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 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 by
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
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.
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
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


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 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 by
Mask3D Scannet2000.388 10.542 10.357 10.237 10.610 10.091 10.125 40.000 10.000 10.000 10.065 20.668 10.451 11.000 10.955 10.640 10.500 10.039 10.125 20.063 10.409 10.311 10.291 10.609 20.266 10.000 10.163 10.000 10.008 10.044 10.496 11.000 10.000 10.018 10.000 10.756 10.573 10.808 10.000 10.010 10.042 20.130 20.552 10.042 10.000 11.000 10.725 30.750 10.883 11.000 10.832 30.024 10.107 10.614 20.226 10.250 10.628 10.792 10.677 20.400 10.741 10.278 10.511 10.077 40.111 10.313 10.715 10.302 10.017 20.200 10.000 10.188 10.000 10.178 10.736 11.000 10.615 10.514 10.409 10.380 40.600 10.000 10.000 10.400 10.013 10.254 10.381 10.000 10.123 30.400 10.839 10.258 10.463 10.926 10.265 10.000 10.857 10.099 10.021 10.500 10.027 10.028 11.000 10.502 40.016 10.076 30.500 10.612 10.578 10.005 10.597 10.194 10.497 10.000 10.500 10.000 10.323 30.000 11.000 10.000 10.748 10.708 20.050 30.890 11.000 10.008 10.151 20.301 11.000 11.000 10.792 20.945 11.000 10.511 10.004 10.753 10.776 10.287 10.020 10.003 30.974 20.033 10.412 40.000 10.000 10.000 10.667 10.000 10.000 10.491 10.676 10.352 10.335 10.060 10.822 40.527 11.000 10.517 10.606 10.853 10.000 10.004 10.806 11.000 10.727 10.000 10.042 10.739 10.000 10.399 20.391 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.
LGround Inst.permissive0.246 20.413 20.170 20.130 20.455 40.003 40.500 10.000 10.000 10.000 10.017 30.333 30.111 41.000 10.681 30.400 20.000 20.000 21.000 10.003 40.000 20.167 20.190 20.637 10.067 20.000 10.081 20.000 10.000 20.000 20.264 30.000 20.000 10.000 20.000 10.387 30.031 40.754 20.000 10.000 20.151 10.135 10.056 30.000 20.000 10.582 30.589 40.500 20.815 21.000 10.903 10.000 20.097 20.588 30.000 20.000 20.234 20.000 20.500 30.400 10.682 30.156 20.159 30.750 10.046 20.125 30.660 20.000 20.200 10.000 40.000 10.000 20.000 10.164 20.402 20.500 20.373 20.025 20.143 40.426 20.317 20.000 10.000 10.000 20.000 20.063 20.000 20.000 10.000 40.000 30.575 30.250 20.241 20.772 20.000 20.000 10.653 30.034 20.000 20.000 20.000 20.000 21.000 10.561 30.000 20.100 20.500 10.541 40.452 20.000 20.581 20.000 20.364 20.000 10.000 20.000 10.571 10.000 10.000 20.000 10.568 40.511 30.167 20.857 20.000 20.000 20.164 10.112 20.000 40.530 41.000 10.286 20.000 20.125 20.000 20.464 40.706 20.208 30.000 20.125 10.744 30.000 20.500 10.000 10.000 10.000 10.511 20.000 10.000 10.344 20.541 20.068 20.333 20.000 21.000 10.196 30.533 30.318 20.000 30.748 30.000 10.000 20.690 21.000 10.400 30.000 10.000 20.667 20.000 10.333 30.333 20.270 30.399 20.083 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.209 30.361 40.157 30.085 30.506 20.007 20.500 10.000 10.000 10.000 10.000 40.093 40.221 30.667 30.524 40.400 20.000 20.000 20.000 30.004 30.000 20.000 40.109 40.589 30.000 30.000 10.059 40.000 10.000 20.000 20.322 20.000 20.000 10.000 20.000 10.405 20.055 30.700 40.000 10.000 20.028 30.091 40.083 20.000 20.000 10.667 20.768 10.000 30.807 31.000 10.776 40.000 20.000 40.340 40.000 20.000 20.103 40.000 20.750 10.200 30.634 40.053 40.246 20.677 20.006 40.198 20.432 30.000 20.000 30.050 30.000 10.000 20.000 10.111 40.356 30.500 20.188 40.000 30.220 30.448 10.050 40.000 10.000 10.000 20.000 20.032 40.000 20.000 10.396 10.000 30.573 40.000 40.228 30.747 30.000 20.000 10.573 40.021 40.000 20.000 20.000 20.000 20.500 30.573 20.000 20.000 40.125 40.592 30.364 40.000 20.450 40.000 20.364 20.000 10.000 20.000 10.340 20.000 10.000 20.000 10.610 30.833 10.221 10.702 40.000 20.000 20.135 40.094 30.125 20.571 30.500 30.143 40.000 20.125 20.000 20.618 20.667 30.115 40.000 20.125 11.000 10.000 20.500 10.000 10.000 10.000 10.502 30.000 10.000 10.312 30.248 40.050 30.000 40.000 20.997 20.420 20.500 40.149 40.451 20.748 20.000 10.000 20.636 30.667 40.600 20.000 10.000 20.278 40.000 10.333 30.000 40.294 20.381 40.110 2
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 40.369 30.134 40.078 40.479 30.003 30.500 10.000 10.000 10.000 10.100 10.371 20.300 20.667 30.746 20.400 20.000 20.000 20.000 30.031 20.000 20.074 30.165 30.413 40.000 30.000 10.070 30.000 10.000 20.000 20.221 40.000 20.000 10.000 20.000 10.372 40.070 20.706 30.000 10.000 20.000 40.123 30.033 40.000 20.000 10.422 40.732 20.000 30.778 41.000 10.845 20.000 20.090 30.636 10.000 20.000 20.158 30.000 20.250 40.050 40.693 20.123 30.051 40.385 30.009 30.118 40.406 40.000 20.000 30.200 10.000 10.000 20.000 10.133 30.307 40.500 20.251 30.000 30.281 20.402 30.317 20.000 10.000 10.000 20.000 20.060 30.000 20.000 10.396 10.200 20.669 20.021 30.218 40.720 40.000 20.000 10.696 20.025 30.000 20.000 20.000 20.000 20.125 40.596 10.000 20.191 10.500 10.595 20.369 30.000 20.500 30.000 20.143 40.000 10.000 20.000 10.226 40.000 10.000 20.000 10.701 20.511 30.000 40.851 30.000 20.000 20.150 30.052 40.100 30.981 20.500 30.286 20.000 20.000 40.000 20.545 30.522 40.250 20.000 20.000 40.522 40.000 20.500 10.000 10.000 10.000 10.282 40.000 10.000 10.178 40.382 30.018 40.056 30.000 20.997 20.107 40.677 20.313 30.000 30.726 40.000 10.000 20.583 40.903 30.200 40.000 10.000 20.333 30.000 10.442 10.083 30.109 40.387 30.000 4
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 20.831 170.685 50.714 10.979 10.594 30.310 160.801 10.892 70.841 20.819 30.723 20.940 70.887 10.725 10
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
OccuSeg+Semantic0.764 20.758 420.796 160.839 110.746 80.907 10.562 30.850 120.680 70.672 50.978 20.610 10.335 80.777 40.819 290.847 10.830 10.691 70.972 10.885 20.727 8
O-CNNpermissive0.762 30.924 20.823 40.844 90.770 20.852 90.577 10.847 130.711 10.640 130.958 90.592 40.217 530.762 100.888 80.758 80.813 50.726 10.932 130.868 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 40.906 40.793 190.802 250.689 220.825 250.556 40.867 80.681 60.602 250.960 70.555 140.365 30.779 30.859 150.747 100.795 170.717 30.917 160.856 150.764 2
PointTransformerV20.752 40.742 490.809 110.872 10.758 40.860 60.552 50.891 50.610 250.687 20.960 70.559 120.304 190.766 80.926 20.767 60.797 130.644 170.942 50.876 70.722 12
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 60.793 280.790 200.807 220.750 70.856 80.524 120.881 60.588 350.642 120.977 40.591 50.274 310.781 20.929 10.804 30.796 140.642 180.947 30.885 20.715 14
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 60.909 30.818 70.811 190.752 60.839 150.485 270.842 150.673 80.644 100.957 110.528 210.305 180.773 60.859 150.788 40.818 40.693 60.916 170.856 150.723 11
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 80.623 730.804 120.859 30.745 90.824 270.501 190.912 20.690 40.685 30.956 120.567 90.320 130.768 70.918 30.720 180.802 90.676 90.921 150.881 40.779 1
StratifiedFormerpermissive0.747 90.901 50.803 130.845 80.757 50.846 110.512 150.825 180.696 30.645 90.956 120.576 70.262 390.744 150.861 140.742 110.770 280.705 40.899 270.860 120.734 5
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 MVFusion0.746 100.771 360.819 60.848 60.702 200.865 50.397 650.899 30.699 20.664 60.948 360.588 60.330 90.746 140.851 210.764 70.796 140.704 50.935 100.866 90.728 6
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 100.870 100.838 20.858 40.729 130.850 100.501 190.874 70.587 360.658 70.956 120.564 100.299 200.765 90.900 50.716 210.812 60.631 230.939 80.858 130.709 15
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-FPN0.744 120.842 170.800 140.767 370.740 100.836 180.541 70.914 10.672 90.626 140.958 90.552 150.272 320.777 40.886 100.696 280.801 100.674 100.941 60.858 130.717 13
EQ-Net0.743 130.620 740.799 150.849 50.730 120.822 290.493 250.897 40.664 100.681 40.955 160.562 110.378 10.760 110.903 40.738 120.801 100.673 110.907 200.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
MinkowskiNetpermissive0.736 140.859 130.818 70.832 120.709 170.840 140.521 140.853 110.660 120.643 110.951 260.544 160.286 260.731 160.893 60.675 350.772 260.683 80.874 460.852 180.727 8
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 150.890 60.837 30.864 20.726 140.873 20.530 110.824 190.489 680.647 80.978 20.609 20.336 70.624 320.733 420.758 80.776 240.570 480.949 20.877 50.728 6
PointTransformer++0.725 160.727 560.811 100.819 150.765 30.841 130.502 180.814 240.621 210.623 150.955 160.556 130.284 270.620 330.866 120.781 50.757 350.648 150.932 130.862 110.709 15
SparseConvNet0.725 160.647 700.821 50.846 70.721 150.869 30.533 90.754 380.603 310.614 180.955 160.572 80.325 110.710 170.870 110.724 160.823 20.628 240.934 110.865 100.683 21
MatchingNet0.724 180.812 250.812 90.810 200.735 110.834 190.495 240.860 100.572 420.602 250.954 190.512 240.280 280.757 120.845 240.725 150.780 220.606 330.937 90.851 190.700 18
INS-Conv-semantic0.717 190.751 450.759 330.812 180.704 190.868 40.537 80.842 150.609 270.608 210.953 210.534 170.293 220.616 340.864 130.719 200.793 180.640 190.933 120.845 230.663 26
PointMetaBase0.714 200.835 180.785 230.821 130.684 240.846 110.531 100.865 90.614 220.596 280.953 210.500 270.246 450.674 180.888 80.692 290.764 300.624 250.849 600.844 240.675 23
contrastBoundarypermissive0.705 210.769 390.775 270.809 210.687 230.820 320.439 520.812 250.661 110.591 300.945 450.515 230.171 710.633 290.856 170.720 180.796 140.668 120.889 350.847 210.689 20
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 220.889 70.745 420.813 170.672 270.818 360.493 250.815 220.623 190.610 190.947 390.470 370.249 440.594 380.848 220.705 250.779 230.646 160.892 330.823 310.611 42
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 Click0.701 230.825 220.796 160.723 440.716 160.832 200.433 540.816 200.634 170.609 200.969 60.418 630.344 50.559 500.833 260.715 220.808 70.560 520.902 240.847 210.680 22
JSENetpermissive0.699 240.881 90.762 310.821 130.667 280.800 490.522 130.792 300.613 230.607 220.935 640.492 300.205 580.576 440.853 190.691 300.758 340.652 140.872 490.828 280.649 31
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-IIpermissive0.696 250.704 600.790 200.787 290.709 170.837 160.459 370.815 220.543 520.615 170.956 120.529 190.250 420.551 550.790 340.703 260.799 120.619 280.908 190.848 200.700 18
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
CU-Hybrid Net0.693 260.596 780.789 220.803 240.677 260.800 490.469 310.846 140.554 500.591 300.948 360.500 270.316 140.609 350.847 230.732 130.808 70.593 400.894 310.839 250.652 30
One-Thing-One-Click0.693 260.743 480.794 180.655 670.684 240.822 290.497 230.719 480.622 200.617 160.977 40.447 500.339 60.750 130.664 570.703 260.790 200.596 370.946 40.855 170.647 32
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 280.884 80.754 370.795 280.647 330.818 360.422 560.802 280.612 240.604 230.945 450.462 410.189 660.563 490.853 190.726 140.765 290.632 220.904 220.821 340.606 46
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 290.704 600.741 460.754 410.656 290.829 220.501 190.741 430.609 270.548 380.950 300.522 220.371 20.633 290.756 370.715 220.771 270.623 260.861 560.814 360.658 27
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 300.866 110.748 390.819 150.645 350.794 530.450 420.802 280.587 360.604 230.945 450.464 400.201 610.554 520.840 250.723 170.732 440.602 350.907 200.822 330.603 49
KP-FCNN0.684 310.847 160.758 350.784 310.647 330.814 390.473 290.772 330.605 290.594 290.935 640.450 480.181 690.587 390.805 320.690 310.785 210.614 290.882 390.819 350.632 37
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 310.728 550.757 360.776 330.690 210.804 470.464 350.816 200.577 410.587 320.945 450.508 260.276 300.671 190.710 470.663 400.750 380.589 430.881 400.832 270.653 29
Superpoint Network0.683 330.851 150.728 510.800 270.653 310.806 450.468 320.804 260.572 420.602 250.946 420.453 470.239 480.519 610.822 270.689 330.762 320.595 390.895 300.827 290.630 38
PointContrast_LA_SEM0.683 330.757 430.784 240.786 300.639 370.824 270.408 600.775 320.604 300.541 400.934 680.532 180.269 350.552 530.777 350.645 500.793 180.640 190.913 180.824 300.671 24
VI-PointConv0.676 350.770 380.754 370.783 320.621 410.814 390.552 50.758 360.571 440.557 360.954 190.529 190.268 370.530 590.682 520.675 350.719 470.603 340.888 360.833 260.665 25
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 360.789 290.748 390.763 390.635 390.814 390.407 620.747 400.581 400.573 330.950 300.484 310.271 340.607 360.754 380.649 450.774 250.596 370.883 380.823 310.606 46
SALANet0.670 370.816 240.770 290.768 360.652 320.807 440.451 390.747 400.659 130.545 390.924 740.473 360.149 810.571 460.811 310.635 530.746 390.623 260.892 330.794 490.570 59
PointConvpermissive0.666 380.781 310.759 330.699 520.644 360.822 290.475 280.779 310.564 470.504 560.953 210.428 570.203 600.586 410.754 380.661 410.753 360.588 440.902 240.813 380.642 33
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 380.703 620.781 250.751 430.655 300.830 210.471 300.769 340.474 710.537 420.951 260.475 350.279 290.635 270.698 510.675 350.751 370.553 570.816 680.806 400.703 17
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++permissive0.663 400.746 460.708 540.722 450.638 380.820 320.451 390.566 750.599 330.541 400.950 300.510 250.313 150.648 240.819 290.616 590.682 620.590 420.869 520.810 390.656 28
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 410.778 320.702 570.806 230.619 420.813 420.468 320.693 560.494 640.524 480.941 560.449 490.298 210.510 630.821 280.675 350.727 460.568 500.826 650.803 420.637 35
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 420.698 630.743 440.650 680.564 600.820 320.505 170.758 360.631 180.479 610.945 450.480 330.226 490.572 450.774 360.690 310.735 420.614 290.853 590.776 630.597 52
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 430.752 440.734 480.664 650.583 540.815 380.399 640.754 380.639 150.535 440.942 540.470 370.309 170.665 200.539 650.650 440.708 520.635 210.857 580.793 510.642 33
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 440.778 320.731 490.699 520.577 550.829 220.446 440.736 440.477 700.523 500.945 450.454 450.269 350.484 700.749 410.618 570.738 400.599 360.827 640.792 540.621 40
MVPNetpermissive0.641 450.831 190.715 520.671 620.590 500.781 590.394 660.679 590.642 140.553 370.937 610.462 410.256 400.649 230.406 780.626 540.691 590.666 130.877 420.792 540.608 45
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 450.776 340.703 560.721 460.557 630.826 240.451 390.672 610.563 480.483 600.943 530.425 600.162 760.644 250.726 430.659 420.709 510.572 470.875 440.786 580.559 64
PointMRNet0.640 470.717 590.701 580.692 550.576 560.801 480.467 340.716 490.563 480.459 660.953 210.429 560.169 730.581 420.854 180.605 600.710 490.550 580.894 310.793 510.575 57
FPConvpermissive0.639 480.785 300.760 320.713 500.603 450.798 510.392 670.534 800.603 310.524 480.948 360.457 430.250 420.538 570.723 450.598 640.696 570.614 290.872 490.799 430.567 61
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 490.797 270.769 300.641 730.590 500.820 320.461 360.537 790.637 160.536 430.947 390.388 700.206 570.656 210.668 550.647 480.732 440.585 450.868 530.793 510.473 82
PointSPNet0.637 500.734 520.692 650.714 490.576 560.797 520.446 440.743 420.598 340.437 710.942 540.403 660.150 800.626 310.800 330.649 450.697 560.557 550.846 610.777 620.563 62
SConv0.636 510.830 200.697 610.752 420.572 590.780 610.445 460.716 490.529 550.530 450.951 260.446 510.170 720.507 650.666 560.636 520.682 620.541 640.886 370.799 430.594 53
Supervoxel-CNN0.635 520.656 680.711 530.719 470.613 430.757 700.444 490.765 350.534 540.566 340.928 720.478 340.272 320.636 260.531 670.664 390.645 730.508 710.864 550.792 540.611 42
joint point-basedpermissive0.634 530.614 750.778 260.667 640.633 400.825 250.420 570.804 260.467 730.561 350.951 260.494 290.291 230.566 470.458 730.579 700.764 300.559 540.838 620.814 360.598 51
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 540.866 110.731 490.771 340.576 560.809 430.410 590.684 570.497 630.491 580.949 330.466 390.105 850.581 420.646 590.620 550.680 640.542 630.817 670.795 470.618 41
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
PointMTL0.632 550.731 530.688 680.675 590.591 490.784 580.444 490.565 760.610 250.492 570.949 330.456 440.254 410.587 390.706 480.599 630.665 690.612 320.868 530.791 570.579 56
PointNet2-SFPN0.631 560.771 360.692 650.672 600.524 670.837 160.440 510.706 540.538 530.446 680.944 510.421 620.219 520.552 530.751 400.591 660.737 410.543 620.901 260.768 650.557 65
3DSM_DMMF0.631 560.626 720.745 420.801 260.607 440.751 710.506 160.729 470.565 460.491 580.866 880.434 520.197 640.595 370.630 600.709 240.705 540.560 520.875 440.740 730.491 77
APCF-Net0.631 560.742 490.687 700.672 600.557 630.792 560.408 600.665 620.545 510.508 530.952 250.428 570.186 670.634 280.702 490.620 550.706 530.555 560.873 470.798 450.581 55
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 590.604 770.741 460.766 380.590 500.747 720.501 190.734 450.503 620.527 460.919 780.454 450.323 120.550 560.420 770.678 340.688 600.544 600.896 290.795 470.627 39
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 600.800 260.625 800.719 470.545 650.806 450.445 460.597 700.448 770.519 510.938 600.481 320.328 100.489 690.499 720.657 430.759 330.592 410.881 400.797 460.634 36
SegGroup_sempermissive0.627 610.818 230.747 410.701 510.602 460.764 670.385 710.629 670.490 660.508 530.931 710.409 650.201 610.564 480.725 440.618 570.692 580.539 650.873 470.794 490.548 68
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 620.830 200.694 630.757 400.563 610.772 650.448 430.647 650.520 570.509 520.949 330.431 550.191 650.496 670.614 610.647 480.672 670.535 670.876 430.783 590.571 58
HPEIN0.618 630.729 540.668 710.647 700.597 480.766 660.414 580.680 580.520 570.525 470.946 420.432 530.215 540.493 680.599 620.638 510.617 780.570 480.897 280.806 400.605 48
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-GCNpermissive0.610 640.858 140.772 280.489 850.532 660.792 560.404 630.643 660.570 450.507 550.935 640.414 640.046 910.510 630.702 490.602 620.705 540.549 590.859 570.773 640.534 71
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 650.760 410.667 720.649 690.521 680.793 540.457 380.648 640.528 560.434 730.947 390.401 670.153 790.454 720.721 460.648 470.717 480.536 660.904 220.765 660.485 78
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 660.634 710.743 440.697 540.601 470.781 590.437 530.585 730.493 650.446 680.933 690.394 680.011 930.654 220.661 580.603 610.733 430.526 680.832 630.761 680.480 79
LAP-D0.594 670.720 570.692 650.637 740.456 770.773 640.391 690.730 460.587 360.445 700.940 580.381 710.288 240.434 750.453 750.591 660.649 710.581 460.777 720.749 720.610 44
DPC0.592 680.720 570.700 590.602 780.480 730.762 690.380 720.713 520.585 390.437 710.940 580.369 730.288 240.434 750.509 710.590 680.639 760.567 510.772 730.755 700.592 54
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 690.766 400.659 750.683 570.470 760.740 740.387 700.620 690.490 660.476 620.922 760.355 760.245 460.511 620.511 700.571 710.643 740.493 750.872 490.762 670.600 50
ROSMRF0.580 700.772 350.707 550.681 580.563 610.764 670.362 740.515 810.465 740.465 650.936 630.427 590.207 560.438 730.577 630.536 740.675 660.486 760.723 790.779 600.524 73
SD-DETR0.576 710.746 460.609 840.445 890.517 690.643 850.366 730.714 510.456 750.468 640.870 870.432 530.264 380.558 510.674 530.586 690.688 600.482 770.739 770.733 750.537 70
SQN_0.1%0.569 720.676 650.696 620.657 660.497 700.779 620.424 550.548 770.515 590.376 780.902 850.422 610.357 40.379 790.456 740.596 650.659 700.544 600.685 820.665 860.556 66
TextureNetpermissive0.566 730.672 670.664 730.671 620.494 710.719 750.445 460.678 600.411 830.396 760.935 640.356 750.225 500.412 770.535 660.565 720.636 770.464 790.794 710.680 830.568 60
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
DVVNet0.562 740.648 690.700 590.770 350.586 530.687 790.333 780.650 630.514 600.475 630.906 820.359 740.223 510.340 810.442 760.422 850.668 680.501 720.708 800.779 600.534 71
Pointnet++ & Featurepermissive0.557 750.735 510.661 740.686 560.491 720.744 730.392 670.539 780.451 760.375 790.946 420.376 720.205 580.403 780.356 810.553 730.643 740.497 730.824 660.756 690.515 74
GMLPs0.538 760.495 860.693 640.647 700.471 750.793 540.300 810.477 820.505 610.358 800.903 840.327 790.081 880.472 710.529 680.448 830.710 490.509 690.746 750.737 740.554 67
PanopticFusion-label0.529 770.491 870.688 680.604 770.386 820.632 860.225 910.705 550.434 800.293 860.815 890.348 770.241 470.499 660.669 540.507 760.649 710.442 850.796 700.602 890.561 63
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_weak0.516 780.676 650.591 870.609 750.442 780.774 630.335 770.597 700.422 820.357 810.932 700.341 780.094 870.298 830.528 690.473 810.676 650.495 740.602 880.721 780.349 89
Online SegFusion0.515 790.607 760.644 780.579 800.434 790.630 870.353 750.628 680.440 780.410 740.762 920.307 810.167 740.520 600.403 790.516 750.565 810.447 830.678 830.701 800.514 75
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, FTSDF0.501 800.558 820.608 850.424 910.478 740.690 780.246 870.586 720.468 720.450 670.911 800.394 680.160 770.438 730.212 880.432 840.541 860.475 780.742 760.727 760.477 80
PCNN0.498 810.559 810.644 780.560 820.420 810.711 770.229 890.414 830.436 790.352 820.941 560.324 800.155 780.238 880.387 800.493 770.529 870.509 690.813 690.751 710.504 76
3DMV0.484 820.484 880.538 890.643 720.424 800.606 900.310 790.574 740.433 810.378 770.796 900.301 820.214 550.537 580.208 890.472 820.507 900.413 880.693 810.602 890.539 69
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 830.577 800.611 830.356 930.321 900.715 760.299 830.376 870.328 900.319 840.944 510.285 840.164 750.216 910.229 860.484 790.545 850.456 810.755 740.709 790.475 81
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 840.679 640.604 860.578 810.380 830.682 800.291 840.106 930.483 690.258 910.920 770.258 880.025 920.231 900.325 820.480 800.560 830.463 800.725 780.666 850.231 93
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 850.474 890.623 810.463 870.366 850.651 830.310 790.389 860.349 880.330 830.937 610.271 860.126 830.285 840.224 870.350 900.577 800.445 840.625 860.723 770.394 85
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
SurfaceConvPF0.442 860.505 850.622 820.380 920.342 880.654 820.227 900.397 850.367 860.276 880.924 740.240 890.198 630.359 800.262 840.366 870.581 790.435 860.640 850.668 840.398 84
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 860.548 830.548 880.597 790.363 860.628 880.300 810.292 880.374 850.307 850.881 860.268 870.186 670.238 880.204 900.407 860.506 910.449 820.667 840.620 880.462 83
Tangent Convolutionspermissive0.438 880.437 910.646 770.474 860.369 840.645 840.353 750.258 900.282 920.279 870.918 790.298 830.147 820.283 850.294 830.487 780.562 820.427 870.619 870.633 870.352 88
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 890.525 840.647 760.522 830.324 890.488 930.077 940.712 530.353 870.401 750.636 940.281 850.176 700.340 810.565 640.175 940.551 840.398 890.370 940.602 890.361 87
SPLAT Netcopyleft0.393 900.472 900.511 900.606 760.311 910.656 810.245 880.405 840.328 900.197 920.927 730.227 910.000 950.001 950.249 850.271 930.510 880.383 910.593 890.699 810.267 91
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+FTSDF0.383 910.297 930.491 910.432 900.358 870.612 890.274 850.116 920.411 830.265 890.904 830.229 900.079 890.250 860.185 910.320 910.510 880.385 900.548 900.597 920.394 85
PointNet++permissive0.339 920.584 790.478 920.458 880.256 930.360 940.250 860.247 910.278 930.261 900.677 930.183 920.117 840.212 920.145 930.364 880.346 940.232 940.548 900.523 930.252 92
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 930.353 920.290 940.278 940.166 940.553 910.169 930.286 890.147 940.148 940.908 810.182 930.064 900.023 940.018 950.354 890.363 920.345 920.546 920.685 820.278 90
ScanNetpermissive0.306 940.203 940.366 930.501 840.311 910.524 920.211 920.002 950.342 890.189 930.786 910.145 940.102 860.245 870.152 920.318 920.348 930.300 930.460 930.437 940.182 94
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 950.000 950.041 950.172 950.030 950.062 950.001 950.035 940.004 950.051 950.143 950.019 950.003 940.041 930.050 940.003 950.054 950.018 950.005 950.264 950.082 95


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 10.786 220.716 210.696 40.885 30.500 20.714 170.810 10.672 30.715 30.679 60.809 11.000 10.831 10.833 80.787 31.000 10.602 4
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation.
SPFormer0.770 20.903 330.903 10.806 90.609 120.886 20.568 10.815 60.705 30.711 10.655 40.652 80.685 81.000 10.789 30.809 110.776 41.000 10.583 7
SoftGroup++0.769 31.000 10.803 170.937 10.684 50.865 50.213 160.870 20.664 40.571 60.758 10.702 40.807 21.000 10.653 150.902 10.792 21.000 10.626 1
SoftGrouppermissive0.761 41.000 10.808 140.845 60.716 10.862 70.243 130.824 30.655 60.620 40.734 20.699 50.791 40.981 220.716 60.844 40.769 51.000 10.594 6
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
PBNetpermissive0.734 51.000 10.810 110.824 70.704 20.830 100.492 30.696 180.601 110.562 80.593 100.587 110.650 101.000 10.698 90.844 50.762 61.000 10.556 14
GraphCut0.732 61.000 10.788 200.724 200.642 80.859 80.248 120.787 100.618 100.596 50.653 60.722 20.583 261.000 10.766 40.861 20.825 11.000 10.504 18
IPCA-Inst0.731 71.000 10.788 210.884 50.698 30.788 230.252 110.760 120.646 70.511 150.637 80.665 70.804 31.000 10.644 160.778 130.747 81.000 10.561 12
TopoSeg0.725 81.000 10.806 160.933 20.668 70.758 260.272 90.734 160.630 80.549 110.654 50.606 90.697 70.966 240.612 190.839 60.754 71.000 10.573 8
DKNet0.718 91.000 10.814 100.782 120.619 90.872 40.224 140.751 140.569 130.677 20.585 120.724 10.633 180.981 220.515 260.819 90.736 91.000 10.617 2
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.700 101.000 10.848 50.763 180.609 130.792 210.262 100.824 30.627 90.535 130.547 200.493 160.600 201.000 10.712 80.731 250.689 131.000 10.563 11
HAISpermissive0.699 111.000 10.849 40.820 80.675 60.808 160.279 70.757 130.465 180.517 140.596 90.559 120.600 201.000 10.654 140.767 150.676 140.994 290.560 13
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 121.000 10.697 370.888 40.556 190.803 170.387 50.626 250.417 220.556 100.585 130.702 30.600 201.000 10.824 20.720 270.692 111.000 10.509 17
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SphereSeg0.680 131.000 10.856 30.744 190.618 100.893 10.151 180.651 230.713 20.537 120.579 150.430 240.651 91.000 10.389 350.744 220.697 100.991 300.601 5
Box2Mask0.677 141.000 10.847 60.771 140.509 250.816 120.277 80.558 320.482 150.562 90.640 70.448 200.700 51.000 10.666 100.852 30.578 250.997 240.488 22
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 151.000 10.758 290.682 230.576 170.842 90.477 40.504 350.524 140.567 70.585 140.451 190.557 271.000 10.751 50.797 120.563 281.000 10.467 25
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 161.000 10.822 90.764 170.616 110.815 130.139 220.694 200.597 120.459 200.566 160.599 100.600 200.516 410.715 70.819 100.635 181.000 10.603 3
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 171.000 10.760 270.667 250.581 150.863 60.323 60.655 220.477 160.473 180.549 180.432 230.650 101.000 10.655 130.738 230.585 240.944 340.472 24
CSC-Pretrained0.648 181.000 10.810 120.768 150.523 240.813 140.143 210.819 50.389 230.422 270.511 230.443 210.650 101.000 10.624 180.732 240.634 191.000 10.375 31
PE0.645 191.000 10.773 240.798 110.538 210.786 240.088 290.799 90.350 270.435 260.547 190.545 130.646 170.933 250.562 220.761 180.556 330.997 240.501 20
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 201.000 10.758 280.582 330.539 200.826 110.046 330.765 110.372 250.436 250.588 110.539 150.650 101.000 10.577 200.750 200.653 170.997 240.495 21
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 211.000 10.841 70.893 30.531 220.802 180.115 260.588 300.448 190.438 230.537 220.430 250.550 280.857 270.534 240.764 170.657 150.987 310.568 9
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 221.000 10.895 20.800 100.480 280.676 300.144 200.737 150.354 260.447 210.400 320.365 300.700 51.000 10.569 210.836 70.599 211.000 10.473 23
PointGroup0.636 231.000 10.765 250.624 270.505 270.797 190.116 250.696 180.384 240.441 220.559 170.476 170.596 241.000 10.666 100.756 190.556 320.997 240.513 16
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+Group0.635 240.667 340.797 190.714 220.562 180.774 250.146 190.810 80.429 210.476 170.546 210.399 270.633 181.000 10.632 170.722 260.609 201.000 10.514 15
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
DENet0.629 251.000 10.797 180.608 280.589 140.627 340.219 150.882 10.310 290.402 310.383 340.396 280.650 101.000 10.663 120.543 420.691 121.000 10.568 10
3D-MPA0.611 261.000 10.833 80.765 160.526 230.756 270.136 240.588 300.470 170.438 240.432 300.358 310.650 100.857 270.429 310.765 160.557 311.000 10.430 27
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
PCJC0.578 271.000 10.810 130.583 320.449 310.813 150.042 340.603 280.341 280.490 160.465 260.410 260.650 100.835 330.264 400.694 310.561 290.889 380.504 19
SSEN0.575 281.000 10.761 260.473 350.477 290.795 200.066 300.529 330.658 50.460 190.461 270.380 290.331 400.859 260.401 340.692 320.653 161.000 10.348 33
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 290.528 430.708 360.626 260.580 160.745 280.063 310.627 240.240 330.400 320.497 240.464 180.515 291.000 10.475 280.745 210.571 261.000 10.429 28
MTML0.549 301.000 10.807 150.588 310.327 360.647 320.004 390.815 70.180 350.418 280.364 360.182 350.445 331.000 10.442 300.688 330.571 271.000 10.396 29
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
One_Thing_One_Clickpermissive0.529 310.667 340.718 320.777 130.399 320.683 290.000 420.669 210.138 380.391 330.374 350.539 140.360 390.641 380.556 230.774 140.593 220.997 240.251 38
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-CNN0.515 321.000 10.538 440.282 380.468 300.790 220.173 170.345 400.429 200.413 300.484 250.176 360.595 250.591 390.522 250.668 340.476 370.986 320.327 34
Occipital-SCS0.512 331.000 10.716 330.509 340.506 260.611 350.092 280.602 290.177 360.346 360.383 330.165 370.442 340.850 320.386 360.618 380.543 340.889 380.389 30
3D-BoNet0.488 341.000 10.672 390.590 300.301 380.484 450.098 270.620 260.306 300.341 370.259 400.125 390.434 360.796 340.402 330.499 440.513 360.909 370.439 26
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-inst0.478 350.667 340.712 350.595 290.259 410.550 410.000 420.613 270.175 370.250 420.434 280.437 220.411 380.857 270.485 270.591 410.267 470.944 340.359 32
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_WSIS0.470 360.667 340.685 380.677 240.372 340.562 390.000 420.482 360.244 320.316 390.298 370.052 450.442 350.857 270.267 390.702 280.559 301.000 10.287 36
SALoss-ResNet0.459 371.000 10.737 310.159 480.259 400.587 370.138 230.475 370.217 340.416 290.408 310.128 380.315 410.714 350.411 320.536 430.590 230.873 410.304 35
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)
MASCpermissive0.447 380.528 430.555 420.381 360.382 330.633 330.002 400.509 340.260 310.361 350.432 290.327 320.451 320.571 400.367 370.639 360.386 380.980 330.276 37
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 390.667 340.773 230.185 450.317 370.656 310.000 420.407 390.134 390.381 340.267 390.217 340.476 310.714 350.452 290.629 370.514 351.000 10.222 41
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 401.000 10.432 460.245 400.190 420.577 380.013 370.263 420.033 450.320 380.240 410.075 410.422 370.857 270.117 440.699 290.271 460.883 400.235 40
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 410.667 340.542 430.264 390.157 450.550 400.000 420.205 450.009 460.270 410.218 420.075 410.500 300.688 370.007 500.698 300.301 430.459 470.200 42
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 420.667 340.715 340.233 410.189 430.479 460.008 380.218 430.067 440.201 440.173 430.107 400.123 460.438 420.150 420.615 390.355 390.916 360.093 49
R-PointNet0.306 430.500 450.405 470.311 370.348 350.589 360.054 320.068 480.126 400.283 400.290 380.028 460.219 440.214 450.331 380.396 480.275 440.821 430.245 39
Region-18class0.284 440.250 490.751 300.228 430.270 390.521 420.000 420.468 380.008 480.205 430.127 440.000 500.068 480.070 480.262 410.652 350.323 410.740 440.173 43
SemRegionNet-20cls0.250 450.333 460.613 400.229 420.163 440.493 430.000 420.304 410.107 410.147 460.100 450.052 440.231 420.119 460.039 460.445 460.325 400.654 450.141 45
3D-BEVIS0.248 460.667 340.566 410.076 490.035 500.394 480.027 360.035 490.098 420.099 480.030 490.025 470.098 470.375 440.126 430.604 400.181 480.854 420.171 44
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
tmp0.248 460.667 340.437 450.188 440.153 460.491 440.000 420.208 440.094 430.153 450.099 460.057 430.217 450.119 460.039 460.466 450.302 420.640 460.140 46
ASIS0.199 480.333 460.253 490.167 470.140 470.438 470.000 420.177 460.008 470.121 470.069 470.004 490.231 430.429 430.036 480.445 470.273 450.333 490.119 48
Sgpn_scannet0.143 490.208 500.390 480.169 460.065 480.275 490.029 350.069 470.000 490.087 490.043 480.014 480.027 500.000 490.112 450.351 490.168 490.438 480.138 47
MaskRCNN 2d->3d Proj0.058 500.333 460.002 500.000 500.053 490.002 500.002 410.021 500.000 490.045 500.024 500.238 330.065 490.000 490.014 490.107 500.020 500.110 500.006 50


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 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