 110.560 80.060 50.794 80.517 120.732 100.274 50.647 110.948 70.459 150.849 110.864 80.306 80.648 50.282 130.717 110.496 60.624 80.533 70.363 90.821 40.573 130.009 150.411 40.593 80.000 10.841 40.873 90.704 130.242 50.000 70.495 100.041 150.487 80.304 70.439 120.613 120.133 160.055 150.853 70.634 110.075 110.791 50.601 90.574 150.483 120.669 100.217 90.000 30.198 70.518 50.782 130.345 100.914 50.273 40.193 30.598 130.440 80.499 80.570 10.000 10.381 100.775 30.000 40.063 50.000 10.000 50.712 70.752 130.507 110.512 150.158 150.036 120.773 10.361 100.000 30.000 70.000 10.032 60.000 30.032 140.651 140.000 10.000 90.000 100.831 40.595 40.273 150.229 60.200 80.191 80.000 130.425 90.233 120.125 100.000 40.279 40.213 150.003 10.608 30.044 110.138 80.321 110.408 110.593 100.198 40.205 70.139 120.000 10.614 70.609 70.838 40.000 10.014 50.260 40.080 110.010 50.000 80.136 120.136 40.047 50.000 100.000 10.787 20.797 90.000 10.354 130.000 10.372 30.357 130.000 10.507 150.000 10.000 10.121 100.423 20.903 80.028 40.089 60.000 10.252 30.000 10.072 150.465 120.340 110.189 150.020 150.011 150.320 150.606 60.060 30.000 10.496 80.000 20.000 20.070 90.618 120.000 20.000 10.000 10.139 100.047 40.000 30.558 60.000 1
DITR0.409 20.616 10.351 10.215 30.831 30.791 10.947 50.619 10.730 20.762 20.494 20.571 10.597 20.000 120.853 10.625 30.796 20.301 30.723 30.959 40.617 20.862 70.917 30.573 10.562 100.591 10.784 70.504 50.757 10.737 20.429 40.853 10.662 30.135 30.459 30.558 110.000 10.913 10.878 60.687 150.008 150.000 70.615 40.238 10.651 10.370 30.742 20.925 20.360 10.167 40.938 10.752 20.118 30.827 10.670 40.723 20.614 30.628 140.372 10.000 30.143 120.175 160.873 30.652 10.991 10.340 10.148 80.814 10.656 10.524 60.491 40.000 10.743 10.752 40.000 40.000 90.000 10.399 10.865 20.953 10.833 10.694 20.444 60.000 160.688 60.609 20.000 30.053 20.000 10.022 110.000 30.053 110.940 30.000 10.186 10.093 50.854 20.877 10.534 20.404 10.270 30.191 80.198 40.461 70.375 10.152 30.921 10.132 90.235 120.000 30.617 10.330 10.896 10.399 50.431 50.597 80.759 10.554 30.400 20.000 10.559 100.699 10.852 20.000 10.000 100.091 100.385 10.000 70.000 80.478 40.077 90.000 70.140 40.000 10.000 40.670 130.000 10.452 40.000 10.263 50.361 110.000 10.643 40.000 10.000 10.357 50.005 110.928 20.362 10.496 10.000 10.000 70.000 10.072 150.585 20.587 30.476 40.037 130.191 50.410 120.629 40.118 10.000 10.479 110.000 20.000 20.107 70.839 30.000 20.000 10.000 10.139 100.036 60.000 30.247 90.000 1
BFANet ScanNet200permissive0.360 40.553 60.293 40.193 40.827 40.689 40.970 30.528 120.661 60.753 60.436 70.378 70.469 140.042 70.810 30.654 10.760 40.266 90.659 90.973 30.574 40.849 110.897 40.382 20.546 120.372 80.698 130.491 80.617 90.526 90.436 10.764 130.476 160.101 60.409 60.585 90.000 10.835 50.901 30.810 50.102 120.000 70.688 20.096 50.483 100.264 110.612 80.591 150.358 20.161 50.863 50.707 40.128 20.814 20.669 50.629 90.563 40.651 130.258 40.000 30.194 90.494 80.806 110.394 60.953 40.000 60.233 10.757 40.508 50.556 30.476 50.000 10.573 50.741 60.000 40.000 90.000 10.000 50.000 160.852 50.678 30.616 50.460 40.338 30.710 40.534 40.000 30.025 40.000 10.043 20.000 30.056 100.493 160.000 10.000 90.109 40.785 60.590 60.298 120.282 40.143 120.262 40.053 100.526 40.337 50.215 10.000 40.135 80.510 40.000 30.596 40.043 130.511 30.321 110.459 20.772 20.124 120.060 130.266 50.000 10.574 90.568 90.653 100.000 10.093 10.298 20.239 20.000 70.516 20.129 130.284 20.000 70.431 10.000 10.000 40.848 60.000 10.492 10.000 10.376 20.522 50.000 10.469 160.000 10.000 10.330 60.151 80.875 140.000 90.254 30.000 10.000 70.000 10.088 110.661 10.481 40.255 110.105 10.139 100.666 40.641 30.000 120.000 10.614 20.000 20.000 20.000 110.921 10.000 20.000 10.000 10.497 10.000 80.000 30.000 100.000 1
ALS-MinkowskiNetcopyleft0.414 10.610 20.322 30.271 10.852 10.710 20.973 10.572 30.719 30.795 10.477 50.506 20.601 10.000 120.804 50.646 20.804 10.344 20.777 10.984 10.671 10.879 20.936 10.342 40.632 70.449 30.817 30.475 90.723 20.798 10.376 80.832 20.693 10.031 90.564 10.510 130.000 10.893 20.905 10.672 160.314 10.000 70.718 10.153 20.542 20.397 20.726 30.752 80.252 70.226 10.916 20.800 10.047 150.807 30.769 10.709 30.630 20.769 10.217 90.000 30.285 10.598 30.846 90.535 20.956 30.000 60.137 110.784 20.464 60.463 130.230 110.000 10.598 30.662 90.000 40.087 20.000 10.135 20.900 10.780 110.703 20.741 10.571 20.149 90.697 50.646 10.000 30.076 10.000 10.025 90.000 30.106 40.981 10.000 10.043 60.113 30.888 10.248 150.404 40.252 50.314 10.220 50.245 10.466 60.366 20.159 20.000 40.149 70.690 20.000 30.531 50.253 20.285 50.460 10.440 40.813 10.230 20.283 50.159 100.000 10.728 10.666 50.958 10.000 10.021 40.252 60.118 40.000 70.445 30.223 100.285 10.194 30.390 20.000 10.475 30.842 70.000 10.455 30.000 10.250 70.458 70.000 10.865 10.000 10.000 10.635 10.359 40.972 10.087 30.447 20.000 10.000 70.000 10.129 20.532 60.446 70.503 30.071 110.135 120.699 30.717 10.097 20.000 10.665 10.000 20.000 21.000 10.752 50.000 20.000 10.000 10.142 80.200 10.259 11.000 10.000 1
PTv3 ScanNet2000.393 30.592 30.330 20.216 20.851 20.687 60.971 20.586 20.755 10.752 70.505 10.404 60.575 40.000 120.848 20.616 40.761 30.349 10.738 20.978 20.546 60.860 80.926 20.346 30.654 30.384 60.828 10.523 30.699 30.583 50.387 70.822 30.688 20.118 50.474 20.603 40.000 10.832 70.903 20.753 80.140 90.000 70.650 30.109 40.520 30.457 10.497 90.871 40.281 30.192 30.887 40.748 30.168 10.727 70.733 20.740 10.644 10.714 40.190 120.000 30.256 30.449 90.914 10.514 30.759 140.337 20.172 60.692 60.617 20.636 10.325 70.000 10.641 20.782 10.000 40.065 30.000 10.000 50.842 30.903 20.661 40.662 40.612 10.405 20.731 30.566 30.000 30.000 70.000 10.017 140.301 10.088 50.941 20.000 10.077 30.000 100.717 70.790 20.310 110.026 160.264 40.349 10.220 30.397 120.366 20.115 120.000 40.337 10.463 60.000 30.531 50.218 30.593 20.455 20.469 10.708 30.210 30.592 20.108 150.000 10.728 10.682 30.671 80.000 10.000 100.407 10.136 30.022 30.575 10.436 50.259 30.428 10.048 50.000 10.000 40.879 50.000 10.480 20.000 10.133 90.597 10.000 10.690 20.000 10.000 10.009 150.000 140.921 40.000 90.151 40.000 10.000 70.000 10.109 70.494 110.622 20.394 80.073 100.141 80.798 10.528 70.026 50.000 10.551 40.000 20.000 20.134 60.717 70.000 20.000 10.000 10.188 30.000 80.000 30.791 20.000 1
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
L3DETR-ScanNet_2000.336 70.533 100.279 50.155 90.801 90.689 40.946 60.539 100.660 70.759 40.380 130.333 130.583 30.000 120.788 100.529 90.740 70.261 110.679 80.940 120.525 100.860 80.883 60.226 120.613 90.397 50.720 100.512 40.565 110.620 30.417 50.775 120.629 50.158 20.298 110.579 100.000 10.835 50.883 50.927 10.114 100.079 40.511 90.073 100.508 50.312 50.629 50.861 50.192 130.098 120.908 30.636 100.032 160.563 160.514 140.664 50.505 90.697 60.225 80.000 30.264 20.411 110.860 70.321 120.960 20.058 50.109 130.776 30.526 40.557 20.303 90.000 10.339 110.712 70.000 40.014 70.000 10.000 50.638 110.856 40.641 70.579 100.107 160.119 110.661 100.416 60.000 30.000 70.000 10.007 160.000 30.067 80.910 50.000 10.000 90.000 100.463 100.448 80.294 130.324 20.293 20.211 60.108 70.448 80.068 160.141 60.000 40.330 20.699 10.000 30.256 100.192 50.000 140.355 70.418 70.209 160.146 110.679 10.101 160.000 10.503 140.687 20.671 80.000 10.000 100.174 90.117 50.000 70.122 60.515 20.104 60.259 20.312 30.000 10.000 40.765 110.000 10.369 110.000 10.183 80.422 100.000 10.646 30.000 10.000 10.565 20.001 130.125 160.010 70.002 90.000 10.487 10.000 10.075 120.548 40.420 80.233 130.082 70.138 110.430 100.427 120.000 120.000 10.549 50.000 20.000 20.074 80.409 150.000 20.000 10.000 10.152 60.051 30.000 30.598 40.000 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet2000.333 100.558 40.269 80.124 120.821 50.703 30.946 60.569 40.662 40.748 80.487 30.455 30.572 60.000 120.789 90.534 80.736 80.271 70.713 40.949 60.498 130.877 30.860 100.332 60.706 10.474 20.788 60.406 120.637 50.495 100.355 110.805 70.592 110.015 130.396 70.602 50.000 10.799 100.876 70.713 120.276 20.000 70.493 120.080 80.448 140.363 40.661 40.833 60.262 50.125 60.823 110.665 80.076 80.720 80.557 100.637 80.517 80.672 90.227 70.000 30.158 110.496 70.843 100.352 90.835 120.000 60.103 140.711 50.527 30.526 50.320 80.000 10.568 60.625 110.067 10.000 90.000 10.001 40.806 50.836 70.621 90.591 70.373 80.314 50.668 90.398 80.003 20.000 70.000 10.016 150.024 20.043 120.906 60.000 10.052 50.000 100.384 110.330 120.342 70.100 110.223 60.183 120.112 60.476 50.313 70.130 90.196 30.112 110.370 100.000 30.234 110.071 80.160 60.403 40.398 130.492 140.197 50.076 120.272 40.000 10.200 160.560 100.735 70.000 10.000 100.000 110.110 70.002 60.021 70.412 60.000 110.000 70.000 100.000 10.000 40.794 100.000 10.445 50.000 10.022 100.509 60.000 10.517 120.000 10.000 10.001 160.245 50.915 60.024 60.089 60.000 10.262 20.000 10.103 90.524 70.392 100.515 20.013 160.251 40.411 110.662 20.001 110.000 10.473 120.000 20.000 20.150 50.699 80.000 20.000 10.000 10.166 50.000 80.024 20.000 100.000 1
PPT-SpUNet-F.T.0.332 110.556 50.270 60.123 130.816 60.682 90.946 60.549 90.657 80.756 50.459 60.376 80.550 100.001 110.807 40.616 40.727 110.267 80.691 50.942 110.530 90.872 50.874 70.330 70.542 130.374 70.792 40.400 130.673 40.572 60.433 20.793 80.623 60.008 160.351 90.594 70.000 10.783 120.876 70.833 40.213 60.000 70.537 70.091 60.519 40.304 70.620 70.942 10.264 40.124 70.855 60.695 50.086 70.646 100.506 150.658 60.535 60.715 30.314 20.000 30.241 40.608 20.897 20.359 80.858 100.000 60.076 160.611 100.392 110.509 70.378 60.000 10.579 40.565 150.000 40.000 90.000 10.000 50.755 60.806 90.661 40.572 120.350 90.181 70.660 110.300 130.000 30.000 70.000 10.023 100.000 30.042 130.930 40.000 10.000 90.077 70.584 80.392 100.339 80.185 90.171 110.308 20.006 120.563 30.256 80.150 40.000 40.002 150.345 110.000 30.045 130.197 40.063 100.323 100.453 30.600 70.163 100.037 140.349 30.000 10.672 30.679 40.753 50.000 10.000 100.000 110.117 50.000 70.000 80.291 90.000 110.000 70.039 60.000 10.000 40.899 20.000 10.374 100.000 10.000 120.545 40.000 10.634 50.000 10.000 10.074 120.223 60.914 70.000 90.021 80.000 10.000 70.000 10.112 50.498 100.649 10.383 90.095 20.135 120.449 90.432 110.008 90.000 10.518 60.000 20.000 20.000 110.796 40.000 20.000 10.000 10.138 130.000 80.000 30.000 100.000 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
OctFormer ScanNet200permissive0.326 120.539 90.265 90.131 110.806 80.670 120.943 90.535 110.662 40.705 150.423 80.407 50.505 120.003 100.765 120.582 70.686 140.227 150.680 70.943 100.601 30.854 100.892 50.335 50.417 160.357 90.724 90.453 100.632 60.596 40.432 30.783 100.512 150.021 120.244 140.637 10.000 10.787 110.873 90.743 100.000 160.000 70.534 80.110 30.499 60.289 90.626 60.620 110.168 140.204 20.849 90.679 70.117 40.633 110.684 30.650 70.552 50.684 80.312 30.000 30.175 100.429 100.865 40.413 40.837 110.000 60.145 90.626 80.451 70.487 110.513 30.000 10.529 70.613 120.000 40.033 60.000 10.000 50.828 40.871 30.622 80.587 80.411 70.137 100.645 130.343 110.000 30.000 70.000 10.022 110.000 30.026 160.829 100.000 10.022 70.089 60.842 30.253 140.318 100.296 30.178 100.291 30.224 20.584 20.200 130.132 80.000 40.128 100.227 130.000 30.230 120.047 100.149 70.331 90.412 90.618 60.164 90.102 100.522 10.000 10.655 40.378 120.469 140.000 10.000 100.000 110.105 80.000 70.000 80.483 30.000 110.000 70.028 70.000 10.000 40.906 10.000 10.339 140.000 10.000 120.457 80.000 10.612 70.000 10.000 10.408 30.000 140.900 100.000 90.000 100.000 10.029 60.000 10.074 130.455 140.479 50.427 60.079 80.140 90.496 70.414 130.022 60.000 10.471 130.000 20.000 20.000 110.722 60.000 20.000 10.000 10.138 130.000 80.000 30.000 100.000 1
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CeCo0.340 60.551 80.247 120.181 50.784 120.661 130.939 120.564 50.624 120.721 110.484 40.429 40.575 40.027 80.774 110.503 130.753 50.242 120.656 100.945 90.534 70.865 60.860 100.177 160.616 80.400 40.818 20.579 10.615 100.367 130.408 60.726 140.633 40.162 10.360 80.619 20.000 10.828 80.873 90.924 20.109 110.083 30.564 50.057 140.475 120.266 100.781 10.767 70.257 60.100 100.825 100.663 90.048 140.620 130.551 110.595 120.532 70.692 70.246 50.000 30.213 50.615 10.861 60.376 70.900 70.000 60.102 150.660 70.321 140.547 40.226 120.000 10.311 120.742 50.011 30.006 80.000 10.000 50.546 140.824 80.345 130.665 30.450 50.435 10.683 70.411 70.338 10.000 70.000 10.030 80.000 30.068 70.892 70.000 10.063 40.000 100.257 120.304 130.387 50.079 130.228 50.190 100.000 130.586 10.347 40.133 70.000 40.037 120.377 90.000 30.384 80.006 150.003 120.421 30.410 100.643 50.171 80.121 80.142 110.000 10.510 120.447 110.474 130.000 10.000 100.286 30.083 100.000 70.000 80.603 10.096 70.063 40.000 100.000 10.000 40.898 30.000 10.429 60.000 10.400 10.550 30.000 10.633 60.000 10.000 10.377 40.000 140.916 50.000 90.000 100.000 10.000 70.000 10.102 100.499 90.296 130.463 50.089 50.304 10.740 20.401 150.010 70.000 10.560 30.000 20.000 20.709 20.652 90.000 20.000 10.000 10.143 70.000 80.000 30.609 30.000 1
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
AWCS0.305 130.508 130.225 130.142 100.782 130.634 160.937 130.489 140.578 130.721 110.364 140.355 100.515 110.023 90.764 130.523 100.707 130.264 100.633 130.922 130.507 120.886 10.804 140.179 140.436 150.300 110.656 150.529 20.501 140.394 120.296 150.820 50.603 80.131 40.179 160.619 20.000 10.707 150.865 130.773 60.171 70.010 60.484 130.063 120.463 130.254 120.332 150.649 100.220 100.100 100.729 140.613 140.071 120.582 140.628 70.702 40.424 140.749 20.137 140.000 30.142 130.360 120.863 50.305 130.877 90.000 60.173 50.606 110.337 130.478 120.154 140.000 10.253 130.664 80.000 40.000 90.000 10.000 50.626 120.782 100.302 150.602 60.185 120.282 60.651 120.317 120.000 30.000 70.000 10.022 110.000 30.154 10.876 80.000 10.014 80.063 90.029 160.553 70.467 30.084 120.124 130.157 150.049 110.373 130.252 90.097 140.000 40.219 60.542 30.000 30.392 70.172 70.000 140.339 80.417 80.533 130.093 140.115 90.195 80.000 10.516 110.288 150.741 60.000 10.001 90.233 70.056 130.000 70.159 50.334 80.077 90.000 70.000 100.000 10.000 40.749 120.000 10.411 70.000 10.008 110.452 90.000 10.595 90.000 10.000 10.220 90.006 100.894 120.006 80.000 100.000 10.000 70.000 10.112 50.504 80.404 90.551 10.093 40.129 140.484 80.381 160.000 120.000 10.396 140.000 20.000 20.620 30.402 160.000 20.000 10.000 10.142 80.000 80.000 30.512 70.000 1
LGroundpermissive0.272 140.485 140.184 140.106 140.778 140.676 110.932 140.479 160.572 140.718 130.399 110.265 140.453 150.085 30.745 140.446 140.726 120.232 140.622 140.901 140.512 110.826 140.786 150.178 150.549 110.277 140.659 140.381 140.518 130.295 160.323 130.777 110.599 90.028 100.321 100.363 150.000 10.708 140.858 140.746 90.063 130.022 50.457 140.077 90.476 110.243 140.402 130.397 160.233 90.077 140.720 160.610 150.103 50.629 120.437 160.626 100.446 130.702 50.190 120.005 10.058 150.322 130.702 150.244 140.768 130.000 60.134 120.552 140.279 150.395 140.147 150.000 10.207 140.612 130.000 40.000 90.000 10.000 50.658 100.566 140.323 140.525 140.229 110.179 80.467 160.154 150.000 30.002 50.000 10.051 10.000 30.127 20.703 110.000 10.000 90.216 10.112 150.358 110.547 10.187 80.092 150.156 160.055 90.296 140.252 90.143 50.000 40.014 130.398 70.000 30.028 150.173 60.000 140.265 150.348 140.415 150.179 70.019 150.218 70.000 10.597 80.274 160.565 120.000 10.012 70.000 110.039 150.022 30.000 80.117 140.000 110.000 70.000 100.000 10.000 40.324 150.000 10.384 80.000 10.000 120.251 160.000 10.566 100.000 10.000 10.066 130.404 30.886 130.199 20.000 100.000 10.059 50.000 10.136 10.540 50.127 160.295 100.085 60.143 70.514 60.413 140.000 120.000 10.498 70.000 20.000 20.000 110.623 110.000 20.000 10.000 10.132 150.000 80.000 30.000 100.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 160.455 160.171 150.079 160.766 160.659 140.930 160.494 130.542 160.700 160.314 160.215 160.430 160.121 10.697 160.441 150.683 150.235 130.609 160.895 150.476 140.816 150.770 160.186 130.634 60.216 160.734 80.340 150.471 150.307 150.293 160.591 160.542 140.076 70.205 150.464 140.000 10.484 160.832 160.766 70.052 140.000 70.413 150.059 130.418 150.222 150.318 160.609 130.206 120.112 80.743 130.625 130.076 80.579 150.548 120.590 130.371 150.552 160.081 150.003 20.142 130.201 150.638 160.233 150.686 160.000 60.142 100.444 160.375 120.247 160.198 130.000 10.128 160.454 160.019 20.097 10.000 10.000 50.553 130.557 150.373 120.545 130.164 130.014 150.547 150.174 140.000 30.002 50.000 10.037 30.000 30.063 90.664 130.000 10.000 90.130 20.170 130.152 160.335 90.079 130.110 140.175 130.098 80.175 160.166 140.045 160.207 20.014 130.465 50.000 30.001 160.001 160.046 110.299 140.327 150.537 120.033 150.012 160.186 90.000 10.205 150.377 130.463 150.000 10.058 30.000 110.055 140.041 10.000 80.105 150.000 110.000 70.000 100.000 10.000 40.398 140.000 10.308 160.000 10.000 120.319 140.000 10.543 110.000 10.000 10.062 140.004 120.862 150.000 90.000 100.000 10.000 70.000 10.123 40.316 150.225 140.250 120.094 30.180 60.332 130.441 100.000 120.000 10.310 160.000 20.000 20.000 110.592 130.000 20.000 10.000 10.203 20.000 80.000 30.000 100.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 150.463 150.154 160.102 150.771 150.650 150.932 140.483 150.571 150.710 140.331 150.250 150.492 130.044 60.703 150.419 160.606 160.227 150.621 150.865 160.531 80.771 160.813 130.291 100.484 140.242 150.612 160.282 160.440 160.351 140.299 140.622 150.593 100.027 110.293 120.310 160.000 10.757 130.858 140.737 110.150 80.164 10.368 160.084 70.381 160.142 160.357 140.720 90.214 110.092 130.724 150.596 160.056 130.655 90.525 130.581 140.352 160.594 150.056 160.000 30.014 160.224 140.772 140.205 160.720 150.000 60.159 70.531 150.163 160.294 150.136 160.000 10.169 150.589 140.000 40.000 90.000 10.002 30.663 90.466 160.265 160.582 90.337 100.016 140.559 140.084 160.000 30.000 70.000 10.036 40.000 30.125 30.670 120.000 10.102 20.071 80.164 140.406 90.386 60.046 150.068 160.159 140.117 50.284 150.111 150.094 150.000 40.000 160.197 160.000 30.044 140.013 140.002 130.228 160.307 160.588 110.025 160.545 40.134 140.000 10.655 40.302 140.282 160.000 10.060 20.000 110.035 160.000 70.000 80.097 160.000 110.000 70.005 90.000 10.000 40.096 160.000 10.334 150.000 10.000 120.274 150.000 10.513 130.000 10.000 10.280 80.194 70.897 110.000 90.000 100.000 10.000 70.000 10.108 80.279 160.189 150.141 160.059 120.272 20.307 160.445 90.003 100.000 10.353 150.000 20.026 10.000 110.581 140.001 10.000 10.000 10.093 160.002 70.000 30.000 100.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019


ScanNet200 3D Semantic Instance Benchmark

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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 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
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
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.


ScanNet Benchmark

3D Semantic Label Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PTv3-PPT-ALCcopyleft0.798 10.911 100.812 210.854 70.770 120.856 140.555 150.943 10.660 240.735 20.979 10.606 70.492 10.792 40.934 30.841 20.819 50.716 80.947 100.906 10.822 1
PTv3 ScanNet0.794 20.941 30.813 200.851 90.782 60.890 30.597 10.916 50.696 90.713 50.979 10.635 20.384 30.793 30.907 100.821 50.790 330.696 130.967 30.903 20.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
DITR ScanNet0.793 30.811 400.852 20.889 10.774 90.907 10.592 20.927 30.719 10.718 30.961 170.652 10.348 120.817 10.927 50.795 90.824 20.749 10.948 90.887 70.771 11
PonderV20.785 40.978 10.800 290.833 260.788 40.853 190.545 190.910 80.713 20.705 60.979 10.596 90.390 20.769 150.832 440.821 50.792 320.730 20.975 10.897 50.785 6
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 50.964 20.855 10.843 180.781 70.858 130.575 70.831 360.685 150.714 40.979 10.594 100.310 290.801 20.892 180.841 20.819 50.723 50.940 150.887 70.725 27
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
Swin3Dpermissive0.779 60.861 220.818 150.836 230.790 30.875 50.576 60.905 90.704 60.739 10.969 110.611 30.349 110.756 250.958 10.702 480.805 160.708 90.916 350.898 40.801 3
TTT-KD0.773 70.646 940.818 150.809 380.774 90.878 40.581 30.943 10.687 130.704 70.978 50.607 60.336 180.775 110.912 80.838 40.823 30.694 140.967 30.899 30.794 5
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 80.939 40.824 70.854 70.771 110.840 330.564 110.900 110.686 140.677 140.961 170.537 340.348 120.769 150.903 120.785 130.815 80.676 250.939 160.880 130.772 10
OctFormerpermissive0.766 90.925 70.808 250.849 110.786 50.846 290.566 100.876 180.690 110.674 160.960 190.576 200.226 700.753 270.904 110.777 150.815 80.722 60.923 300.877 160.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PPT-SpUNet-Joint0.766 90.932 50.794 350.829 280.751 250.854 170.540 230.903 100.630 370.672 170.963 150.565 240.357 90.788 50.900 140.737 280.802 170.685 190.950 70.887 70.780 7
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+Semantic0.764 110.758 610.796 330.839 210.746 280.907 10.562 120.850 280.680 170.672 170.978 50.610 40.335 200.777 90.819 480.847 10.830 10.691 160.972 20.885 100.727 25
CU-Hybrid Net0.764 110.924 80.819 130.840 200.757 200.853 190.580 40.848 290.709 40.643 270.958 230.587 150.295 360.753 270.884 220.758 220.815 80.725 40.927 260.867 250.743 18
O-CNNpermissive0.762 130.924 80.823 80.844 170.770 120.852 210.577 50.847 310.711 30.640 310.958 230.592 110.217 760.762 200.888 190.758 220.813 120.726 30.932 240.868 240.744 17
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
DiffSegNet0.758 140.725 770.789 400.843 180.762 160.856 140.562 120.920 40.657 270.658 210.958 230.589 130.337 170.782 60.879 230.787 110.779 380.678 210.926 280.880 130.799 4
DTC0.757 150.843 280.820 110.847 140.791 20.862 110.511 360.870 200.707 50.652 230.954 380.604 80.279 470.760 210.942 20.734 290.766 470.701 120.884 570.874 220.736 19
OA-CNN-L_ScanNet200.756 160.783 470.826 60.858 50.776 80.837 360.548 180.896 140.649 290.675 150.962 160.586 160.335 200.771 140.802 520.770 180.787 350.691 160.936 190.880 130.761 13
PNE0.755 170.786 450.835 50.834 250.758 180.849 240.570 90.836 350.648 300.668 190.978 50.581 190.367 70.683 380.856 320.804 70.801 210.678 210.961 50.889 60.716 32
P. Hermosilla: Point Neighborhood Embeddings.
ConDaFormer0.755 170.927 60.822 90.836 230.801 10.849 240.516 330.864 250.651 280.680 130.958 230.584 180.282 440.759 230.855 340.728 310.802 170.678 210.880 620.873 230.756 15
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
PointTransformerV20.752 190.742 680.809 240.872 20.758 180.860 120.552 160.891 160.610 440.687 80.960 190.559 280.304 320.766 180.926 60.767 190.797 250.644 360.942 130.876 190.722 29
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 190.906 130.793 370.802 440.689 430.825 490.556 140.867 210.681 160.602 470.960 190.555 300.365 80.779 80.859 290.747 250.795 290.717 70.917 340.856 330.764 12
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
PointConvFormer0.749 210.793 430.790 380.807 400.750 270.856 140.524 290.881 170.588 560.642 300.977 90.591 120.274 500.781 70.929 40.804 70.796 260.642 370.947 100.885 100.715 33
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 210.909 110.818 150.811 360.752 230.839 350.485 500.842 320.673 190.644 260.957 280.528 400.305 310.773 120.859 290.788 100.818 70.693 150.916 350.856 330.723 28
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 230.623 970.804 270.859 40.745 290.824 510.501 400.912 70.690 110.685 100.956 290.567 230.320 260.768 170.918 70.720 360.802 170.676 250.921 320.881 120.779 8
StratifiedFormerpermissive0.747 240.901 140.803 280.845 160.757 200.846 290.512 350.825 390.696 90.645 250.956 290.576 200.262 610.744 320.861 280.742 260.770 450.705 100.899 470.860 300.734 20
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 250.771 550.819 130.848 130.702 400.865 100.397 880.899 120.699 70.664 200.948 580.588 140.330 220.746 310.851 380.764 200.796 260.704 110.935 200.866 260.728 23
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 250.870 200.838 30.858 50.729 340.850 230.501 400.874 190.587 570.658 210.956 290.564 250.299 340.765 190.900 140.716 390.812 130.631 420.939 160.858 310.709 34
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
DiffSeg3D20.745 270.725 770.814 190.837 220.751 250.831 430.514 340.896 140.674 180.684 110.960 190.564 250.303 330.773 120.820 470.713 420.798 240.690 180.923 300.875 200.757 14
Retro-FPN0.744 280.842 290.800 290.767 580.740 300.836 380.541 210.914 60.672 200.626 350.958 230.552 310.272 520.777 90.886 210.696 490.801 210.674 280.941 140.858 310.717 30
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-Net0.743 290.620 980.799 320.849 110.730 330.822 530.493 470.897 130.664 210.681 120.955 320.562 270.378 40.760 210.903 120.738 270.801 210.673 290.907 390.877 160.745 16
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 300.816 370.806 260.807 400.752 230.828 470.575 70.839 340.699 70.637 320.954 380.520 430.320 260.755 260.834 420.760 210.772 420.676 250.915 370.862 280.717 30
SAT0.742 300.860 230.765 520.819 310.769 140.848 260.533 250.829 370.663 220.631 340.955 320.586 160.274 500.753 270.896 160.729 300.760 530.666 310.921 320.855 350.733 21
LargeKernel3D0.739 320.909 110.820 110.806 420.740 300.852 210.545 190.826 380.594 550.643 270.955 320.541 330.263 600.723 360.858 310.775 170.767 460.678 210.933 220.848 400.694 39
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
RPN0.736 330.776 510.790 380.851 90.754 220.854 170.491 490.866 230.596 540.686 90.955 320.536 350.342 150.624 530.869 250.787 110.802 170.628 430.927 260.875 200.704 36
MinkowskiNetpermissive0.736 330.859 240.818 150.832 270.709 380.840 330.521 310.853 270.660 240.643 270.951 480.544 320.286 420.731 340.893 170.675 580.772 420.683 200.874 690.852 380.727 25
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 350.890 160.837 40.864 30.726 350.873 60.530 280.824 400.489 900.647 240.978 50.609 50.336 180.624 530.733 610.758 220.776 400.570 680.949 80.877 160.728 23
SparseConvNet0.725 360.647 930.821 100.846 150.721 360.869 70.533 250.754 610.603 500.614 390.955 320.572 220.325 240.710 370.870 240.724 340.823 30.628 430.934 210.865 270.683 42
PointTransformer++0.725 360.727 760.811 230.819 310.765 150.841 320.502 390.814 450.621 400.623 370.955 320.556 290.284 430.620 550.866 260.781 140.757 570.648 340.932 240.862 280.709 34
MatchingNet0.724 380.812 390.812 210.810 370.735 320.834 400.495 460.860 260.572 640.602 470.954 380.512 450.280 460.757 240.845 400.725 330.780 370.606 530.937 180.851 390.700 38
INS-Conv-semantic0.717 390.751 640.759 560.812 350.704 390.868 80.537 240.842 320.609 460.608 430.953 420.534 370.293 370.616 560.864 270.719 380.793 300.640 380.933 220.845 440.663 48
PointMetaBase0.714 400.835 300.785 410.821 290.684 450.846 290.531 270.865 240.614 410.596 510.953 420.500 480.246 660.674 390.888 190.692 500.764 490.624 450.849 850.844 450.675 44
contrastBoundarypermissive0.705 410.769 580.775 460.809 380.687 440.820 560.439 760.812 460.661 230.591 530.945 660.515 440.171 950.633 500.856 320.720 360.796 260.668 300.889 540.847 410.689 40
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
ClickSeg_Semantic0.703 420.774 530.800 290.793 490.760 170.847 280.471 540.802 490.463 970.634 330.968 130.491 510.271 540.726 350.910 90.706 440.815 80.551 800.878 630.833 460.570 80
RFCR0.702 430.889 170.745 670.813 340.672 480.818 610.493 470.815 440.623 380.610 410.947 600.470 600.249 650.594 590.848 390.705 450.779 380.646 350.892 520.823 520.611 63
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 Click0.701 440.825 340.796 330.723 650.716 370.832 420.433 780.816 420.634 350.609 420.969 110.418 860.344 140.559 710.833 430.715 400.808 150.560 740.902 440.847 410.680 43
JSENetpermissive0.699 450.881 190.762 530.821 290.667 490.800 730.522 300.792 520.613 420.607 440.935 860.492 500.205 810.576 640.853 360.691 520.758 550.652 330.872 720.828 490.649 52
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-Click0.693 460.743 670.794 350.655 880.684 450.822 530.497 450.719 710.622 390.617 380.977 90.447 730.339 160.750 300.664 780.703 470.790 330.596 580.946 120.855 350.647 53
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 470.732 720.772 470.786 500.677 470.866 90.517 320.848 290.509 830.626 350.952 460.536 350.225 720.545 770.704 690.689 550.810 140.564 730.903 430.854 370.729 22
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 480.884 180.754 600.795 470.647 560.818 610.422 800.802 490.612 430.604 450.945 660.462 630.189 890.563 700.853 360.726 320.765 480.632 410.904 410.821 550.606 67
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 490.704 830.741 710.754 620.656 510.829 450.501 400.741 660.609 460.548 610.950 520.522 420.371 50.633 500.756 560.715 400.771 440.623 460.861 800.814 580.658 49
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 500.866 210.748 640.819 310.645 580.794 760.450 660.802 490.587 570.604 450.945 660.464 620.201 840.554 730.840 410.723 350.732 680.602 560.907 390.822 540.603 70
DGNet0.684 510.712 820.784 420.782 540.658 500.835 390.499 440.823 410.641 320.597 500.950 520.487 530.281 450.575 650.619 820.647 710.764 490.620 480.871 750.846 430.688 41
VACNN++0.684 510.728 750.757 590.776 550.690 410.804 710.464 590.816 420.577 630.587 540.945 660.508 470.276 490.671 400.710 670.663 630.750 610.589 630.881 600.832 480.653 51
KP-FCNN0.684 510.847 270.758 580.784 520.647 560.814 640.473 530.772 550.605 480.594 520.935 860.450 710.181 920.587 600.805 510.690 530.785 360.614 490.882 590.819 560.632 59
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointContrast_LA_SEM0.683 540.757 620.784 420.786 500.639 600.824 510.408 830.775 540.604 490.541 630.934 900.532 380.269 560.552 740.777 540.645 740.793 300.640 380.913 380.824 510.671 45
Superpoint Network0.683 540.851 260.728 750.800 460.653 530.806 690.468 560.804 470.572 640.602 470.946 630.453 700.239 690.519 830.822 450.689 550.762 520.595 600.895 500.827 500.630 60
VI-PointConv0.676 560.770 570.754 600.783 530.621 640.814 640.552 160.758 590.571 660.557 590.954 380.529 390.268 580.530 800.682 730.675 580.719 710.603 550.888 550.833 460.665 47
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 570.789 440.748 640.763 600.635 620.814 640.407 850.747 630.581 610.573 560.950 520.484 540.271 540.607 570.754 570.649 680.774 410.596 580.883 580.823 520.606 67
SALANet0.670 580.816 370.770 500.768 570.652 540.807 680.451 630.747 630.659 260.545 620.924 970.473 590.149 1050.571 670.811 500.635 770.746 620.623 460.892 520.794 710.570 80
O3DSeg0.668 590.822 350.771 490.496 1090.651 550.833 410.541 210.761 580.555 720.611 400.966 140.489 520.370 60.388 1020.580 850.776 160.751 590.570 680.956 60.817 570.646 54
PointConvpermissive0.666 600.781 480.759 560.699 730.644 590.822 530.475 520.779 530.564 690.504 800.953 420.428 800.203 830.586 620.754 570.661 640.753 580.588 640.902 440.813 600.642 55
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 600.703 840.781 440.751 640.655 520.830 440.471 540.769 560.474 930.537 650.951 480.475 580.279 470.635 480.698 720.675 580.751 590.553 790.816 920.806 620.703 37
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++permissive0.663 620.746 650.708 780.722 660.638 610.820 560.451 630.566 990.599 520.541 630.950 520.510 460.313 280.648 450.819 480.616 820.682 860.590 620.869 760.810 610.656 50
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 630.778 490.702 810.806 420.619 650.813 670.468 560.693 790.494 860.524 720.941 780.449 720.298 350.510 850.821 460.675 580.727 700.568 710.826 900.803 650.637 57
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MVF-GNN0.658 630.558 1050.751 620.655 880.690 410.722 980.453 620.867 210.579 620.576 550.893 1090.523 410.293 370.733 330.571 870.692 500.659 930.606 530.875 660.804 640.668 46
HPGCNN0.656 650.698 860.743 690.650 900.564 820.820 560.505 380.758 590.631 360.479 840.945 660.480 560.226 700.572 660.774 550.690 530.735 660.614 490.853 840.776 860.597 73
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 660.752 630.734 730.664 860.583 770.815 630.399 870.754 610.639 330.535 670.942 760.470 600.309 300.665 410.539 890.650 670.708 760.635 400.857 830.793 730.642 55
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 670.778 490.731 740.699 730.577 780.829 450.446 680.736 670.477 920.523 740.945 660.454 670.269 560.484 920.749 600.618 800.738 630.599 570.827 890.792 760.621 62
PointConv-SFPN0.641 680.776 510.703 800.721 670.557 850.826 480.451 630.672 850.563 700.483 830.943 750.425 830.162 1000.644 460.726 620.659 650.709 750.572 670.875 660.786 810.559 86
MVPNetpermissive0.641 680.831 310.715 760.671 830.590 730.781 820.394 890.679 820.642 310.553 600.937 830.462 630.256 620.649 440.406 1020.626 780.691 830.666 310.877 640.792 760.608 66
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 700.717 810.701 820.692 760.576 790.801 720.467 580.716 720.563 700.459 900.953 420.429 790.169 970.581 630.854 350.605 830.710 730.550 810.894 510.793 730.575 78
FPConvpermissive0.639 710.785 460.760 550.713 710.603 680.798 740.392 900.534 1040.603 500.524 720.948 580.457 650.250 640.538 780.723 650.598 870.696 810.614 490.872 720.799 660.567 83
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 720.797 420.769 510.641 950.590 730.820 560.461 600.537 1030.637 340.536 660.947 600.388 930.206 800.656 420.668 760.647 710.732 680.585 650.868 770.793 730.473 106
PointSPNet0.637 730.734 710.692 890.714 700.576 790.797 750.446 680.743 650.598 530.437 950.942 760.403 890.150 1040.626 520.800 530.649 680.697 800.557 770.846 860.777 850.563 84
SConv0.636 740.830 320.697 850.752 630.572 810.780 840.445 700.716 720.529 760.530 680.951 480.446 740.170 960.507 870.666 770.636 760.682 860.541 870.886 560.799 660.594 74
Supervoxel-CNN0.635 750.656 910.711 770.719 680.613 660.757 930.444 730.765 570.534 750.566 570.928 950.478 570.272 520.636 470.531 910.664 620.645 970.508 950.864 790.792 760.611 63
joint point-basedpermissive0.634 760.614 990.778 450.667 850.633 630.825 490.420 810.804 470.467 950.561 580.951 480.494 490.291 390.566 680.458 970.579 940.764 490.559 760.838 870.814 580.598 72
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 770.731 730.688 920.675 800.591 720.784 810.444 730.565 1000.610 440.492 810.949 560.456 660.254 630.587 600.706 680.599 860.665 920.612 520.868 770.791 790.579 77
PointNet2-SFPN0.631 780.771 550.692 890.672 810.524 900.837 360.440 750.706 770.538 740.446 920.944 720.421 850.219 750.552 740.751 590.591 900.737 640.543 860.901 460.768 890.557 87
APCF-Net0.631 780.742 680.687 940.672 810.557 850.792 790.408 830.665 860.545 730.508 770.952 460.428 800.186 900.634 490.702 700.620 790.706 770.555 780.873 700.798 680.581 76
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF0.631 780.626 960.745 670.801 450.607 670.751 940.506 370.729 700.565 680.491 820.866 1120.434 750.197 870.595 580.630 810.709 430.705 780.560 740.875 660.740 970.491 101
FusionAwareConv0.630 810.604 1010.741 710.766 590.590 730.747 950.501 400.734 680.503 850.527 700.919 1010.454 670.323 250.550 760.420 1010.678 570.688 840.544 840.896 490.795 700.627 61
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 820.800 410.625 1040.719 680.545 870.806 690.445 700.597 940.448 1000.519 750.938 820.481 550.328 230.489 910.499 960.657 660.759 540.592 610.881 600.797 690.634 58
SegGroup_sempermissive0.627 830.818 360.747 660.701 720.602 690.764 900.385 940.629 910.490 880.508 770.931 940.409 880.201 840.564 690.725 630.618 800.692 820.539 880.873 700.794 710.548 90
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 840.830 320.694 870.757 610.563 830.772 880.448 670.647 890.520 790.509 760.949 560.431 780.191 880.496 890.614 830.647 710.672 900.535 900.876 650.783 820.571 79
dtc_net0.625 840.703 840.751 620.794 480.535 880.848 260.480 510.676 840.528 770.469 870.944 720.454 670.004 1170.464 940.636 800.704 460.758 550.548 830.924 290.787 800.492 100
HPEIN0.618 860.729 740.668 950.647 920.597 710.766 890.414 820.680 810.520 790.525 710.946 630.432 760.215 770.493 900.599 840.638 750.617 1020.570 680.897 480.806 620.605 69
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-GCNpermissive0.610 870.858 250.772 470.489 1100.532 890.792 790.404 860.643 900.570 670.507 790.935 860.414 870.046 1140.510 850.702 700.602 850.705 780.549 820.859 810.773 870.534 93
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 880.760 600.667 960.649 910.521 910.793 770.457 610.648 880.528 770.434 970.947 600.401 900.153 1030.454 950.721 660.648 700.717 720.536 890.904 410.765 900.485 102
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
Weakly-Openseg v30.604 890.901 140.762 530.627 970.478 970.820 560.346 1000.689 800.353 1100.528 690.933 910.217 1150.172 940.530 800.725 630.593 890.737 640.515 920.858 820.772 880.515 96
wsss-transformer0.600 900.634 950.743 690.697 750.601 700.781 820.437 770.585 970.493 870.446 920.933 910.394 910.011 1160.654 430.661 790.603 840.733 670.526 910.832 880.761 920.480 103
LAP-D0.594 910.720 790.692 890.637 960.456 1010.773 870.391 920.730 690.587 570.445 940.940 800.381 940.288 400.434 980.453 990.591 900.649 950.581 660.777 960.749 960.610 65
DPC0.592 920.720 790.700 830.602 1010.480 960.762 920.380 950.713 750.585 600.437 950.940 800.369 960.288 400.434 980.509 950.590 920.639 1000.567 720.772 970.755 940.592 75
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 930.766 590.659 990.683 780.470 1000.740 970.387 930.620 930.490 880.476 850.922 990.355 990.245 670.511 840.511 940.571 950.643 980.493 990.872 720.762 910.600 71
ROSMRF0.580 940.772 540.707 790.681 790.563 830.764 900.362 970.515 1050.465 960.465 890.936 850.427 820.207 790.438 960.577 860.536 980.675 890.486 1000.723 1030.779 830.524 95
SD-DETR0.576 950.746 650.609 1080.445 1140.517 920.643 1090.366 960.714 740.456 980.468 880.870 1110.432 760.264 590.558 720.674 740.586 930.688 840.482 1010.739 1010.733 990.537 92
SQN_0.1%0.569 960.676 880.696 860.657 870.497 930.779 850.424 790.548 1010.515 810.376 1020.902 1080.422 840.357 90.379 1030.456 980.596 880.659 930.544 840.685 1060.665 1100.556 88
TextureNetpermissive0.566 970.672 900.664 970.671 830.494 940.719 990.445 700.678 830.411 1060.396 1000.935 860.356 980.225 720.412 1000.535 900.565 960.636 1010.464 1030.794 950.680 1070.568 82
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
DVVNet0.562 980.648 920.700 830.770 560.586 760.687 1030.333 1020.650 870.514 820.475 860.906 1050.359 970.223 740.340 1050.442 1000.422 1090.668 910.501 960.708 1040.779 830.534 93
Pointnet++ & Featurepermissive0.557 990.735 700.661 980.686 770.491 950.744 960.392 900.539 1020.451 990.375 1030.946 630.376 950.205 810.403 1010.356 1050.553 970.643 980.497 970.824 910.756 930.515 96
GMLPs0.538 1000.495 1100.693 880.647 920.471 990.793 770.300 1050.477 1060.505 840.358 1040.903 1070.327 1020.081 1110.472 930.529 920.448 1070.710 730.509 930.746 990.737 980.554 89
PanopticFusion-label0.529 1010.491 1110.688 920.604 1000.386 1060.632 1100.225 1160.705 780.434 1030.293 1100.815 1140.348 1000.241 680.499 880.669 750.507 1000.649 950.442 1090.796 940.602 1140.561 85
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 1020.676 880.591 1110.609 980.442 1020.774 860.335 1010.597 940.422 1050.357 1050.932 930.341 1010.094 1100.298 1070.528 930.473 1050.676 880.495 980.602 1120.721 1020.349 114
Online SegFusion0.515 1030.607 1000.644 1020.579 1030.434 1030.630 1110.353 980.628 920.440 1010.410 980.762 1170.307 1040.167 980.520 820.403 1030.516 990.565 1050.447 1070.678 1070.701 1040.514 98
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 1040.558 1050.608 1090.424 1160.478 970.690 1020.246 1120.586 960.468 940.450 910.911 1030.394 910.160 1010.438 960.212 1120.432 1080.541 1100.475 1020.742 1000.727 1000.477 104
PCNN0.498 1050.559 1040.644 1020.560 1050.420 1050.711 1010.229 1140.414 1070.436 1020.352 1060.941 780.324 1030.155 1020.238 1120.387 1040.493 1010.529 1110.509 930.813 930.751 950.504 99
3DMV0.484 1060.484 1120.538 1140.643 940.424 1040.606 1140.310 1030.574 980.433 1040.378 1010.796 1150.301 1050.214 780.537 790.208 1130.472 1060.507 1140.413 1120.693 1050.602 1140.539 91
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 1070.577 1030.611 1070.356 1180.321 1140.715 1000.299 1070.376 1110.328 1140.319 1080.944 720.285 1070.164 990.216 1150.229 1100.484 1030.545 1090.456 1050.755 980.709 1030.475 105
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 1080.679 870.604 1100.578 1040.380 1070.682 1040.291 1080.106 1180.483 910.258 1160.920 1000.258 1110.025 1150.231 1140.325 1060.480 1040.560 1070.463 1040.725 1020.666 1090.231 118
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 1090.474 1130.623 1050.463 1120.366 1090.651 1070.310 1030.389 1100.349 1120.330 1070.937 830.271 1090.126 1070.285 1080.224 1110.350 1140.577 1040.445 1080.625 1100.723 1010.394 110
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 1100.548 1070.548 1130.597 1020.363 1100.628 1120.300 1050.292 1130.374 1080.307 1090.881 1100.268 1100.186 900.238 1120.204 1140.407 1100.506 1150.449 1060.667 1080.620 1130.462 108
SurfaceConvPF0.442 1100.505 1090.622 1060.380 1170.342 1120.654 1060.227 1150.397 1090.367 1090.276 1120.924 970.240 1120.198 860.359 1040.262 1080.366 1110.581 1030.435 1100.640 1090.668 1080.398 109
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 1120.437 1150.646 1010.474 1110.369 1080.645 1080.353 980.258 1150.282 1170.279 1110.918 1020.298 1060.147 1060.283 1090.294 1070.487 1020.562 1060.427 1110.619 1110.633 1120.352 113
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 1130.525 1080.647 1000.522 1060.324 1130.488 1180.077 1190.712 760.353 1100.401 990.636 1190.281 1080.176 930.340 1050.565 880.175 1180.551 1080.398 1130.370 1190.602 1140.361 112
SPLAT Netcopyleft0.393 1140.472 1140.511 1150.606 990.311 1150.656 1050.245 1130.405 1080.328 1140.197 1170.927 960.227 1140.000 1190.001 1200.249 1090.271 1170.510 1120.383 1150.593 1130.699 1050.267 116
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 1150.297 1170.491 1160.432 1150.358 1110.612 1130.274 1100.116 1170.411 1060.265 1130.904 1060.229 1130.079 1120.250 1100.185 1150.320 1150.510 1120.385 1140.548 1140.597 1170.394 110
PointNet++permissive0.339 1160.584 1020.478 1170.458 1130.256 1170.360 1190.250 1110.247 1160.278 1180.261 1150.677 1180.183 1160.117 1080.212 1160.145 1170.364 1120.346 1190.232 1190.548 1140.523 1180.252 117
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
GrowSP++0.323 1170.114 1190.589 1120.499 1080.147 1190.555 1150.290 1090.336 1120.290 1160.262 1140.865 1130.102 1190.000 1190.037 1180.000 1200.000 1200.462 1160.381 1160.389 1180.664 1110.473 106
SSC-UNetpermissive0.308 1180.353 1160.290 1190.278 1190.166 1180.553 1160.169 1180.286 1140.147 1190.148 1190.908 1040.182 1170.064 1130.023 1190.018 1190.354 1130.363 1170.345 1170.546 1160.685 1060.278 115
ScanNetpermissive0.306 1190.203 1180.366 1180.501 1070.311 1150.524 1170.211 1170.002 1200.342 1130.189 1180.786 1160.145 1180.102 1090.245 1110.152 1160.318 1160.348 1180.300 1180.460 1170.437 1190.182 119
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 1200.000 1200.041 1200.172 1200.030 1200.062 1200.001 1200.035 1190.004 1200.051 1200.143 1200.019 1200.003 1180.041 1170.050 1180.003 1190.054 1200.018 1200.005 1200.264 1200.082 120


3D Semantic Instance Benchmark

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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PointRel0.901 11.000 10.978 220.928 30.879 10.962 40.882 30.749 350.947 30.912 10.802 30.753 160.820 21.000 10.984 40.919 50.894 31.000 10.815 13
: Relation3D (PointRel): Enhancing Relation Modeling for Point Cloud Instance Segmentation.
OneFormer3Dcopyleft0.896 21.000 11.000 10.913 60.858 60.951 80.786 130.837 180.916 120.908 30.778 70.803 50.750 131.000 10.976 60.926 40.882 70.995 460.849 1
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation.
MG-Former0.887 31.000 10.991 130.837 240.801 220.935 170.887 20.857 100.946 40.891 90.748 160.805 40.739 151.000 10.993 20.809 560.876 141.000 10.842 3
UniPerception0.884 41.000 10.979 190.872 160.869 30.892 260.806 100.890 60.835 280.892 80.755 120.811 10.779 100.955 460.951 70.876 220.914 10.997 380.840 4
KmaxOneFormerNetpermissive0.883 51.000 11.000 10.798 380.848 100.971 10.853 40.903 30.827 310.910 20.748 150.809 30.724 171.000 10.980 50.855 380.844 221.000 10.832 5
InsSSM0.883 51.000 10.996 50.800 370.865 40.960 50.808 90.852 150.940 60.899 70.785 40.810 20.700 201.000 10.912 180.851 410.895 20.997 380.827 7
Lei Yao, Yi Wang, Moyun Liu, Lap-Pui Chau: SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation. TCSVT, 2024
Competitor-SPFormer0.881 71.000 11.000 10.845 220.854 70.962 30.714 200.857 110.904 140.902 50.782 60.789 100.662 261.000 10.988 30.874 250.886 60.997 380.847 2
TST3D0.879 81.000 10.994 80.921 50.807 210.939 140.771 140.887 70.923 100.862 160.722 210.768 130.756 121.000 10.910 280.904 70.836 250.999 370.824 9
Duc Tran Dang Trung, Byeongkeun Kang, Yeejin Lee: MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation. ACM Multimedia 2024
SIM3D0.878 91.000 10.972 240.863 180.817 190.952 70.821 70.783 290.890 170.902 60.735 190.797 60.799 91.000 10.931 150.893 130.853 201.000 10.792 16
EV3D0.877 101.000 10.996 70.873 140.854 80.950 90.691 240.783 300.926 70.889 120.754 130.794 90.820 21.000 10.912 180.900 90.860 181.000 10.779 19
Spherical Mask(CtoF)0.875 111.000 10.991 140.873 140.850 90.946 110.691 240.752 340.926 70.889 110.759 100.794 80.820 21.000 10.912 180.900 90.878 111.000 10.769 21
TD3Dpermissive0.875 111.000 10.976 230.877 120.783 280.970 20.889 10.828 190.945 50.803 210.713 230.720 230.709 181.000 10.936 130.934 30.873 151.000 10.791 17
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024
Queryformer0.874 131.000 10.978 210.809 350.876 20.936 160.702 210.716 400.920 110.875 150.766 80.772 120.818 61.000 10.995 10.916 60.892 41.000 10.767 22
SoftGroup++0.874 131.000 10.972 250.947 10.839 130.898 250.556 390.913 20.881 200.756 230.828 20.748 180.821 11.000 10.937 120.937 10.887 51.000 10.821 10
Mask3D0.870 151.000 10.985 160.782 450.818 180.938 150.760 150.749 350.923 90.877 140.760 90.785 110.820 21.000 10.912 180.864 340.878 110.983 520.825 8
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ExtMask3D0.867 161.000 11.000 10.756 520.816 200.940 130.795 110.760 330.862 220.888 130.739 170.763 140.774 111.000 10.929 160.878 210.879 91.000 10.819 12
SoftGrouppermissive0.865 171.000 10.969 260.860 190.860 50.913 210.558 360.899 40.911 130.760 220.828 10.736 200.802 80.981 430.919 170.875 230.877 131.000 10.820 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]
MAFT0.860 181.000 10.990 150.810 340.829 140.949 100.809 80.688 460.836 270.904 40.751 140.796 70.741 141.000 10.864 380.848 430.837 231.000 10.828 6
SPFormerpermissive0.851 191.000 10.994 90.806