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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CeCo0.340 10.551 10.247 10.181 10.475 20.057 40.142 30.000 10.000 10.000 10.387 20.463 10.499 20.924 10.774 10.213 10.257 10.000 30.546 40.100 20.006 20.615 10.177 40.534 10.246 10.000 20.400 10.000 10.338 10.006 30.484 10.609 10.000 10.083 10.000 20.873 10.089 20.661 20.000 30.048 40.560 10.408 10.892 10.000 10.000 10.586 10.616 20.000 40.692 20.900 10.721 10.162 10.228 10.860 10.000 10.000 20.575 10.083 20.550 10.347 10.624 10.410 10.360 10.740 10.109 20.321 20.660 10.000 20.121 20.939 10.143 20.000 10.400 10.003 20.190 10.564 10.652 10.615 10.421 10.304 30.579 10.547 10.000 10.000 10.296 10.000 40.030 40.096 10.000 20.916 10.037 10.551 10.171 20.376 10.865 10.286 10.000 10.633 10.102 40.027 40.011 20.000 10.000 10.474 20.742 10.133 20.311 10.824 10.242 10.503 10.068 30.828 10.000 20.429 10.000 10.063 10.000 10.781 10.000 10.000 20.000 10.665 10.633 10.450 10.818 10.000 10.000 10.429 10.532 10.226 10.825 10.510 30.377 10.709 10.079 20.000 10.753 10.683 10.102 40.063 20.401 40.620 30.000 10.619 10.000 40.000 30.000 10.595 20.000 20.000 10.345 20.564 10.411 10.603 10.384 10.945 10.266 10.643 10.367 10.304 10.663 10.000 10.010 10.726 20.767 10.898 10.000 10.784 10.435 10.861 10.000 10.447 10.000 40.257 10.656 10.377 3
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
Minkowski 34Dpermissive0.253 30.463 30.154 40.102 30.381 40.084 10.134 40.000 10.000 10.000 10.386 30.141 40.279 40.737 40.703 30.014 40.164 30.000 30.663 10.092 30.000 30.224 30.291 10.531 20.056 40.000 20.242 30.000 10.000 20.013 20.331 30.000 20.000 10.035 40.001 10.858 20.059 40.650 40.000 30.056 30.353 30.299 30.670 30.000 10.000 10.284 30.484 40.071 30.594 30.720 30.710 30.027 40.068 40.813 20.000 10.005 10.492 20.164 10.274 30.111 40.571 30.307 40.293 30.307 40.150 10.163 40.531 30.002 10.545 10.932 20.093 40.000 10.000 20.002 30.159 30.368 40.581 40.440 40.228 40.406 10.282 40.294 30.000 10.000 10.189 30.060 10.036 30.000 20.000 20.897 20.000 40.525 30.025 40.205 40.771 40.000 20.000 10.593 30.108 30.044 30.000 30.000 10.000 10.282 40.589 30.094 30.169 30.466 40.227 40.419 40.125 20.757 20.002 10.334 30.000 10.000 20.000 10.357 30.000 10.000 20.000 10.582 20.513 40.337 20.612 40.000 10.000 10.250 30.352 40.136 40.724 30.655 10.280 20.000 20.046 40.000 10.606 40.559 20.159 10.102 10.445 10.655 10.000 10.310 40.117 10.000 30.000 10.581 40.026 10.000 10.265 40.483 30.084 40.097 40.044 20.865 40.142 40.588 20.351 20.272 20.596 40.000 10.003 20.622 30.720 20.096 40.000 10.771 30.016 30.772 20.000 10.302 30.194 20.214 30.621 30.197 4
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrainpermissive0.249 40.455 40.171 30.079 40.418 30.059 30.186 20.000 10.000 10.000 10.335 40.250 30.316 30.766 20.697 40.142 20.170 20.003 20.553 30.112 10.097 10.201 40.186 20.476 40.081 30.000 20.216 40.000 10.000 20.001 40.314 40.000 20.000 10.055 20.000 20.832 40.094 10.659 30.002 10.076 20.310 40.293 40.664 40.000 10.000 10.175 40.634 10.130 20.552 40.686 40.700 40.076 20.110 20.770 40.000 10.000 20.430 40.000 40.319 20.166 30.542 40.327 30.205 40.332 30.052 40.375 10.444 40.000 20.012 40.930 40.203 10.000 10.000 20.046 10.175 20.413 30.592 30.471 30.299 20.152 40.340 30.247 40.000 10.000 10.225 20.058 20.037 20.000 20.207 10.862 40.014 20.548 20.033 30.233 30.816 30.000 20.000 10.542 40.123 20.121 10.019 10.000 10.000 10.463 30.454 40.045 40.128 40.557 30.235 20.441 30.063 40.484 40.000 20.308 40.000 10.000 20.000 10.318 40.000 10.000 20.000 10.545 30.543 30.164 40.734 20.000 10.000 10.215 40.371 30.198 20.743 20.205 40.062 40.000 20.079 20.000 10.683 30.547 30.142 20.000 30.441 20.579 40.000 10.464 20.098 20.041 10.000 10.590 30.000 20.000 10.373 10.494 20.174 20.105 30.001 40.895 30.222 30.537 30.307 30.180 30.625 20.000 10.000 30.591 40.609 30.398 20.000 10.766 40.014 40.638 40.000 10.377 20.004 30.206 40.609 40.465 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGroundpermissive0.272 20.485 20.184 20.106 20.476 10.077 20.218 10.000 10.000 10.000 10.547 10.295 20.540 10.746 30.745 20.058 30.112 40.005 10.658 20.077 40.000 30.322 20.178 30.512 30.190 20.199 10.277 20.000 10.000 20.173 10.399 20.000 20.000 10.039 30.000 20.858 20.085 30.676 10.002 10.103 10.498 20.323 20.703 20.000 10.000 10.296 20.549 30.216 10.702 10.768 20.718 20.028 30.092 30.786 30.000 10.000 20.453 30.022 30.251 40.252 20.572 20.348 20.321 20.514 20.063 30.279 30.552 20.000 20.019 30.932 20.132 30.000 10.000 20.000 40.156 40.457 20.623 20.518 20.265 30.358 20.381 20.395 20.000 10.000 10.127 40.012 30.051 10.000 20.000 20.886 30.014 20.437 40.179 10.244 20.826 20.000 20.000 10.599 20.136 10.085 20.000 30.000 10.000 10.565 10.612 20.143 10.207 20.566 20.232 30.446 20.127 10.708 30.000 20.384 20.000 10.000 20.000 10.402 20.000 10.059 10.000 10.525 40.566 20.229 30.659 30.000 10.000 10.265 20.446 20.147 30.720 40.597 20.066 30.000 20.187 10.000 10.726 20.467 40.134 30.000 30.413 30.629 20.000 10.363 30.055 30.022 20.000 10.626 10.000 20.000 10.323 30.479 40.154 30.117 20.028 30.901 20.243 20.415 40.295 40.143 40.610 30.000 10.000 30.777 10.397 40.324 30.000 10.778 20.179 20.702 30.000 10.274 40.404 10.233 20.622 20.398 2
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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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


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 10.843 100.781 10.858 70.575 30.831 180.685 70.714 10.979 10.594 30.310 160.801 10.892 80.841 20.819 30.723 30.940 70.887 10.725 12
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 70.840 110.757 60.853 90.580 10.848 130.709 20.643 110.958 90.587 70.295 220.753 140.884 120.758 100.815 50.725 20.927 160.867 90.743 5
OccuSeg+Semantic0.764 20.758 450.796 190.839 120.746 110.907 10.562 50.850 120.680 90.672 50.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 10
O-CNNpermissive0.762 40.924 20.823 40.844 90.770 20.852 100.577 20.847 140.711 10.640 150.958 90.592 40.217 570.762 100.888 90.758 100.813 60.726 10.932 140.868 80.744 4
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
DMF-Net0.752 50.906 60.793 220.802 280.689 270.825 300.556 60.867 80.681 80.602 290.960 70.555 160.365 30.779 30.859 170.747 130.795 170.717 40.917 190.856 170.764 2
PointTransformerV20.752 50.742 520.809 130.872 10.758 50.860 60.552 70.891 50.610 280.687 20.960 70.559 140.304 190.766 80.926 20.767 70.797 130.644 210.942 50.876 70.722 14
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 70.793 310.790 230.807 240.750 100.856 80.524 160.881 60.588 390.642 140.977 40.591 50.274 320.781 20.929 10.804 30.796 140.642 220.947 30.885 20.715 17
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 70.909 40.818 90.811 210.752 80.839 180.485 310.842 150.673 100.644 100.957 120.528 240.305 180.773 60.859 170.788 40.818 40.693 70.916 200.856 170.723 13
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 90.623 760.804 150.859 30.745 120.824 320.501 230.912 20.690 60.685 30.956 130.567 110.320 130.768 70.918 30.720 210.802 90.676 110.921 170.881 40.779 1
StratifiedFormerpermissive0.747 100.901 70.803 160.845 80.757 60.846 140.512 190.825 210.696 50.645 90.956 130.576 90.262 430.744 180.861 160.742 140.770 300.705 50.899 310.860 140.734 6
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
VMNetpermissive0.746 110.870 120.838 20.858 40.729 170.850 120.501 230.874 70.587 400.658 70.956 130.564 120.299 200.765 90.900 50.716 240.812 70.631 270.939 80.858 150.709 18
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion0.746 110.771 390.819 70.848 60.702 250.865 50.397 680.899 30.699 30.664 60.948 390.588 60.330 90.746 170.851 240.764 80.796 140.704 60.935 100.866 100.728 8
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
Retro-FPN0.744 130.842 190.800 170.767 400.740 130.836 220.541 100.914 10.672 110.626 180.958 90.552 170.272 340.777 40.886 110.696 310.801 100.674 130.941 60.858 150.717 15
EQ-Net0.743 140.620 770.799 180.849 50.730 160.822 340.493 290.897 40.664 120.681 40.955 170.562 130.378 10.760 110.903 40.738 150.801 100.673 140.907 240.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 150.816 260.806 140.807 240.752 80.828 280.575 30.839 170.699 30.637 160.954 220.520 260.320 130.755 130.834 280.760 90.772 270.676 110.915 210.862 120.717 15
SAT0.742 150.860 140.765 340.819 160.769 30.848 130.533 120.829 190.663 130.631 170.955 170.586 80.274 320.753 140.896 60.729 160.760 360.666 160.921 170.855 190.733 7
LargeKernel3D0.739 170.909 40.820 60.806 260.740 130.852 100.545 90.826 200.594 380.643 110.955 170.541 190.263 420.723 200.858 190.775 60.767 310.678 100.933 120.848 230.694 23
MinkowskiNetpermissive0.736 180.859 150.818 90.832 130.709 220.840 170.521 180.853 110.660 150.643 110.951 300.544 180.286 270.731 190.893 70.675 380.772 270.683 90.874 490.852 210.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 190.890 80.837 30.864 20.726 180.873 20.530 150.824 220.489 700.647 80.978 20.609 20.336 70.624 360.733 450.758 100.776 250.570 520.949 20.877 50.728 8
SparseConvNet0.725 200.647 730.821 50.846 70.721 200.869 30.533 120.754 410.603 340.614 220.955 170.572 100.325 110.710 210.870 130.724 190.823 20.628 280.934 110.865 110.683 25
PointTransformer++0.725 200.727 590.811 120.819 160.765 40.841 160.502 220.814 270.621 240.623 190.955 170.556 150.284 280.620 370.866 140.781 50.757 390.648 190.932 140.862 120.709 18
MatchingNet0.724 220.812 280.812 110.810 220.735 150.834 230.495 280.860 100.572 460.602 290.954 220.512 280.280 290.757 120.845 260.725 180.780 230.606 380.937 90.851 220.700 21
INS-Conv-semantic0.717 230.751 480.759 370.812 200.704 240.868 40.537 110.842 150.609 300.608 250.953 250.534 200.293 230.616 380.864 150.719 230.793 180.640 230.933 120.845 270.663 30
PointMetaBase0.714 240.835 200.785 250.821 140.684 290.846 140.531 140.865 90.614 250.596 320.953 250.500 310.246 490.674 220.888 90.692 320.764 330.624 290.849 630.844 280.675 27
contrastBoundarypermissive0.705 250.769 420.775 300.809 230.687 280.820 370.439 550.812 280.661 140.591 350.945 480.515 270.171 750.633 330.856 200.720 210.796 140.668 150.889 380.847 250.689 24
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 260.889 90.745 460.813 190.672 310.818 410.493 290.815 250.623 220.610 230.947 410.470 400.249 480.594 410.848 250.705 280.779 240.646 200.892 360.823 350.611 45
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 Click0.701 270.825 240.796 190.723 470.716 210.832 240.433 570.816 230.634 200.609 240.969 60.418 650.344 50.559 520.833 290.715 250.808 80.560 560.902 280.847 250.680 26
JSENetpermissive0.699 280.881 110.762 350.821 140.667 320.800 530.522 170.792 330.613 260.607 260.935 670.492 330.205 620.576 460.853 220.691 330.758 380.652 180.872 520.828 320.649 34
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-IIpermissive0.696 290.704 630.790 230.787 320.709 220.837 200.459 400.815 250.543 550.615 210.956 130.529 220.250 460.551 570.790 370.703 290.799 120.619 330.908 230.848 230.700 21
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 300.743 510.794 210.655 700.684 290.822 340.497 270.719 510.622 230.617 200.977 40.447 520.339 60.750 160.664 600.703 290.790 200.596 420.946 40.855 190.647 35
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 310.884 100.754 410.795 310.647 370.818 410.422 590.802 310.612 270.604 270.945 480.462 430.189 700.563 510.853 220.726 170.765 320.632 260.904 260.821 380.606 49
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 320.704 630.741 500.754 440.656 330.829 260.501 230.741 460.609 300.548 420.950 340.522 250.371 20.633 330.756 400.715 250.771 290.623 300.861 590.814 400.658 31
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 330.866 130.748 430.819 160.645 390.794 560.450 450.802 310.587 400.604 270.945 480.464 420.201 650.554 540.840 270.723 200.732 480.602 400.907 240.822 370.603 52
KP-FCNN0.684 340.847 180.758 390.784 340.647 370.814 440.473 330.772 360.605 320.594 340.935 670.450 500.181 730.587 420.805 350.690 340.785 220.614 340.882 420.819 390.632 40
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 340.728 580.757 400.776 360.690 260.804 510.464 380.816 230.577 450.587 360.945 480.508 300.276 310.671 230.710 500.663 430.750 420.589 470.881 430.832 310.653 33
Superpoint Network0.683 360.851 170.728 540.800 300.653 350.806 490.468 350.804 290.572 460.602 290.946 450.453 490.239 520.519 630.822 300.689 360.762 350.595 440.895 340.827 330.630 41
PointContrast_LA_SEM0.683 360.757 460.784 260.786 330.639 410.824 320.408 620.775 350.604 330.541 440.934 710.532 210.269 380.552 550.777 380.645 530.793 180.640 230.913 220.824 340.671 28
VI-PointConv0.676 380.770 410.754 410.783 350.621 450.814 440.552 70.758 390.571 480.557 400.954 220.529 220.268 400.530 610.682 550.675 380.719 510.603 390.888 390.833 300.665 29
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 390.789 320.748 430.763 420.635 430.814 440.407 640.747 430.581 440.573 370.950 340.484 340.271 360.607 390.754 410.649 480.774 260.596 420.883 410.823 350.606 49
SALANet0.670 400.816 260.770 320.768 390.652 360.807 480.451 420.747 430.659 160.545 430.924 770.473 390.149 850.571 480.811 340.635 560.746 430.623 300.892 360.794 520.570 62
PointASNLpermissive0.666 410.703 650.781 280.751 460.655 340.830 250.471 340.769 370.474 730.537 460.951 300.475 380.279 300.635 310.698 540.675 380.751 410.553 610.816 700.806 440.703 20
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 410.781 340.759 370.699 550.644 400.822 340.475 320.779 340.564 510.504 600.953 250.428 590.203 640.586 440.754 410.661 440.753 400.588 480.902 280.813 420.642 36
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 430.746 490.708 570.722 480.638 420.820 370.451 420.566 770.599 360.541 440.950 340.510 290.313 150.648 280.819 320.616 610.682 660.590 460.869 550.810 430.656 32
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 440.778 350.702 600.806 260.619 460.813 470.468 350.693 590.494 660.524 520.941 590.449 510.298 210.510 650.821 310.675 380.727 500.568 540.826 680.803 460.637 38
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 450.698 660.743 480.650 710.564 630.820 370.505 210.758 390.631 210.479 640.945 480.480 360.226 530.572 470.774 390.690 340.735 460.614 340.853 620.776 660.597 55
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 460.752 470.734 520.664 680.583 580.815 430.399 670.754 410.639 180.535 480.942 570.470 400.309 170.665 240.539 670.650 470.708 560.635 250.857 610.793 540.642 36
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 470.778 350.731 530.699 550.577 590.829 260.446 470.736 470.477 720.523 540.945 480.454 470.269 380.484 720.749 440.618 590.738 440.599 410.827 670.792 570.621 43
MVPNetpermissive0.641 480.831 210.715 550.671 650.590 540.781 620.394 690.679 610.642 170.553 410.937 640.462 430.256 440.649 270.406 800.626 570.691 630.666 160.877 450.792 570.608 48
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 480.776 370.703 590.721 490.557 660.826 290.451 420.672 630.563 520.483 630.943 560.425 620.162 800.644 290.726 460.659 450.709 550.572 510.875 470.786 610.559 67
PointMRNet0.640 500.717 620.701 610.692 580.576 600.801 520.467 370.716 520.563 520.459 690.953 250.429 580.169 770.581 450.854 210.605 620.710 530.550 620.894 350.793 540.575 60
FPConvpermissive0.639 510.785 330.760 360.713 530.603 490.798 540.392 700.534 820.603 340.524 520.948 390.457 450.250 460.538 590.723 480.598 660.696 610.614 340.872 520.799 470.567 64
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 520.797 300.769 330.641 760.590 540.820 370.461 390.537 810.637 190.536 470.947 410.388 720.206 610.656 250.668 580.647 510.732 480.585 490.868 560.793 540.473 85
PointSPNet0.637 530.734 550.692 680.714 520.576 600.797 550.446 470.743 450.598 370.437 740.942 570.403 680.150 840.626 350.800 360.649 480.697 600.557 590.846 640.777 650.563 65
SConv0.636 540.830 220.697 640.752 450.572 620.780 640.445 490.716 520.529 580.530 490.951 300.446 530.170 760.507 670.666 590.636 550.682 660.541 670.886 400.799 470.594 56
Supervoxel-CNN0.635 550.656 710.711 560.719 500.613 470.757 730.444 520.765 380.534 570.566 380.928 750.478 370.272 340.636 300.531 690.664 420.645 760.508 740.864 580.792 570.611 45
joint point-basedpermissive0.634 560.614 780.778 290.667 670.633 440.825 300.420 600.804 290.467 750.561 390.951 300.494 320.291 240.566 490.458 750.579 720.764 330.559 580.838 650.814 400.598 54
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 570.731 560.688 710.675 620.591 530.784 610.444 520.565 780.610 280.492 610.949 370.456 460.254 450.587 420.706 510.599 650.665 720.612 370.868 560.791 600.579 59
PointNet2-SFPN0.631 580.771 390.692 680.672 630.524 700.837 200.440 540.706 570.538 560.446 710.944 540.421 640.219 560.552 550.751 430.591 680.737 450.543 660.901 300.768 680.557 68
APCF-Net0.631 580.742 520.687 730.672 630.557 660.792 590.408 620.665 640.545 540.508 570.952 290.428 590.186 710.634 320.702 520.620 580.706 570.555 600.873 500.798 490.581 58
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF0.631 580.626 750.745 460.801 290.607 480.751 740.506 200.729 500.565 500.491 620.866 910.434 540.197 680.595 400.630 620.709 270.705 580.560 560.875 470.740 760.491 80
FusionAwareConv0.630 610.604 800.741 500.766 410.590 540.747 750.501 230.734 480.503 650.527 500.919 810.454 470.323 120.550 580.420 790.678 370.688 640.544 640.896 330.795 510.627 42
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 620.800 290.625 830.719 500.545 680.806 490.445 490.597 720.448 790.519 550.938 630.481 350.328 100.489 710.499 740.657 460.759 370.592 450.881 430.797 500.634 39
SegGroup_sempermissive0.627 630.818 250.747 450.701 540.602 500.764 700.385 740.629 690.490 680.508 570.931 740.409 670.201 650.564 500.725 470.618 590.692 620.539 680.873 500.794 520.548 71
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 640.830 220.694 660.757 430.563 640.772 680.448 460.647 670.520 600.509 560.949 370.431 570.191 690.496 690.614 630.647 510.672 700.535 700.876 460.783 620.571 61
HPEIN0.618 650.729 570.668 740.647 730.597 520.766 690.414 610.680 600.520 600.525 510.946 450.432 550.215 580.493 700.599 640.638 540.617 810.570 520.897 320.806 440.605 51
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-GCNpermissive0.610 660.858 160.772 310.489 880.532 690.792 590.404 660.643 680.570 490.507 590.935 670.414 660.046 940.510 650.702 520.602 640.705 580.549 630.859 600.773 670.534 74
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 670.760 440.667 750.649 720.521 710.793 570.457 410.648 660.528 590.434 760.947 410.401 690.153 830.454 740.721 490.648 500.717 520.536 690.904 260.765 690.485 81
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 680.634 740.743 480.697 570.601 510.781 620.437 560.585 750.493 670.446 710.933 720.394 700.011 960.654 260.661 610.603 630.733 470.526 710.832 660.761 710.480 82
LAP-D0.594 690.720 600.692 680.637 770.456 800.773 670.391 720.730 490.587 400.445 730.940 610.381 730.288 250.434 770.453 770.591 680.649 740.581 500.777 740.749 750.610 47
DPC0.592 700.720 600.700 620.602 810.480 760.762 720.380 750.713 550.585 430.437 740.940 610.369 750.288 250.434 770.509 730.590 700.639 790.567 550.772 750.755 730.592 57
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 710.766 430.659 780.683 600.470 790.740 770.387 730.620 710.490 680.476 650.922 790.355 780.245 500.511 640.511 720.571 730.643 770.493 780.872 520.762 700.600 53
ROSMRF0.580 720.772 380.707 580.681 610.563 640.764 700.362 770.515 830.465 760.465 680.936 660.427 610.207 600.438 750.577 650.536 760.675 690.486 790.723 810.779 630.524 76
SD-DETR0.576 730.746 490.609 870.445 920.517 720.643 880.366 760.714 540.456 770.468 670.870 900.432 550.264 410.558 530.674 560.586 710.688 640.482 800.739 790.733 780.537 73
SQN_0.1%0.569 740.676 680.696 650.657 690.497 730.779 650.424 580.548 790.515 620.376 810.902 880.422 630.357 40.379 810.456 760.596 670.659 730.544 640.685 840.665 890.556 69
TextureNetpermissive0.566 750.672 700.664 760.671 650.494 740.719 780.445 490.678 620.411 850.396 790.935 670.356 770.225 540.412 790.535 680.565 740.636 800.464 820.794 730.680 860.568 63
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
DVVNet0.562 760.648 720.700 620.770 380.586 570.687 820.333 810.650 650.514 630.475 660.906 850.359 760.223 550.340 830.442 780.422 870.668 710.501 750.708 820.779 630.534 74
Pointnet++ & Featurepermissive0.557 770.735 540.661 770.686 590.491 750.744 760.392 700.539 800.451 780.375 820.946 450.376 740.205 620.403 800.356 830.553 750.643 770.497 760.824 690.756 720.515 77
GMLPs0.538 780.495 880.693 670.647 730.471 780.793 570.300 840.477 840.505 640.358 830.903 870.327 810.081 910.472 730.529 700.448 850.710 530.509 720.746 770.737 770.554 70
PanopticFusion-label0.529 790.491 890.688 710.604 800.386 850.632 890.225 940.705 580.434 820.293 890.815 920.348 790.241 510.499 680.669 570.507 780.649 740.442 880.796 720.602 920.561 66
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_weak0.516 800.676 680.591 900.609 780.442 810.774 660.335 800.597 720.422 840.357 840.932 730.341 800.094 900.298 850.528 710.473 830.676 680.495 770.602 900.721 810.349 92
Online SegFusion0.515 810.607 790.644 810.579 830.434 820.630 900.353 780.628 700.440 800.410 770.762 950.307 830.167 780.520 620.403 810.516 770.565 840.447 860.678 850.701 830.514 78
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, FTSDF0.501 820.558 840.608 880.424 940.478 770.690 810.246 900.586 740.468 740.450 700.911 830.394 700.160 810.438 750.212 900.432 860.541 890.475 810.742 780.727 790.477 83
PCNN0.498 830.559 830.644 810.560 850.420 840.711 800.229 920.414 850.436 810.352 850.941 590.324 820.155 820.238 900.387 820.493 790.529 900.509 720.813 710.751 740.504 79
3DMV0.484 840.484 900.538 920.643 750.424 830.606 930.310 820.574 760.433 830.378 800.796 930.301 840.214 590.537 600.208 910.472 840.507 930.413 910.693 830.602 920.539 72
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 850.577 820.611 860.356 960.321 930.715 790.299 860.376 890.328 920.319 870.944 540.285 860.164 790.216 930.229 880.484 810.545 880.456 840.755 760.709 820.475 84
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 860.679 670.604 890.578 840.380 860.682 830.291 870.106 950.483 710.258 940.920 800.258 900.025 950.231 920.325 840.480 820.560 860.463 830.725 800.666 880.231 96
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 870.474 910.623 840.463 900.366 880.651 860.310 820.389 880.349 900.330 860.937 640.271 880.126 870.285 860.224 890.350 920.577 830.445 870.625 880.723 800.394 88
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
SurfaceConvPF0.442 880.505 870.622 850.380 950.342 910.654 850.227 930.397 870.367 880.276 910.924 770.240 910.198 670.359 820.262 860.366 890.581 820.435 890.640 870.668 870.398 87
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 880.548 850.548 910.597 820.363 890.628 910.300 840.292 900.374 870.307 880.881 890.268 890.186 710.238 900.204 920.407 880.506 940.449 850.667 860.620 910.462 86
Tangent Convolutionspermissive0.438 900.437 930.646 800.474 890.369 870.645 870.353 780.258 920.282 940.279 900.918 820.298 850.147 860.283 870.294 850.487 800.562 850.427 900.619 890.633 900.352 91
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 910.525 860.647 790.522 860.324 920.488 960.077 970.712 560.353 890.401 780.636 970.281 870.176 740.340 830.565 660.175 960.551 870.398 920.370 960.602 920.361 90
SimConv0.410 920.000 970.782 270.772 370.722 190.838 190.407 640.000 980.000 980.595 330.947 410.000 980.270 370.000 980.000 980.000 980.786 210.621 320.000 980.841 290.621 43
SPLAT Netcopyleft0.393 930.472 920.511 930.606 790.311 940.656 840.245 910.405 860.328 920.197 950.927 760.227 930.000 980.001 970.249 870.271 950.510 910.383 940.593 910.699 840.267 94
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+FTSDF0.383 940.297 950.491 940.432 930.358 900.612 920.274 880.116 940.411 850.265 920.904 860.229 920.079 920.250 880.185 930.320 930.510 910.385 930.548 920.597 950.394 88
PointNet++permissive0.339 950.584 810.478 950.458 910.256 960.360 970.250 890.247 930.278 950.261 930.677 960.183 940.117 880.212 940.145 950.364 900.346 970.232 970.548 920.523 960.252 95
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 960.353 940.290 970.278 970.166 970.553 940.169 960.286 910.147 960.148 970.908 840.182 950.064 930.023 960.018 970.354 910.363 950.345 950.546 940.685 850.278 93
ScanNetpermissive0.306 970.203 960.366 960.501 870.311 940.524 950.211 950.002 970.342 910.189 960.786 940.145 960.102 890.245 890.152 940.318 940.348 960.300 960.460 950.437 970.182 97
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 980.000 970.041 980.172 980.030 980.062 980.001 980.035 960.004 970.051 980.143 980.019 970.003 970.041 950.050 960.003 970.054 980.018 980.005 970.264 980.082 98


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
Mask3D0.780 11.000 10.786 240.716 230.696 40.885 40.500 20.714 170.810 20.672 30.715 30.679 60.809 11.000 10.831 10.833 70.787 31.000 10.602 5
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SPFormerpermissive0.770 20.903 360.903 10.806 110.609 130.886 30.568 10.815 60.705 50.711 10.655 40.652 90.685 91.000 10.789 30.809 120.776 51.000 10.583 9
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 31.000 10.803 180.937 10.684 50.865 60.213 180.870 20.664 70.571 80.758 10.702 40.807 21.000 10.653 150.902 10.792 21.000 10.626 1
SoftGrouppermissive0.761 41.000 10.808 150.845 70.716 10.862 80.243 150.824 30.655 90.620 40.734 20.699 50.791 40.981 230.716 60.844 40.769 61.000 10.594 8
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 51.000 10.774 250.867 60.621 90.934 10.404 50.706 180.812 10.605 60.633 90.626 100.690 81.000 10.640 170.820 90.777 41.000 10.612 3
PBNetpermissive0.747 61.000 10.818 110.837 90.713 20.844 100.457 40.647 250.711 40.614 50.617 100.657 80.650 111.000 10.692 90.822 80.765 71.000 10.595 7
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. Arxiv
GraphCut0.732 71.000 10.788 220.724 220.642 80.859 90.248 140.787 100.618 130.596 70.653 60.722 20.583 281.000 10.766 40.861 20.825 11.000 10.504 20
IPCA-Inst0.731 81.000 10.788 230.884 50.698 30.788 250.252 130.760 120.646 100.511 160.637 80.665 70.804 31.000 10.644 160.778 150.747 91.000 10.561 14
TopoSeg0.725 91.000 10.806 170.933 20.668 70.758 280.272 110.734 160.630 110.549 120.654 50.606 110.697 70.966 250.612 210.839 50.754 81.000 10.573 10
DKNet0.718 101.000 10.814 120.782 140.619 100.872 50.224 160.751 140.569 150.677 20.585 130.724 10.633 190.981 230.515 290.819 100.736 101.000 10.617 2
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.699 111.000 10.848 60.763 200.609 140.792 230.262 120.824 30.627 120.535 140.547 210.481 180.600 211.000 10.712 80.731 270.689 141.000 10.563 13
HAISpermissive0.699 111.000 10.849 50.820 100.675 60.808 180.279 90.757 130.465 200.517 150.596 110.559 130.600 211.000 10.654 140.767 170.676 150.994 320.560 15
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 131.000 10.697 410.888 40.556 200.803 190.387 60.626 270.417 240.556 110.585 140.702 30.600 211.000 10.824 20.720 300.692 121.000 10.509 19
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SphereSeg0.680 141.000 10.856 40.744 210.618 110.893 20.151 200.651 240.713 30.537 130.579 160.430 260.651 101.000 10.389 380.744 240.697 110.991 340.601 6
Box2Mask0.677 151.000 10.847 70.771 160.509 280.816 140.277 100.558 340.482 170.562 100.640 70.448 220.700 51.000 10.666 100.852 30.578 280.997 270.488 24
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 161.000 10.758 330.682 260.576 180.842 110.477 30.504 380.524 160.567 90.585 150.451 210.557 291.000 10.751 50.797 130.563 311.000 10.467 28
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 171.000 10.822 100.764 190.616 120.815 150.139 240.694 200.597 140.459 220.566 170.599 120.600 210.516 450.715 70.819 110.635 201.000 10.603 4
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 181.000 10.760 310.667 280.581 160.863 70.323 70.655 230.477 180.473 200.549 190.432 250.650 111.000 10.655 130.738 250.585 270.944 380.472 27
CSC-Pretrained0.648 191.000 10.810 130.768 170.523 260.813 160.143 230.819 50.389 270.422 300.511 240.443 230.650 111.000 10.624 190.732 260.634 211.000 10.375 35
PE0.645 201.000 10.773 270.798 130.538 220.786 260.088 310.799 90.350 310.435 290.547 200.545 140.646 180.933 260.562 240.761 200.556 360.997 270.501 22
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 211.000 10.758 320.582 370.539 210.826 130.046 350.765 110.372 290.436 280.588 120.539 160.650 111.000 10.577 220.750 220.653 180.997 270.495 23
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 221.000 10.841 80.893 30.531 240.802 200.115 280.588 320.448 210.438 260.537 230.430 270.550 300.857 280.534 270.764 190.657 160.987 350.568 11
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 231.000 10.895 30.800 120.480 320.676 340.144 220.737 150.354 300.447 230.400 360.365 320.700 51.000 10.569 230.836 60.599 231.000 10.473 26
PointGroup0.636 241.000 10.765 280.624 300.505 300.797 210.116 270.696 190.384 280.441 240.559 180.476 190.596 251.000 10.666 100.756 210.556 350.997 270.513 18
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+Group0.635 250.667 370.797 210.714 240.562 190.774 270.146 210.810 80.429 230.476 190.546 220.399 290.633 191.000 10.632 180.722 290.609 221.000 10.514 17
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
DENet0.629 261.000 10.797 200.608 310.589 150.627 380.219 170.882 10.310 330.402 350.383 380.396 300.650 111.000 10.663 120.543 460.691 131.000 10.568 12
3D-MPA0.611 271.000 10.833 90.765 180.526 250.756 290.136 260.588 320.470 190.438 270.432 330.358 330.650 110.857 280.429 340.765 180.557 341.000 10.430 30
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 281.000 10.801 190.599 320.535 230.728 320.286 80.436 420.679 60.491 170.433 310.256 350.404 420.857 280.620 200.724 280.510 401.000 10.539 16
AOIA0.601 291.000 10.761 300.687 250.485 310.828 120.008 410.663 220.405 260.405 340.425 340.490 170.596 250.714 380.553 260.779 140.597 240.992 330.424 32
PCJC0.578 301.000 10.810 140.583 360.449 350.813 170.042 360.603 300.341 320.490 180.465 280.410 280.650 110.835 360.264 440.694 340.561 320.889 420.504 21
SSEN0.575 311.000 10.761 290.473 390.477 330.795 220.066 320.529 350.658 80.460 210.461 290.380 310.331 440.859 270.401 370.692 360.653 171.000 10.348 37
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 320.528 470.708 400.626 290.580 170.745 310.063 330.627 260.240 370.400 360.497 250.464 200.515 311.000 10.475 310.745 230.571 291.000 10.429 31
NeuralBF0.555 330.667 370.896 20.843 80.517 270.751 300.029 370.519 360.414 250.439 250.465 270.000 530.484 330.857 280.287 420.693 350.651 191.000 10.485 25
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
MTML0.549 341.000 10.807 160.588 350.327 400.647 360.004 430.815 70.180 390.418 310.364 400.182 380.445 361.000 10.442 330.688 370.571 301.000 10.396 33
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
One_Thing_One_Clickpermissive0.529 350.667 370.718 360.777 150.399 360.683 330.000 460.669 210.138 420.391 370.374 390.539 150.360 430.641 420.556 250.774 160.593 250.997 270.251 42
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-CNN0.515 361.000 10.538 480.282 420.468 340.790 240.173 190.345 440.429 220.413 330.484 260.176 390.595 270.591 430.522 280.668 380.476 410.986 360.327 38
Occipital-SCS0.512 371.000 10.716 370.509 380.506 290.611 390.092 300.602 310.177 400.346 400.383 370.165 400.442 370.850 350.386 390.618 420.543 370.889 420.389 34
3D-BoNet0.488 381.000 10.672 430.590 340.301 420.484 490.098 290.620 280.306 340.341 410.259 440.125 420.434 390.796 370.402 360.499 480.513 390.909 410.439 29
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-inst0.478 390.667 370.712 390.595 330.259 450.550 450.000 460.613 290.175 410.250 460.434 300.437 240.411 410.857 280.485 300.591 450.267 510.944 380.359 36
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_WSIS0.470 400.667 370.685 420.677 270.372 380.562 430.000 460.482 390.244 360.316 430.298 410.052 480.442 380.857 280.267 430.702 310.559 331.000 10.287 40
SALoss-ResNet0.459 411.000 10.737 350.159 520.259 440.587 410.138 250.475 400.217 380.416 320.408 350.128 410.315 450.714 380.411 350.536 470.590 260.873 450.304 39
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)
MASCpermissive0.447 420.528 470.555 460.381 400.382 370.633 370.002 440.509 370.260 350.361 390.432 320.327 340.451 350.571 440.367 400.639 400.386 420.980 370.276 41
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 430.667 370.773 260.185 490.317 410.656 350.000 460.407 430.134 430.381 380.267 430.217 370.476 340.714 380.452 320.629 410.514 381.000 10.222 45
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 441.000 10.432 500.245 440.190 460.577 420.013 400.263 460.033 490.320 420.240 450.075 440.422 400.857 280.117 480.699 320.271 500.883 440.235 44
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 450.667 370.542 470.264 430.157 490.550 440.000 460.205 490.009 500.270 450.218 460.075 440.500 320.688 410.007 540.698 330.301 470.459 510.200 46
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 460.667 370.715 380.233 450.189 470.479 500.008 410.218 470.067 480.201 480.173 470.107 430.123 500.438 460.150 460.615 430.355 430.916 400.093 53
R-PointNet0.306 470.500 490.405 510.311 410.348 390.589 400.054 340.068 520.126 440.283 440.290 420.028 490.219 480.214 490.331 410.396 520.275 480.821 470.245 43
Region-18class0.284 480.250 530.751 340.228 470.270 430.521 460.000 460.468 410.008 520.205 470.127 480.000 530.068 520.070 520.262 450.652 390.323 450.740 480.173 47
SemRegionNet-20cls0.250 490.333 500.613 440.229 460.163 480.493 470.000 460.304 450.107 450.147 500.100 490.052 470.231 460.119 500.039 500.445 500.325 440.654 490.141 49
tmp0.248 500.667 370.437 490.188 480.153 500.491 480.000 460.208 480.094 470.153 490.099 500.057 460.217 490.119 500.039 500.466 490.302 460.640 500.140 50
3D-BEVIS0.248 500.667 370.566 450.076 530.035 540.394 520.027 390.035 530.098 460.099 520.030 530.025 500.098 510.375 480.126 470.604 440.181 520.854 460.171 48
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
ASIS0.199 520.333 500.253 530.167 510.140 510.438 510.000 460.177 500.008 510.121 510.069 510.004 520.231 470.429 470.036 520.445 510.273 490.333 530.119 52
Sgpn_scannet0.143 530.208 540.390 520.169 500.065 520.275 530.029 380.069 510.000 530.087 530.043 520.014 510.027 540.000 530.112 490.351 530.168 530.438 520.138 51
MaskRCNN 2d->3d Proj0.058 540.333 500.002 540.000 540.053 530.002 540.002 450.021 540.000 530.045 540.024 540.238 360.065 530.000 530.014 530.107 540.020 540.110 540.006 54


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 10.422 140.898 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 20.451 100.769 30.656 30.567 30.931 30.395 40.390 40.700 30.534 30.689 80.770 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 20.179 190.648 30.463 30.549 20.742 60.676 20.628 20.961 10.420 20.379 50.684 60.381 140.732 20.723 30.599 20.827 120.851 20.634 6
CMX0.613 40.681 70.725 80.502 110.634 50.297 150.478 80.830 20.651 40.537 60.924 40.375 50.315 110.686 50.451 110.714 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 50.856 170.359 90.306 120.596 110.539 20.627 170.706 40.497 70.785 170.757 150.476 18
MCA-Net0.595 60.533 160.756 60.746 40.590 80.334 70.506 50.670 110.587 70.500 100.905 80.366 80.352 80.601 100.506 50.669 140.648 70.501 60.839 110.769 110.516 17
RFBNet0.592 70.616 90.758 50.659 50.581 90.330 80.469 90.655 140.543 120.524 70.924 40.355 100.336 100.572 130.479 70.671 120.648 70.480 90.814 150.814 50.614 9
FAN_NV_RVC0.586 80.510 170.764 40.079 220.620 70.330 80.494 60.753 40.573 80.556 40.884 120.405 30.303 130.718 20.452 100.672 110.658 50.509 40.898 30.813 60.727 2
DCRedNet0.583 90.682 60.723 90.542 100.510 160.310 120.451 100.668 120.549 110.520 80.920 60.375 50.446 20.528 160.417 120.670 130.577 150.478 100.862 70.806 70.628 8
MIX6D_RVC0.582 100.695 40.687 130.225 170.632 60.328 100.550 10.748 50.623 50.494 130.890 100.350 110.254 190.688 40.454 90.716 30.597 140.489 80.881 50.768 120.575 11
SSMAcopyleft0.577 110.695 40.716 110.439 130.563 110.314 110.444 120.719 80.551 100.503 90.887 110.346 120.348 90.603 90.353 160.709 50.600 120.457 120.901 20.786 80.599 10
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE0.566 120.569 150.686 150.435 140.524 130.294 160.421 150.712 90.543 120.463 150.872 130.320 130.363 70.611 80.477 80.686 90.627 90.443 150.862 70.775 100.639 5
SN_RN152pyrx8_RVCcopyleft0.546 130.572 130.663 170.638 70.518 140.298 140.366 200.633 170.510 150.446 170.864 150.296 160.267 160.542 150.346 170.704 60.575 160.431 160.853 100.766 130.630 7
UDSSEG_RVC0.545 140.610 110.661 180.588 80.556 120.268 180.482 70.642 160.572 90.475 140.836 190.312 140.367 60.630 70.189 190.639 160.495 200.452 130.826 130.756 160.541 13
segfomer with 6d0.542 150.594 120.687 130.146 200.579 100.308 130.515 40.703 100.472 170.498 110.868 140.369 70.282 140.589 120.390 130.701 70.556 170.416 180.860 90.759 140.539 15
FuseNetpermissive0.535 160.570 140.681 160.182 180.512 150.290 170.431 130.659 130.504 160.495 120.903 90.308 150.428 30.523 170.365 150.676 100.621 110.470 110.762 180.779 90.541 13
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 170.613 100.722 100.418 150.358 220.337 60.370 190.479 200.443 180.368 200.907 70.207 190.213 210.464 200.525 40.618 180.657 60.450 140.788 160.721 190.408 21
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 180.481 200.612 190.579 90.456 180.343 50.384 170.623 180.525 140.381 190.845 180.254 180.264 180.557 140.182 200.581 200.598 130.429 170.760 190.661 210.446 20
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 190.505 180.709 120.092 210.427 190.241 190.411 160.654 150.385 220.457 160.861 160.053 220.279 150.503 180.481 60.645 150.626 100.365 200.748 200.725 180.529 16
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 200.490 190.581 200.289 160.507 170.067 220.379 180.610 190.417 200.435 180.822 210.278 170.267 160.503 180.228 180.616 190.533 190.375 190.820 140.729 170.560 12
Enet (reimpl)0.376 210.264 220.452 220.452 120.365 200.181 200.143 220.456 210.409 210.346 210.769 220.164 200.218 200.359 210.123 220.403 220.381 220.313 220.571 210.685 200.472 19
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)permissive0.330 220.293 210.521 210.657 60.361 210.161 210.250 210.004 220.440 190.183 220.836 190.125 210.060 220.319 220.132 210.417 210.412 210.344 210.541 220.427 220.109 22
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF0.003 230.000 230.005 230.000 230.000 230.037 230.001 230.000 230.001 230.005 230.003 230.000 230.000 230.000 230.000 230.000 230.002 230.001 230.000 230.006 230.000 23


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 recallapartmentbathroombedroom / 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-taskpermissive0.700 10.500 11.000 10.882 20.500 21.000 11.000 10.500 21.000 11.000 10.778 10.000 20.938 10.000 2
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE0.691 20.500 10.938 20.824 31.000 11.000 10.500 21.000 10.857 20.500 20.556 30.000 20.812 20.500 1
SE-ResNeXt-SSMA0.498 30.000 40.812 30.941 10.500 20.500 30.500 20.500 20.429 40.500 20.667 20.500 10.625 30.000 2
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 40.250 30.812 30.529 40.500 20.500 30.000 40.500 20.571 30.000 40.556 30.000 20.375 40.000 2