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 ioualarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefloorfolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwallwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CeCo0.340 10.551 20.247 20.181 10.475 20.057 60.142 50.000 10.000 10.000 10.387 30.463 30.499 40.924 10.774 20.213 10.257 20.000 30.546 60.100 30.006 20.615 10.177 60.534 10.246 10.000 40.400 20.000 10.338 10.006 50.484 20.609 10.000 10.083 20.000 20.873 20.089 30.661 30.000 30.048 60.560 10.408 10.892 20.000 10.000 10.586 10.616 30.000 50.692 30.900 10.721 20.162 10.228 10.860 10.000 10.000 20.575 10.083 20.550 10.347 10.624 20.410 20.360 20.740 10.109 40.321 40.660 20.000 20.121 20.939 20.143 30.000 10.400 10.003 30.190 10.564 10.652 20.615 20.421 10.304 50.579 10.547 10.000 20.000 10.296 30.000 50.030 40.096 10.000 30.916 10.037 30.551 30.171 30.376 10.865 30.286 10.000 10.633 10.102 60.027 40.011 30.000 10.000 20.474 40.742 10.133 20.311 20.824 20.242 30.503 30.068 40.828 10.000 30.429 20.000 10.063 10.000 20.781 10.000 10.000 30.000 10.665 10.633 10.450 10.818 10.000 30.000 10.429 20.532 10.226 20.825 10.510 40.377 10.709 10.079 40.000 10.753 10.683 10.102 60.063 20.401 50.620 40.000 10.619 10.000 60.000 40.000 10.595 40.000 20.000 10.345 30.564 20.411 10.603 10.384 20.945 20.266 20.643 10.367 30.304 10.663 20.000 10.010 10.726 40.767 20.898 10.000 10.784 20.435 10.861 20.000 10.447 20.000 60.257 20.656 20.377 4
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
Minkowski 34Dpermissive0.253 50.463 50.154 60.102 50.381 60.084 10.134 60.000 10.000 10.000 10.386 40.141 60.279 60.737 50.703 50.014 60.164 40.000 30.663 20.092 50.000 30.224 50.291 20.531 20.056 60.000 40.242 50.000 10.000 30.013 40.331 50.000 30.000 10.035 60.001 10.858 40.059 50.650 50.000 30.056 50.353 50.299 40.670 50.000 10.000 10.284 50.484 50.071 30.594 50.720 50.710 50.027 50.068 60.813 30.000 10.005 10.492 40.164 10.274 50.111 60.571 50.307 60.293 40.307 60.150 30.163 60.531 50.002 10.545 10.932 40.093 60.000 10.000 40.002 40.159 40.368 60.581 50.440 60.228 60.406 20.282 60.294 50.000 20.000 10.189 50.060 10.036 30.000 30.000 30.897 30.000 60.525 50.025 60.205 60.771 60.000 30.000 10.593 40.108 40.044 30.000 40.000 10.000 20.282 60.589 50.094 50.169 50.466 60.227 60.419 60.125 30.757 30.002 10.334 50.000 10.000 20.000 20.357 40.000 10.000 30.000 10.582 40.513 60.337 30.612 60.000 30.000 10.250 50.352 60.136 60.724 50.655 10.280 20.000 40.046 60.000 10.606 60.559 40.159 20.102 10.445 20.655 20.000 10.310 60.117 10.000 40.000 10.581 60.026 10.000 10.265 60.483 50.084 60.097 60.044 40.865 60.142 60.588 20.351 40.272 20.596 60.000 10.003 20.622 50.720 30.096 60.000 10.771 50.016 50.772 40.000 10.302 40.194 30.214 50.621 50.197 6
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrainpermissive0.249 60.455 60.171 50.079 60.418 50.059 50.186 40.000 10.000 10.000 10.335 60.250 50.316 50.766 30.697 60.142 30.170 30.003 20.553 50.112 20.097 10.201 60.186 30.476 60.081 50.000 40.216 60.000 10.000 30.001 60.314 60.000 30.000 10.055 40.000 20.832 60.094 10.659 40.002 10.076 20.310 60.293 60.664 60.000 10.000 10.175 60.634 20.130 20.552 60.686 60.700 60.076 30.110 40.770 60.000 10.000 20.430 60.000 50.319 40.166 50.542 60.327 50.205 50.332 50.052 60.375 20.444 60.000 20.012 60.930 60.203 10.000 10.000 40.046 20.175 30.413 50.592 40.471 50.299 40.152 60.340 50.247 60.000 20.000 10.225 40.058 20.037 20.000 30.207 10.862 60.014 40.548 40.033 50.233 50.816 50.000 30.000 10.542 60.123 20.121 10.019 20.000 10.000 20.463 50.454 60.045 60.128 60.557 50.235 40.441 50.063 50.484 60.000 30.308 60.000 10.000 20.000 20.318 60.000 10.000 30.000 10.545 50.543 40.164 60.734 30.000 30.000 10.215 60.371 50.198 30.743 30.205 50.062 50.000 40.079 40.000 10.683 50.547 50.142 30.000 50.441 30.579 60.000 10.464 40.098 30.041 10.000 10.590 50.000 20.000 10.373 20.494 30.174 40.105 50.001 60.895 50.222 50.537 30.307 50.180 40.625 30.000 10.000 40.591 60.609 50.398 40.000 10.766 60.014 60.638 60.000 10.377 30.004 50.206 60.609 60.465 2
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
AWCS0.305 30.508 30.225 30.142 20.463 30.063 40.195 30.000 10.000 10.000 10.467 20.551 10.504 30.773 20.764 30.142 30.029 60.000 30.626 40.100 30.000 30.360 30.179 40.507 40.137 40.006 30.300 30.000 10.000 30.172 20.364 40.512 20.000 10.056 30.000 20.865 30.093 20.634 60.000 30.071 40.396 40.296 50.876 30.000 10.000 10.373 30.436 60.063 40.749 10.877 20.721 20.131 20.124 30.804 40.000 10.000 20.515 30.010 40.452 30.252 30.578 30.417 10.179 60.484 30.171 20.337 30.606 30.000 20.115 30.937 30.142 40.000 10.008 30.000 50.157 50.484 30.402 60.501 40.339 30.553 10.529 20.478 30.000 20.000 10.404 10.001 40.022 50.077 20.000 30.894 40.219 10.628 10.093 40.305 30.886 10.233 20.000 10.603 20.112 30.023 50.000 40.000 10.000 20.741 10.664 20.097 40.253 30.782 30.264 20.523 20.154 10.707 50.000 30.411 30.000 10.000 20.000 20.332 50.000 10.000 30.000 10.602 20.595 20.185 50.656 50.159 10.000 10.355 30.424 40.154 40.729 40.516 30.220 30.620 20.084 30.000 10.707 40.651 30.173 10.014 40.381 60.582 50.000 10.619 10.049 50.000 40.000 10.702 10.000 20.000 10.302 50.489 40.317 30.334 30.392 10.922 30.254 30.533 40.394 20.129 60.613 40.000 10.000 40.820 10.649 40.749 30.000 10.782 30.282 30.863 10.000 10.288 50.006 40.220 40.633 30.542 1
OA-CNN-L_ScanNet2000.333 20.558 10.269 10.124 30.448 40.080 20.272 10.000 10.000 10.000 10.342 50.515 20.524 20.713 60.789 10.158 20.384 10.000 30.806 10.125 10.000 30.496 20.332 10.498 50.227 20.024 20.474 10.000 10.003 20.071 30.487 10.000 30.000 10.110 10.000 20.876 10.013 60.703 10.000 30.076 20.473 30.355 20.906 10.000 10.000 10.476 20.706 10.000 50.672 40.835 30.748 10.015 60.223 20.860 10.000 10.000 20.572 20.000 50.509 20.313 20.662 10.398 30.396 10.411 40.276 10.527 10.711 10.000 20.076 40.946 10.166 20.000 10.022 20.160 10.183 20.493 20.699 10.637 10.403 20.330 40.406 30.526 20.024 10.000 10.392 20.000 50.016 60.000 30.196 20.915 20.112 20.557 20.197 10.352 20.877 20.000 30.000 10.592 50.103 50.000 60.067 10.000 10.089 10.735 20.625 30.130 30.568 10.836 10.271 10.534 10.043 60.799 20.001 20.445 10.000 10.000 20.024 10.661 20.000 10.262 10.000 10.591 30.517 50.373 20.788 20.021 20.000 10.455 10.517 20.320 10.823 20.200 60.001 60.150 30.100 20.000 10.736 20.668 20.103 50.052 30.662 10.720 10.000 10.602 30.112 20.002 30.000 10.637 20.000 20.000 10.621 10.569 10.398 20.412 20.234 30.949 10.363 10.492 50.495 10.251 30.665 10.000 10.001 30.805 20.833 10.794 20.000 10.821 10.314 20.843 30.000 10.560 10.245 20.262 10.713 10.370 5
LGroundpermissive0.272 40.485 40.184 40.106 40.476 10.077 30.218 20.000 10.000 10.000 10.547 10.295 40.540 10.746 40.745 40.058 50.112 50.005 10.658 30.077 60.000 30.322 40.178 50.512 30.190 30.199 10.277 40.000 10.000 30.173 10.399 30.000 30.000 10.039 50.000 20.858 40.085 40.676 20.002 10.103 10.498 20.323 30.703 40.000 10.000 10.296 40.549 40.216 10.702 20.768 40.718 40.028 40.092 50.786 50.000 10.000 20.453 50.022 30.251 60.252 30.572 40.348 40.321 30.514 20.063 50.279 50.552 40.000 20.019 50.932 40.132 50.000 10.000 40.000 50.156 60.457 40.623 30.518 30.265 50.358 30.381 40.395 40.000 20.000 10.127 60.012 30.051 10.000 30.000 30.886 50.014 40.437 60.179 20.244 40.826 40.000 30.000 10.599 30.136 10.085 20.000 40.000 10.000 20.565 30.612 40.143 10.207 40.566 40.232 50.446 40.127 20.708 40.000 30.384 40.000 10.000 20.000 20.402 30.000 10.059 20.000 10.525 60.566 30.229 40.659 40.000 30.000 10.265 40.446 30.147 50.720 60.597 20.066 40.000 40.187 10.000 10.726 30.467 60.134 40.000 50.413 40.629 30.000 10.363 50.055 40.022 20.000 10.626 30.000 20.000 10.323 40.479 60.154 50.117 40.028 50.901 40.243 40.415 60.295 60.143 50.610 50.000 10.000 40.777 30.397 60.324 50.000 10.778 40.179 40.702 50.000 10.274 60.404 10.233 30.622 40.398 3
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 Infoavgalarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mask3D Scannet2000.388 10.542 10.357 10.237 10.610 10.091 10.125 50.000 10.000 10.000 10.065 30.668 10.451 11.000 10.955 10.640 10.500 10.039 10.125 20.063 20.409 10.311 20.291 10.609 30.266 10.000 10.163 10.000 10.008 10.044 20.496 11.000 10.000 10.018 20.000 10.756 10.573 10.808 20.000 10.010 10.042 30.130 30.552 10.042 10.000 11.000 10.725 40.750 10.883 11.000 10.832 40.024 20.107 10.614 30.226 10.250 10.628 20.792 10.677 20.400 10.741 10.278 10.511 10.077 50.111 10.313 20.715 20.302 10.017 30.200 20.000 10.188 10.000 10.178 20.736 11.000 10.615 10.514 10.409 20.380 50.600 10.000 10.000 10.400 10.013 20.254 10.381 10.000 10.123 40.400 10.839 10.258 10.463 10.926 10.265 10.000 10.857 20.099 10.021 20.500 10.027 10.028 11.000 10.502 50.016 10.076 40.500 10.612 10.578 10.005 20.597 20.194 10.497 10.000 10.500 10.000 20.323 40.000 11.000 10.000 10.748 10.708 20.050 40.890 21.000 10.008 20.151 30.301 11.000 11.000 10.792 30.945 11.000 10.511 10.004 20.753 10.776 20.287 20.020 20.003 40.974 30.033 10.412 50.000 10.000 20.000 20.667 10.000 10.000 10.491 10.676 20.352 10.335 10.060 20.822 50.527 21.000 10.517 10.606 10.853 10.000 10.004 10.806 11.000 10.727 10.000 10.042 20.739 20.000 10.399 30.391 10.504 10.591 10.571 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.permissive0.203 50.369 40.134 50.078 50.479 40.003 40.500 10.000 10.000 10.000 10.100 20.371 30.300 20.667 40.746 20.400 30.000 20.000 30.000 30.031 30.000 20.074 40.165 30.413 50.000 40.000 10.070 40.000 10.000 20.000 30.221 50.000 20.000 10.000 30.000 10.372 50.070 20.706 40.000 10.000 20.000 50.123 40.033 50.000 20.000 10.422 50.732 30.000 40.778 51.000 10.845 30.000 30.090 40.636 20.000 30.000 30.158 40.000 30.250 50.050 40.693 30.123 40.051 50.385 30.009 40.118 50.406 50.000 20.000 40.200 20.000 10.000 30.000 10.133 40.307 50.500 20.251 40.000 40.281 30.402 40.317 20.000 10.000 10.000 30.000 30.060 40.000 30.000 10.396 20.200 30.669 20.021 40.218 40.720 50.000 30.000 10.696 30.025 40.000 30.000 30.000 20.000 30.125 50.596 20.000 20.191 10.500 10.595 20.369 40.000 30.500 40.000 20.143 50.000 10.000 30.000 20.226 50.000 10.000 20.000 10.701 20.511 40.000 50.851 40.000 30.000 30.150 40.052 50.100 30.981 30.500 40.286 30.000 20.000 50.000 30.545 40.522 50.250 30.000 30.000 50.522 50.000 30.500 20.000 10.000 20.000 20.282 50.000 10.000 10.178 50.382 40.018 50.056 40.000 30.997 30.107 50.677 20.313 40.000 40.726 50.000 10.000 20.583 40.903 40.200 50.000 10.000 30.333 40.000 10.442 20.083 40.109 50.387 40.000 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
LGround Inst.permissive0.246 30.413 30.170 30.130 30.455 50.003 50.500 10.000 10.000 10.000 10.017 40.333 40.111 51.000 10.681 40.400 30.000 20.000 31.000 10.003 50.000 20.167 30.190 20.637 20.067 30.000 10.081 30.000 10.000 20.000 30.264 40.000 20.000 10.000 30.000 10.387 40.031 50.754 30.000 10.000 20.151 20.135 20.056 40.000 20.000 10.582 40.589 50.500 20.815 21.000 10.903 10.000 30.097 20.588 40.000 30.000 30.234 30.000 30.500 30.400 10.682 40.156 30.159 40.750 10.046 30.125 40.660 30.000 20.200 20.000 50.000 10.000 30.000 10.164 30.402 30.500 20.373 30.025 30.143 50.426 30.317 20.000 10.000 10.000 30.000 30.063 30.000 30.000 10.000 50.000 40.575 30.250 20.241 20.772 30.000 30.000 10.653 40.034 30.000 30.000 30.000 20.000 31.000 10.561 40.000 20.100 20.500 10.541 40.452 30.000 30.581 30.000 20.364 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.568 40.511 40.167 30.857 30.000 30.000 30.164 20.112 30.000 50.530 51.000 10.286 30.000 20.125 30.000 30.464 50.706 30.208 40.000 30.125 20.744 40.000 30.500 20.000 10.000 20.000 20.511 30.000 10.000 10.344 20.541 30.068 30.333 20.000 31.000 10.196 40.533 30.318 30.000 40.748 30.000 10.000 20.690 21.000 10.400 30.000 10.000 30.667 30.000 10.333 40.333 20.270 30.399 30.083 4
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.209 40.361 50.157 40.085 40.506 20.007 30.500 10.000 10.000 10.000 10.000 50.093 50.221 40.667 40.524 50.400 30.000 20.000 30.000 30.004 40.000 20.000 50.109 50.589 40.000 40.000 10.059 50.000 10.000 20.000 30.322 20.000 20.000 10.000 30.000 10.405 30.055 40.700 50.000 10.000 20.028 40.091 50.083 30.000 20.000 10.667 20.768 20.000 40.807 31.000 10.776 50.000 30.000 50.340 50.000 30.000 30.103 50.000 30.750 10.200 30.634 50.053 50.246 30.677 20.006 50.198 30.432 40.000 20.000 40.050 40.000 10.000 30.000 10.111 50.356 40.500 20.188 50.000 40.220 40.448 20.050 50.000 10.000 10.000 30.000 30.032 50.000 30.000 10.396 20.000 40.573 40.000 50.228 30.747 40.000 30.000 10.573 50.021 50.000 30.000 30.000 20.000 30.500 40.573 30.000 20.000 50.125 40.592 30.364 50.000 30.450 50.000 20.364 20.000 10.000 30.000 20.340 30.000 10.000 20.000 10.610 30.833 10.221 10.702 50.000 30.000 30.135 50.094 40.125 20.571 40.500 40.143 50.000 20.125 30.000 30.618 20.667 40.115 50.000 30.125 21.000 10.000 30.500 20.000 10.000 20.000 20.502 40.000 10.000 10.312 40.248 50.050 40.000 50.000 30.997 30.420 30.500 40.149 50.451 20.748 20.000 10.000 20.636 30.667 50.600 20.000 10.000 30.278 50.000 10.333 40.000 50.294 20.381 50.110 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
TD3D Scannet2000.320 20.501 20.264 20.164 20.506 30.062 20.500 10.000 10.000 10.000 10.208 10.431 20.252 31.000 10.733 30.587 20.000 20.008 20.000 30.106 10.000 20.356 10.123 40.686 10.101 20.000 10.152 20.000 10.000 20.226 10.280 30.000 20.000 10.250 10.000 10.619 20.061 30.841 10.000 10.000 20.167 10.194 10.333 20.000 20.000 10.667 20.820 10.250 30.790 41.000 10.879 20.077 10.094 30.708 10.217 20.049 20.634 10.792 10.331 40.033 50.716 20.159 20.396 20.331 40.099 20.415 10.842 10.000 20.458 10.542 10.000 10.101 20.000 10.218 10.513 20.500 20.458 20.104 20.516 10.456 10.268 40.000 10.000 10.400 10.022 10.233 20.143 20.000 10.677 10.400 10.504 50.095 30.083 50.890 20.061 20.000 10.906 10.076 20.231 10.125 20.000 20.003 20.792 30.881 10.000 20.098 30.125 40.498 50.459 20.063 10.715 10.000 20.241 40.000 10.396 20.063 10.605 10.000 10.000 20.000 10.448 50.629 30.202 20.967 10.250 20.038 10.192 10.185 20.083 41.000 11.000 10.857 20.000 20.470 20.012 10.565 30.798 10.621 10.111 10.500 11.000 10.017 20.509 10.000 10.008 11.000 10.525 20.000 10.000 10.332 30.679 10.264 20.333 20.267 11.000 10.549 10.299 50.387 20.328 30.744 40.000 10.000 20.435 51.000 10.283 40.000 10.196 10.817 10.000 10.472 10.222 30.123 40.560 20.156 2


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mix3Dpermissive0.781 10.964 10.855 10.843 110.781 10.858 70.575 30.831 200.685 70.714 10.979 10.594 30.310 180.801 10.892 80.841 20.819 30.723 30.940 80.887 10.725 13
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
CU-Hybrid Net0.764 20.924 20.819 90.840 120.757 70.853 90.580 10.848 150.709 20.643 130.958 110.587 70.295 240.753 150.884 120.758 120.815 60.725 20.927 180.867 110.743 6
OccuSeg+Semantic0.764 20.758 470.796 210.839 130.746 120.907 10.562 50.850 140.680 90.672 60.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 11
O-CNNpermissive0.762 40.924 20.823 60.844 100.770 30.852 100.577 20.847 160.711 10.640 170.958 110.592 40.217 590.762 110.888 90.758 120.813 70.726 10.932 160.868 100.744 5
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
OA-CNN-L_ScanNet200.756 50.783 350.826 50.858 40.776 20.837 200.548 90.896 50.649 170.675 50.962 80.586 80.335 80.771 70.802 360.770 80.787 220.691 80.936 110.880 50.761 3
PointTransformerV20.752 60.742 540.809 150.872 10.758 60.860 60.552 70.891 60.610 310.687 20.960 90.559 150.304 210.766 90.926 20.767 90.797 140.644 230.942 60.876 80.722 15
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 60.906 60.793 240.802 300.689 280.825 320.556 60.867 100.681 80.602 310.960 90.555 170.365 30.779 30.859 170.747 150.795 180.717 40.917 210.856 190.764 2
PointConvFormer0.749 80.793 320.790 250.807 250.750 110.856 80.524 170.881 80.588 420.642 160.977 40.591 50.274 350.781 20.929 10.804 30.796 150.642 240.947 30.885 20.715 18
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 80.909 40.818 110.811 220.752 90.839 190.485 330.842 170.673 100.644 120.957 140.528 260.305 200.773 60.859 170.788 40.818 50.693 70.916 220.856 190.723 14
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 100.623 800.804 170.859 30.745 130.824 340.501 240.912 20.690 60.685 30.956 150.567 120.320 140.768 80.918 30.720 230.802 100.676 130.921 190.881 40.779 1
StratifiedFormerpermissive0.747 110.901 70.803 180.845 90.757 70.846 140.512 200.825 230.696 50.645 110.956 150.576 100.262 450.744 190.861 160.742 160.770 310.705 50.899 340.860 160.734 7
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 MVFusion0.746 120.771 410.819 90.848 70.702 260.865 50.397 710.899 30.699 30.664 80.948 420.588 60.330 100.746 180.851 240.764 100.796 150.704 60.935 120.866 120.728 9
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 120.870 120.838 20.858 40.729 180.850 120.501 240.874 90.587 430.658 90.956 150.564 130.299 220.765 100.900 50.716 260.812 80.631 290.939 90.858 170.709 19
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Retro-FPN0.744 140.842 190.800 190.767 420.740 140.836 230.541 110.914 10.672 110.626 200.958 110.552 180.272 370.777 40.886 110.696 330.801 110.674 150.941 70.858 170.717 16
EQ-Net0.743 150.620 810.799 200.849 60.730 170.822 360.493 310.897 40.664 120.681 40.955 190.562 140.378 10.760 120.903 40.738 170.801 110.673 160.907 270.877 60.745 4
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 160.860 140.765 360.819 170.769 40.848 130.533 130.829 210.663 130.631 190.955 190.586 80.274 350.753 150.896 60.729 180.760 380.666 180.921 190.855 210.733 8
LRPNet0.742 160.816 270.806 160.807 250.752 90.828 300.575 30.839 190.699 30.637 180.954 240.520 280.320 140.755 140.834 280.760 110.772 280.676 130.915 230.862 140.717 16
TXC0.740 180.842 190.832 40.805 290.715 220.846 140.473 350.885 70.615 270.671 70.971 60.547 190.320 140.697 230.799 380.777 60.819 30.682 110.946 40.871 90.696 24
LargeKernel3D0.739 190.909 40.820 80.806 270.740 140.852 100.545 100.826 220.594 410.643 130.955 190.541 210.263 440.723 210.858 190.775 70.767 320.678 120.933 140.848 250.694 25
MinkowskiNetpermissive0.736 200.859 150.818 110.832 140.709 230.840 180.521 190.853 130.660 150.643 130.951 320.544 200.286 290.731 200.893 70.675 400.772 280.683 100.874 520.852 230.727 11
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 210.890 80.837 30.864 20.726 190.873 20.530 160.824 240.489 740.647 100.978 20.609 20.336 70.624 380.733 470.758 120.776 260.570 540.949 20.877 60.728 9
SparseConvNet0.725 220.647 770.821 70.846 80.721 200.869 30.533 130.754 440.603 370.614 240.955 190.572 110.325 120.710 220.870 130.724 210.823 20.628 300.934 130.865 130.683 28
PointTransformer++0.725 220.727 610.811 140.819 170.765 50.841 170.502 230.814 300.621 260.623 210.955 190.556 160.284 300.620 390.866 140.781 50.757 410.648 210.932 160.862 140.709 19
MatchingNet0.724 240.812 290.812 130.810 230.735 160.834 250.495 300.860 120.572 490.602 310.954 240.512 300.280 320.757 130.845 260.725 200.780 240.606 400.937 100.851 240.700 22
INS-Conv-semantic0.717 250.751 500.759 390.812 210.704 250.868 40.537 120.842 170.609 330.608 270.953 270.534 220.293 250.616 400.864 150.719 250.793 190.640 250.933 140.845 300.663 33
PointMetaBase0.714 260.835 210.785 270.821 150.684 300.846 140.531 150.865 110.614 280.596 350.953 270.500 330.246 510.674 240.888 90.692 340.764 340.624 310.849 670.844 310.675 30
contrastBoundarypermissive0.705 270.769 440.775 320.809 240.687 290.820 390.439 590.812 310.661 140.591 370.945 500.515 290.171 770.633 350.856 200.720 230.796 150.668 170.889 410.847 270.689 26
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 280.889 90.745 480.813 200.672 320.818 430.493 310.815 280.623 240.610 250.947 440.470 430.249 500.594 430.848 250.705 300.779 250.646 220.892 390.823 370.611 47
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 Click0.701 290.825 250.796 210.723 490.716 210.832 260.433 610.816 260.634 220.609 260.969 70.418 680.344 50.559 550.833 290.715 270.808 90.560 580.902 310.847 270.680 29
JSENetpermissive0.699 300.881 110.762 370.821 150.667 330.800 560.522 180.792 360.613 290.607 280.935 700.492 350.205 640.576 480.853 220.691 350.758 400.652 200.872 550.828 340.649 37
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
PicassoNet-IIpermissive0.696 310.704 660.790 250.787 340.709 230.837 200.459 430.815 280.543 580.615 230.956 150.529 240.250 480.551 600.790 390.703 310.799 130.619 350.908 260.848 250.700 22
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 320.743 530.794 230.655 730.684 300.822 360.497 290.719 540.622 250.617 220.977 40.447 550.339 60.750 170.664 620.703 310.790 210.596 440.946 40.855 210.647 38
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 330.884 100.754 430.795 330.647 390.818 430.422 630.802 340.612 300.604 290.945 500.462 460.189 720.563 540.853 220.726 190.765 330.632 280.904 290.821 400.606 51
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 340.704 660.741 520.754 460.656 350.829 280.501 240.741 490.609 330.548 440.950 360.522 270.371 20.633 350.756 420.715 270.771 300.623 320.861 630.814 420.658 34
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 350.866 130.748 450.819 170.645 410.794 590.450 480.802 340.587 430.604 290.945 500.464 450.201 670.554 570.840 270.723 220.732 500.602 420.907 270.822 390.603 54
VACNN++0.684 360.728 600.757 420.776 390.690 270.804 530.464 410.816 260.577 480.587 380.945 500.508 320.276 340.671 250.710 520.663 450.750 440.589 490.881 460.832 330.653 36
KP-FCNN0.684 360.847 180.758 410.784 360.647 390.814 460.473 350.772 390.605 350.594 360.935 700.450 530.181 750.587 440.805 350.690 360.785 230.614 360.882 450.819 410.632 43
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet0.684 360.712 650.784 280.782 380.658 340.835 240.499 280.823 250.641 190.597 340.950 360.487 360.281 310.575 490.619 650.647 530.764 340.620 340.871 580.846 290.688 27
Superpoint Network0.683 390.851 170.728 560.800 320.653 370.806 510.468 380.804 320.572 490.602 310.946 470.453 520.239 540.519 660.822 300.689 380.762 370.595 460.895 370.827 350.630 44
PointContrast_LA_SEM0.683 390.757 480.784 280.786 350.639 430.824 340.408 660.775 380.604 360.541 460.934 740.532 230.269 400.552 580.777 400.645 560.793 190.640 250.913 240.824 360.671 31
VI-PointConv0.676 410.770 430.754 430.783 370.621 470.814 460.552 70.758 420.571 510.557 420.954 240.529 240.268 420.530 640.682 570.675 400.719 530.603 410.888 420.833 320.665 32
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 420.789 330.748 450.763 440.635 450.814 460.407 680.747 460.581 470.573 390.950 360.484 370.271 390.607 410.754 430.649 500.774 270.596 440.883 440.823 370.606 51
SALANet0.670 430.816 270.770 340.768 410.652 380.807 500.451 450.747 460.659 160.545 450.924 800.473 420.149 870.571 510.811 340.635 590.746 450.623 320.892 390.794 540.570 64
PointConvpermissive0.666 440.781 360.759 390.699 580.644 420.822 360.475 340.779 370.564 540.504 620.953 270.428 620.203 660.586 460.754 430.661 460.753 420.588 500.902 310.813 440.642 39
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 440.703 680.781 300.751 480.655 360.830 270.471 370.769 400.474 770.537 480.951 320.475 410.279 330.635 330.698 560.675 400.751 430.553 630.816 740.806 460.703 21
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++permissive0.663 460.746 510.708 600.722 500.638 440.820 390.451 450.566 810.599 390.541 460.950 360.510 310.313 170.648 300.819 320.616 640.682 690.590 480.869 590.810 450.656 35
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 470.778 370.702 630.806 270.619 480.813 490.468 380.693 620.494 690.524 540.941 610.449 540.298 230.510 680.821 310.675 400.727 520.568 560.826 720.803 480.637 41
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 480.698 690.743 500.650 740.564 650.820 390.505 220.758 420.631 230.479 670.945 500.480 390.226 550.572 500.774 410.690 360.735 480.614 360.853 660.776 690.597 57
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 490.752 490.734 540.664 710.583 600.815 450.399 700.754 440.639 200.535 500.942 590.470 430.309 190.665 260.539 710.650 490.708 580.635 270.857 650.793 560.642 39
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 500.778 370.731 550.699 580.577 610.829 280.446 500.736 500.477 760.523 560.945 500.454 500.269 400.484 750.749 460.618 620.738 460.599 430.827 710.792 590.621 46
MVPNetpermissive0.641 510.831 220.715 580.671 680.590 560.781 650.394 720.679 640.642 180.553 430.937 670.462 460.256 460.649 290.406 840.626 600.691 660.666 180.877 480.792 590.608 50
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 510.776 390.703 620.721 510.557 680.826 310.451 450.672 660.563 550.483 660.943 580.425 650.162 820.644 310.726 480.659 470.709 570.572 530.875 500.786 640.559 69
PointMRNet0.640 530.717 640.701 640.692 610.576 620.801 550.467 400.716 550.563 550.459 720.953 270.429 610.169 790.581 470.854 210.605 650.710 550.550 640.894 380.793 560.575 62
FPConvpermissive0.639 540.785 340.760 380.713 560.603 510.798 570.392 730.534 860.603 370.524 540.948 420.457 480.250 480.538 620.723 500.598 690.696 640.614 360.872 550.799 490.567 66
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 550.797 310.769 350.641 790.590 560.820 390.461 420.537 850.637 210.536 490.947 440.388 760.206 630.656 270.668 600.647 530.732 500.585 510.868 600.793 560.473 88
PointSPNet0.637 560.734 570.692 710.714 550.576 620.797 580.446 500.743 480.598 400.437 770.942 590.403 720.150 860.626 370.800 370.649 500.697 630.557 610.846 680.777 680.563 67
SConv0.636 570.830 230.697 670.752 470.572 640.780 670.445 520.716 550.529 610.530 510.951 320.446 560.170 780.507 700.666 610.636 580.682 690.541 690.886 430.799 490.594 58
Supervoxel-CNN0.635 580.656 750.711 590.719 520.613 490.757 760.444 550.765 410.534 600.566 400.928 780.478 400.272 370.636 320.531 730.664 440.645 790.508 770.864 620.792 590.611 47
joint point-basedpermissive0.634 590.614 820.778 310.667 700.633 460.825 320.420 640.804 320.467 790.561 410.951 320.494 340.291 260.566 520.458 790.579 760.764 340.559 600.838 690.814 420.598 56
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 600.731 580.688 740.675 650.591 550.784 640.444 550.565 820.610 310.492 640.949 400.456 490.254 470.587 440.706 530.599 680.665 750.612 390.868 600.791 630.579 61
3DSM_DMMF0.631 610.626 790.745 480.801 310.607 500.751 770.506 210.729 530.565 530.491 650.866 940.434 570.197 700.595 420.630 640.709 290.705 600.560 580.875 500.740 790.491 83
PointNet2-SFPN0.631 610.771 410.692 710.672 660.524 730.837 200.440 580.706 600.538 590.446 740.944 560.421 670.219 580.552 580.751 450.591 720.737 470.543 680.901 330.768 710.557 70
APCF-Net0.631 610.742 540.687 760.672 660.557 680.792 620.408 660.665 670.545 570.508 590.952 310.428 620.186 730.634 340.702 540.620 610.706 590.555 620.873 530.798 510.581 60
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 640.604 840.741 520.766 430.590 560.747 780.501 240.734 510.503 680.527 520.919 840.454 500.323 130.550 610.420 830.678 390.688 670.544 660.896 360.795 530.627 45
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 650.800 300.625 860.719 520.545 710.806 510.445 520.597 760.448 830.519 570.938 660.481 380.328 110.489 740.499 780.657 480.759 390.592 470.881 460.797 520.634 42
SegGroup_sempermissive0.627 660.818 260.747 470.701 570.602 520.764 730.385 770.629 730.490 720.508 590.931 770.409 700.201 670.564 530.725 490.618 620.692 650.539 700.873 530.794 540.548 73
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 670.830 230.694 690.757 450.563 660.772 710.448 490.647 700.520 630.509 580.949 400.431 600.191 710.496 720.614 660.647 530.672 730.535 720.876 490.783 650.571 63
HPEIN0.618 680.729 590.668 770.647 760.597 540.766 720.414 650.680 630.520 630.525 530.946 470.432 580.215 600.493 730.599 670.638 570.617 840.570 540.897 350.806 460.605 53
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-GCNpermissive0.610 690.858 160.772 330.489 910.532 720.792 620.404 690.643 720.570 520.507 610.935 700.414 690.046 960.510 680.702 540.602 670.705 600.549 650.859 640.773 700.534 76
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 700.760 460.667 780.649 750.521 740.793 600.457 440.648 690.528 620.434 790.947 440.401 730.153 850.454 770.721 510.648 520.717 540.536 710.904 290.765 720.485 84
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 710.634 780.743 500.697 600.601 530.781 650.437 600.585 790.493 700.446 740.933 750.394 740.011 980.654 280.661 630.603 660.733 490.526 730.832 700.761 740.480 85
dtc_net0.596 720.683 700.725 570.715 540.549 700.803 540.444 550.647 700.493 700.495 630.941 610.409 700.000 1000.424 820.544 700.598 690.703 620.522 740.912 250.792 590.520 79
LAP-D0.594 730.720 620.692 710.637 800.456 830.773 700.391 750.730 520.587 430.445 760.940 640.381 770.288 270.434 800.453 810.591 720.649 770.581 520.777 780.749 780.610 49
DPC0.592 740.720 620.700 650.602 840.480 790.762 750.380 780.713 580.585 460.437 770.940 640.369 790.288 270.434 800.509 770.590 740.639 820.567 570.772 790.755 760.592 59
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 750.766 450.659 810.683 630.470 820.740 800.387 760.620 750.490 720.476 680.922 820.355 820.245 520.511 670.511 760.571 770.643 800.493 810.872 550.762 730.600 55
ROSMRF0.580 760.772 400.707 610.681 640.563 660.764 730.362 800.515 870.465 800.465 710.936 690.427 640.207 620.438 780.577 680.536 800.675 720.486 820.723 850.779 660.524 78
SD-DETR0.576 770.746 510.609 900.445 950.517 750.643 910.366 790.714 570.456 810.468 700.870 930.432 580.264 430.558 560.674 580.586 750.688 670.482 830.739 830.733 810.537 75
SQN_0.1%0.569 780.676 720.696 680.657 720.497 760.779 680.424 620.548 830.515 650.376 840.902 910.422 660.357 40.379 850.456 800.596 710.659 760.544 660.685 880.665 920.556 71
TextureNetpermissive0.566 790.672 740.664 790.671 680.494 770.719 810.445 520.678 650.411 890.396 820.935 700.356 810.225 560.412 830.535 720.565 780.636 830.464 850.794 770.680 890.568 65
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
DVVNet0.562 800.648 760.700 650.770 400.586 590.687 850.333 840.650 680.514 660.475 690.906 880.359 800.223 570.340 870.442 820.422 910.668 740.501 780.708 860.779 660.534 76
Pointnet++ & Featurepermissive0.557 810.735 560.661 800.686 620.491 780.744 790.392 730.539 840.451 820.375 850.946 470.376 780.205 640.403 840.356 870.553 790.643 800.497 790.824 730.756 750.515 80
GMLPs0.538 820.495 920.693 700.647 760.471 810.793 600.300 870.477 880.505 670.358 860.903 900.327 850.081 930.472 760.529 740.448 890.710 550.509 750.746 810.737 800.554 72
PanopticFusion-label0.529 830.491 930.688 740.604 830.386 880.632 920.225 970.705 610.434 860.293 920.815 950.348 830.241 530.499 710.669 590.507 820.649 770.442 910.796 760.602 950.561 68
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_weak0.516 840.676 720.591 930.609 810.442 840.774 690.335 830.597 760.422 880.357 870.932 760.341 840.094 920.298 890.528 750.473 870.676 710.495 800.602 940.721 840.349 95
Online SegFusion0.515 850.607 830.644 840.579 860.434 850.630 930.353 810.628 740.440 840.410 800.762 980.307 870.167 800.520 650.403 850.516 810.565 870.447 890.678 890.701 860.514 81
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, FTSDF0.501 860.558 880.608 910.424 970.478 800.690 840.246 930.586 780.468 780.450 730.911 860.394 740.160 830.438 780.212 940.432 900.541 920.475 840.742 820.727 820.477 86
PCNN0.498 870.559 870.644 840.560 880.420 870.711 830.229 950.414 890.436 850.352 880.941 610.324 860.155 840.238 940.387 860.493 830.529 930.509 750.813 750.751 770.504 82
3DMV0.484 880.484 940.538 950.643 780.424 860.606 960.310 850.574 800.433 870.378 830.796 960.301 880.214 610.537 630.208 950.472 880.507 960.413 940.693 870.602 950.539 74
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 890.577 860.611 890.356 990.321 960.715 820.299 890.376 930.328 960.319 900.944 560.285 900.164 810.216 970.229 920.484 850.545 910.456 870.755 800.709 850.475 87
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 900.679 710.604 920.578 870.380 890.682 860.291 900.106 990.483 750.258 970.920 830.258 940.025 970.231 960.325 880.480 860.560 890.463 860.725 840.666 910.231 99
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 910.474 950.623 870.463 930.366 910.651 890.310 850.389 920.349 940.330 890.937 670.271 920.126 890.285 900.224 930.350 960.577 860.445 900.625 920.723 830.394 91
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
SurfaceConvPF0.442 920.505 910.622 880.380 980.342 940.654 880.227 960.397 910.367 920.276 940.924 800.240 950.198 690.359 860.262 900.366 930.581 850.435 920.640 910.668 900.398 90
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 920.548 890.548 940.597 850.363 920.628 940.300 870.292 940.374 910.307 910.881 920.268 930.186 730.238 940.204 960.407 920.506 970.449 880.667 900.620 940.462 89
Tangent Convolutionspermissive0.438 940.437 970.646 830.474 920.369 900.645 900.353 810.258 960.282 980.279 930.918 850.298 890.147 880.283 910.294 890.487 840.562 880.427 930.619 930.633 930.352 94
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 950.525 900.647 820.522 890.324 950.488 990.077 1000.712 590.353 930.401 810.636 1000.281 910.176 760.340 870.565 690.175 1000.551 900.398 950.370 1000.602 950.361 93
SPLAT Netcopyleft0.393 960.472 960.511 960.606 820.311 970.656 870.245 940.405 900.328 960.197 980.927 790.227 970.000 1000.001 1010.249 910.271 990.510 940.383 970.593 950.699 870.267 97
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+FTSDF0.383 970.297 990.491 970.432 960.358 930.612 950.274 910.116 980.411 890.265 950.904 890.229 960.079 940.250 920.185 970.320 970.510 940.385 960.548 960.597 980.394 91
PointNet++permissive0.339 980.584 850.478 980.458 940.256 990.360 1000.250 920.247 970.278 990.261 960.677 990.183 980.117 900.212 980.145 990.364 940.346 1000.232 1000.548 960.523 990.252 98
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 990.353 980.290 1000.278 1000.166 1000.553 970.169 990.286 950.147 1000.148 1000.908 870.182 990.064 950.023 1000.018 1010.354 950.363 980.345 980.546 980.685 880.278 96
ScanNetpermissive0.306 1000.203 1000.366 990.501 900.311 970.524 980.211 980.002 1010.342 950.189 990.786 970.145 1000.102 910.245 930.152 980.318 980.348 990.300 990.460 990.437 1000.182 100
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 1010.000 1010.041 1010.172 1010.030 1010.062 1010.001 1010.035 1000.004 1010.051 1010.143 1010.019 1010.003 990.041 990.050 1000.003 1010.054 1010.018 1010.005 1010.264 1010.082 101


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Queryformer0.787 11.000 10.933 10.601 340.754 10.886 40.558 20.661 250.767 30.665 40.716 30.639 110.808 31.000 10.844 10.897 20.804 21.000 10.624 2
Mask3D0.780 21.000 10.786 270.716 250.696 50.885 50.500 40.714 180.810 20.672 30.715 40.679 70.809 21.000 10.831 20.833 80.787 41.000 10.602 6
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 30.903 390.903 20.806 130.609 170.886 30.568 10.815 60.705 70.711 10.655 60.652 100.685 111.000 10.789 40.809 140.776 71.000 10.583 11
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 41.000 10.803 200.937 10.684 60.865 70.213 200.870 20.664 90.571 100.758 10.702 40.807 41.000 10.653 160.902 10.792 31.000 10.626 1
ISBNetpermissive0.763 51.000 10.873 50.717 240.666 90.858 110.508 30.667 230.764 40.643 50.676 50.688 60.825 11.000 10.773 50.741 270.777 61.000 10.556 17
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
SoftGrouppermissive0.761 61.000 10.808 170.845 80.716 20.862 90.243 170.824 40.655 110.620 60.734 20.699 50.791 60.981 250.716 80.844 50.769 81.000 10.594 9
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3D0.751 71.000 10.774 280.867 70.621 130.934 10.404 70.706 190.812 10.605 80.633 110.626 120.690 101.000 10.640 180.820 110.777 51.000 10.612 4
PBNetpermissive0.747 81.000 10.818 130.837 100.713 30.844 120.457 60.647 280.711 60.614 70.617 130.657 90.650 131.000 10.692 100.822 100.765 101.000 10.595 8
GraphCut0.732 91.000 10.788 250.724 230.642 110.859 100.248 160.787 110.618 140.596 90.653 80.722 20.583 311.000 10.766 60.861 30.825 11.000 10.504 23
IPCA-Inst0.731 101.000 10.788 260.884 60.698 40.788 270.252 150.760 130.646 120.511 180.637 100.665 80.804 51.000 10.644 170.778 170.747 121.000 10.561 15
TopoSeg0.725 111.000 10.806 190.933 20.668 80.758 300.272 140.734 170.630 130.549 140.654 70.606 130.697 90.966 270.612 220.839 60.754 111.000 10.573 12
DKNet0.718 121.000 10.814 140.782 160.619 140.872 60.224 180.751 150.569 180.677 20.585 160.724 10.633 230.981 250.515 320.819 120.736 131.000 10.617 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 131.000 10.850 70.924 30.648 100.747 330.162 220.862 30.572 170.520 160.624 120.549 160.649 211.000 10.560 270.706 330.768 91.000 10.591 10
HAISpermissive0.699 141.000 10.849 80.820 110.675 70.808 210.279 120.757 140.465 230.517 170.596 140.559 150.600 251.000 10.654 150.767 190.676 170.994 350.560 16
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 151.000 10.697 440.888 50.556 230.803 220.387 80.626 300.417 270.556 130.585 170.702 30.600 251.000 10.824 30.720 320.692 151.000 10.509 22
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 161.000 10.799 220.811 120.622 120.817 160.376 90.805 90.590 160.487 210.568 200.525 200.650 130.835 380.600 230.829 90.655 191.000 10.526 19
SphereSeg0.680 171.000 10.856 60.744 220.618 150.893 20.151 230.651 270.713 50.537 150.579 190.430 290.651 121.000 10.389 410.744 260.697 140.991 370.601 7
Box2Mask0.677 181.000 10.847 90.771 180.509 310.816 170.277 130.558 370.482 200.562 120.640 90.448 250.700 71.000 10.666 110.852 40.578 310.997 300.488 27
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 191.000 10.758 360.682 280.576 210.842 130.477 50.504 410.524 190.567 110.585 180.451 240.557 321.000 10.751 70.797 150.563 341.000 10.467 31
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 201.000 10.822 120.764 210.616 160.815 180.139 270.694 210.597 150.459 250.566 210.599 140.600 250.516 480.715 90.819 130.635 231.000 10.603 5
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 211.000 10.760 340.667 300.581 190.863 80.323 100.655 260.477 210.473 230.549 230.432 280.650 131.000 10.655 140.738 280.585 300.944 410.472 30
CSC-Pretrained0.648 221.000 10.810 150.768 190.523 290.813 190.143 260.819 50.389 300.422 330.511 270.443 260.650 131.000 10.624 200.732 290.634 241.000 10.375 38
PE0.645 231.000 10.773 300.798 150.538 250.786 280.088 340.799 100.350 340.435 320.547 240.545 170.646 220.933 280.562 260.761 220.556 390.997 300.501 25
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 241.000 10.758 350.582 400.539 240.826 150.046 380.765 120.372 320.436 310.588 150.539 190.650 131.000 10.577 240.750 240.653 210.997 300.495 26
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 251.000 10.841 100.893 40.531 270.802 230.115 310.588 350.448 240.438 290.537 260.430 300.550 330.857 300.534 300.764 210.657 180.987 380.568 13
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 261.000 10.895 40.800 140.480 350.676 370.144 250.737 160.354 330.447 260.400 390.365 350.700 71.000 10.569 250.836 70.599 261.000 10.473 29
PointGroup0.636 271.000 10.765 310.624 320.505 330.797 240.116 300.696 200.384 310.441 270.559 220.476 220.596 281.000 10.666 110.756 230.556 380.997 300.513 21
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
DD-UNet+Group0.635 280.667 400.797 240.714 260.562 220.774 290.146 240.810 80.429 260.476 220.546 250.399 320.633 231.000 10.632 190.722 310.609 251.000 10.514 20
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
DENet0.629 291.000 10.797 230.608 330.589 180.627 410.219 190.882 10.310 360.402 380.383 410.396 330.650 131.000 10.663 130.543 490.691 161.000 10.568 14
3D-MPA0.611 301.000 10.833 110.765 200.526 280.756 310.136 290.588 350.470 220.438 300.432 360.358 360.650 130.857 300.429 370.765 200.557 371.000 10.430 33
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 311.000 10.801 210.599 350.535 260.728 350.286 110.436 450.679 80.491 190.433 340.256 380.404 450.857 300.620 210.724 300.510 431.000 10.539 18
AOIA0.601 321.000 10.761 330.687 270.485 340.828 140.008 440.663 240.405 290.405 370.425 370.490 210.596 280.714 410.553 290.779 160.597 270.992 360.424 35
PCJC0.578 331.000 10.810 160.583 390.449 380.813 200.042 390.603 330.341 350.490 200.465 310.410 310.650 130.835 380.264 470.694 370.561 350.889 450.504 24
SSEN0.575 341.000 10.761 320.473 420.477 360.795 250.066 350.529 380.658 100.460 240.461 320.380 340.331 470.859 290.401 400.692 390.653 201.000 10.348 40
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 350.528 500.708 430.626 310.580 200.745 340.063 360.627 290.240 400.400 390.497 280.464 230.515 341.000 10.475 340.745 250.571 321.000 10.429 34
NeuralBF0.555 360.667 400.896 30.843 90.517 300.751 320.029 400.519 390.414 280.439 280.465 300.000 560.484 360.857 300.287 450.693 380.651 221.000 10.485 28
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
MTML0.549 371.000 10.807 180.588 380.327 430.647 390.004 460.815 70.180 420.418 340.364 430.182 410.445 391.000 10.442 360.688 400.571 331.000 10.396 36
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
One_Thing_One_Clickpermissive0.529 380.667 400.718 390.777 170.399 390.683 360.000 490.669 220.138 450.391 400.374 420.539 180.360 460.641 450.556 280.774 180.593 280.997 300.251 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN0.515 391.000 10.538 510.282 450.468 370.790 260.173 210.345 470.429 250.413 360.484 290.176 420.595 300.591 460.522 310.668 410.476 440.986 390.327 41
Occipital-SCS0.512 401.000 10.716 400.509 410.506 320.611 420.092 330.602 340.177 430.346 430.383 400.165 430.442 400.850 370.386 420.618 450.543 400.889 450.389 37
3D-BoNet0.488 411.000 10.672 460.590 370.301 450.484 520.098 320.620 310.306 370.341 440.259 470.125 450.434 420.796 400.402 390.499 510.513 420.909 440.439 32
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PanopticFusion-inst0.478 420.667 400.712 420.595 360.259 480.550 480.000 490.613 320.175 440.250 490.434 330.437 270.411 440.857 300.485 330.591 480.267 540.944 410.359 39
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS0.470 430.667 400.685 450.677 290.372 410.562 460.000 490.482 420.244 390.316 460.298 440.052 510.442 410.857 300.267 460.702 340.559 361.000 10.287 43
SALoss-ResNet0.459 441.000 10.737 380.159 550.259 470.587 440.138 280.475 430.217 410.416 350.408 380.128 440.315 480.714 410.411 380.536 500.590 290.873 480.304 42
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.447 450.528 500.555 490.381 430.382 400.633 400.002 470.509 400.260 380.361 420.432 350.327 370.451 380.571 470.367 430.639 430.386 450.980 400.276 44
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 460.667 400.773 290.185 520.317 440.656 380.000 490.407 460.134 460.381 410.267 460.217 400.476 370.714 410.452 350.629 440.514 411.000 10.222 48
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 471.000 10.432 530.245 470.190 490.577 450.013 430.263 490.033 520.320 450.240 480.075 470.422 430.857 300.117 510.699 350.271 530.883 470.235 47
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 480.667 400.542 500.264 460.157 520.550 470.000 490.205 520.009 530.270 480.218 490.075 470.500 350.688 440.007 570.698 360.301 500.459 540.200 49
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 490.667 400.715 410.233 480.189 500.479 530.008 440.218 500.067 510.201 510.173 500.107 460.123 530.438 490.150 490.615 460.355 460.916 430.093 56
R-PointNet0.306 500.500 520.405 540.311 440.348 420.589 430.054 370.068 550.126 470.283 470.290 450.028 520.219 510.214 520.331 440.396 550.275 510.821 500.245 46
Region-18class0.284 510.250 560.751 370.228 500.270 460.521 490.000 490.468 440.008 550.205 500.127 510.000 560.068 550.070 550.262 480.652 420.323 480.740 510.173 50
SemRegionNet-20cls0.250 520.333 530.613 470.229 490.163 510.493 500.000 490.304 480.107 480.147 530.100 520.052 500.231 490.119 530.039 530.445 530.325 470.654 520.141 52
tmp0.248 530.667 400.437 520.188 510.153 530.491 510.000 490.208 510.094 500.153 520.099 530.057 490.217 520.119 530.039 530.466 520.302 490.640 530.140 53
3D-BEVIS0.248 530.667 400.566 480.076 560.035 570.394 550.027 420.035 560.098 490.099 550.030 560.025 530.098 540.375 510.126 500.604 470.181 550.854 490.171 51
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
ASIS0.199 550.333 530.253 560.167 540.140 540.438 540.000 490.177 530.008 540.121 540.069 540.004 550.231 500.429 500.036 550.445 540.273 520.333 560.119 55
Sgpn_scannet0.143 560.208 570.390 550.169 530.065 550.275 560.029 410.069 540.000 560.087 560.043 550.014 540.027 570.000 560.112 520.351 560.168 560.438 550.138 54
MaskRCNN 2d->3d Proj0.058 570.333 530.002 570.000 570.053 560.002 570.002 480.021 570.000 560.045 570.024 570.238 390.065 560.000 560.014 560.107 570.020 570.110 570.006 57


This table lists the benchmark results for the 2D 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
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 10.512 10.422 150.898 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
BPNet_2Dcopyleft0.670 20.822 30.795 30.836 20.659 20.481 20.451 110.769 30.656 30.567 30.931 30.395 40.390 40.700 30.534 30.689 90.770 20.574 30.865 60.831 30.675 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 200.648 30.463 30.549 20.742 60.676 20.628 20.961 10.420 20.379 50.684 60.381 150.732 20.723 30.599 20.827 130.851 20.634 6
CMX0.613 40.681 70.725 90.502 120.634 50.297 150.478 90.830 20.651 40.537 60.924 40.375 50.315 120.686 50.451 120.714 40.543 180.504 50.894 40.823 40.688 3
DMMF_3d0.605 50.651 80.744 70.782 30.637 40.387 40.536 30.732 70.590 60.540 50.856 180.359 90.306 130.596 110.539 20.627 180.706 40.497 70.785 180.757 160.476 19
MCA-Net0.595 60.533 170.756 60.746 40.590 80.334 70.506 60.670 120.587 70.500 100.905 80.366 80.352 80.601 100.506 60.669 150.648 70.501 60.839 120.769 120.516 18
RFBNet0.592 70.616 90.758 50.659 50.581 90.330 80.469 100.655 150.543 120.524 70.924 40.355 100.336 100.572 140.479 80.671 130.648 70.480 90.814 160.814 50.614 9
FAN_NV_RVC0.586 80.510 180.764 40.079 230.620 70.330 80.494 70.753 40.573 80.556 40.884 130.405 30.303 140.718 20.452 110.672 120.658 50.509 40.898 30.813 60.727 2
DCRedNet0.583 90.682 60.723 100.542 110.510 170.310 120.451 110.668 130.549 110.520 80.920 60.375 50.446 20.528 170.417 130.670 140.577 150.478 100.862 70.806 70.628 8
MIX6D_RVC0.582 100.695 40.687 140.225 180.632 60.328 100.550 10.748 50.623 50.494 130.890 110.350 120.254 200.688 40.454 100.716 30.597 140.489 80.881 50.768 130.575 12
SSMAcopyleft0.577 110.695 40.716 120.439 140.563 110.314 110.444 130.719 80.551 100.503 90.887 120.346 130.348 90.603 90.353 170.709 50.600 120.457 120.901 20.786 80.599 11
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE0.566 120.569 160.686 160.435 150.524 140.294 160.421 160.712 90.543 120.463 150.872 140.320 140.363 70.611 80.477 90.686 100.627 90.443 150.862 70.775 110.639 5
EMSAFormer0.564 130.581 130.736 80.564 100.546 130.219 200.517 40.675 110.486 170.427 190.904 90.352 110.320 110.589 120.528 40.708 60.464 210.413 190.847 110.786 80.611 10
SN_RN152pyrx8_RVCcopyleft0.546 140.572 140.663 180.638 70.518 150.298 140.366 210.633 180.510 150.446 170.864 160.296 170.267 170.542 160.346 180.704 70.575 160.431 160.853 100.766 140.630 7
UDSSEG_RVC0.545 150.610 110.661 190.588 80.556 120.268 180.482 80.642 170.572 90.475 140.836 200.312 150.367 60.630 70.189 200.639 170.495 200.452 130.826 140.756 170.541 14
segfomer with 6d0.542 160.594 120.687 140.146 210.579 100.308 130.515 50.703 100.472 180.498 110.868 150.369 70.282 150.589 120.390 140.701 80.556 170.416 180.860 90.759 150.539 16
FuseNetpermissive0.535 170.570 150.681 170.182 190.512 160.290 170.431 140.659 140.504 160.495 120.903 100.308 160.428 30.523 180.365 160.676 110.621 110.470 110.762 190.779 100.541 14
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 180.613 100.722 110.418 160.358 230.337 60.370 200.479 210.443 190.368 210.907 70.207 200.213 220.464 210.525 50.618 190.657 60.450 140.788 170.721 200.408 22
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 190.481 210.612 200.579 90.456 190.343 50.384 180.623 190.525 140.381 200.845 190.254 190.264 190.557 150.182 210.581 210.598 130.429 170.760 200.661 220.446 21
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 200.505 190.709 130.092 220.427 200.241 190.411 170.654 160.385 230.457 160.861 170.053 230.279 160.503 190.481 70.645 160.626 100.365 210.748 210.725 190.529 17
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 210.490 200.581 210.289 170.507 180.067 230.379 190.610 200.417 210.435 180.822 220.278 180.267 170.503 190.228 190.616 200.533 190.375 200.820 150.729 180.560 13
Enet (reimpl)0.376 220.264 230.452 230.452 130.365 210.181 210.143 230.456 220.409 220.346 220.769 230.164 210.218 210.359 220.123 230.403 230.381 230.313 230.571 220.685 210.472 20
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)permissive0.330 230.293 220.521 220.657 60.361 220.161 220.250 220.004 230.440 200.183 230.836 200.125 220.060 230.319 230.132 220.417 220.412 220.344 220.541 230.427 230.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF0.003 240.000 240.005 240.000 240.000 240.037 240.001 240.000 240.001 240.005 240.003 240.000 240.000 240.000 240.000 240.000 240.002 240.001 240.000 240.006 240.000 24


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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
UniDet_RVC0.205 10.381 10.323 10.037 10.226 10.177 10.063 10.277 10.120 10.067 10.131 10.074 20.317 10.080 10.235 10.289 10.141 10.678 10.080 1
MaskRCNN_ScanNetpermissive0.119 20.129 20.212 20.002 20.112 20.148 20.014 20.205 20.044 20.066 20.078 20.095 10.142 20.030 20.128 20.139 20.080 20.459 20.057 2
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 iouapartmentbathroombedroom / hotelbookstore / libraryconference roomcopy/mail roomhallwaykitchenlaundry roomliving room / loungemiscofficestorage / basement / garage
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
multi-taskpermissive0.646 10.500 11.000 10.789 10.333 20.667 21.000 10.500 11.000 11.000 10.778 10.000 20.833 10.000 2
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE0.556 20.500 10.938 20.778 20.667 11.000 10.250 20.500 10.750 20.333 20.500 30.000 20.812 20.200 1
SE-ResNeXt-SSMA0.355 30.000 40.684 30.696 30.200 40.500 30.200 30.500 10.429 30.200 30.545 20.111 10.556 30.000 2
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.231 40.200 30.481 40.346 40.250 30.250 40.000 40.500 10.333 40.000 40.357 40.000 20.286 40.000 2