Presenting the ScanNet200 Benchmark

We present the ScanNet200 benchmark, which studies an order of magnitude more class categories than previous version of ScanNet. The scene geometry is shared within the two tasks, but the parsing of surface annotation allows for a larger vocabulary and more realistic setting for in the wild 3D understanding methods.

The ScanNet200 benchmark includes both finer-grained categories as well as a large number of previously unaddressed classes. This induces a much more challenging setting regarding the diversity of naturally observed semantic classes seen in the raw ScanNet RGB-D observations, where the data also reflects naturally encountered class imbalances. The difference in category frequencies between ScanNet and ScanNet200 can be seen in the Figure above.

ScanNet200 Benchmark

This table lists the benchmark results for the ScanNet200 3D semantic label scenario.




Method Infoavg iouhead ioucommon ioutail ioualarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefloorfolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwallwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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.
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
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
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 Infoavg ap 25%head ap 25%common ap 25%tail ap 25%alarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort by
TD3D Scannet2000.379 20.603 20.306 20.190 20.635 20.073 20.500 10.000 10.000 10.000 10.495 30.735 20.275 51.000 10.979 20.590 20.000 40.021 20.000 30.146 30.000 20.356 20.173 50.795 10.226 20.000 10.173 20.000 10.000 20.226 20.390 20.000 20.000 10.250 10.000 10.706 20.061 30.885 10.093 20.186 20.259 40.200 10.667 10.000 20.000 10.667 20.825 10.250 40.834 41.000 10.958 10.553 10.111 30.748 10.220 20.051 20.866 20.792 10.390 50.045 50.800 20.302 50.517 10.533 30.113 20.427 10.843 20.000 20.458 10.600 10.000 10.101 20.000 10.259 10.717 20.500 20.615 20.520 20.526 20.457 10.270 40.000 10.000 10.400 20.088 20.294 20.181 20.000 11.000 10.400 10.710 50.103 30.477 50.905 20.061 20.000 10.906 20.102 20.232 10.125 20.000 20.003 20.792 31.000 10.000 20.102 30.125 40.559 50.523 30.075 20.715 10.000 20.424 50.000 10.396 20.250 10.638 10.000 10.000 20.000 10.622 50.833 20.221 10.970 10.250 20.038 10.260 20.415 10.125 21.000 11.000 10.857 20.000 20.908 10.012 10.869 30.836 10.635 10.111 10.625 11.000 10.020 20.510 10.003 30.009 21.000 10.778 10.000 10.000 10.370 30.755 10.288 20.333 30.274 21.000 10.557 10.731 20.456 20.433 30.769 50.000 10.000 20.621 41.000 10.458 40.000 10.196 20.817 10.000 10.472 10.222 30.205 50.689 20.274 3
LGround Inst.permissive0.314 30.529 30.225 30.155 30.578 50.010 30.500 10.000 10.000 10.000 10.515 20.556 30.696 11.000 10.927 30.400 30.083 30.000 31.000 10.252 10.000 20.167 30.350 20.731 20.067 30.000 10.123 40.000 10.000 20.036 30.372 30.000 20.000 10.250 10.000 10.569 40.031 50.810 30.000 30.000 40.630 10.183 20.278 30.000 20.000 10.582 40.589 50.500 20.863 31.000 10.940 20.000 40.144 10.716 30.000 30.000 30.484 30.000 30.500 30.400 30.798 30.500 20.278 40.750 10.093 30.166 40.783 30.000 20.200 20.400 20.000 10.000 30.000 10.219 20.539 30.500 20.578 30.413 30.181 50.457 20.375 20.000 10.000 10.050 50.000 40.077 40.000 30.000 10.500 50.000 50.743 30.250 20.488 40.846 30.000 30.000 10.800 30.069 30.000 30.000 30.000 20.000 31.000 10.607 40.000 20.200 10.500 10.694 20.528 20.063 30.659 20.000 20.594 20.000 10.000 30.000 20.571 20.000 10.000 20.000 10.716 40.647 50.221 20.857 40.000 30.000 30.217 30.346 30.071 50.530 51.000 10.429 30.000 20.286 30.000 30.826 50.706 30.208 40.000 30.250 40.744 50.000 30.500 20.042 10.000 30.000 20.746 30.000 10.000 10.517 10.625 30.085 50.333 30.000 41.000 10.378 40.533 50.376 40.042 50.814 30.000 10.000 20.765 31.000 10.600 30.000 10.000 40.667 30.000 10.472 10.333 20.337 30.605 30.305 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.permissive0.280 40.488 40.192 50.124 40.593 40.010 40.500 10.000 10.000 10.000 10.447 40.535 40.445 31.000 10.861 40.400 30.225 20.000 30.000 30.142 40.000 20.074 40.342 30.467 50.067 30.000 10.119 50.000 10.000 20.000 40.337 50.000 20.000 10.000 40.000 10.506 50.070 20.804 40.000 30.000 40.333 30.172 30.150 50.000 20.000 10.479 50.745 30.000 50.830 51.000 10.904 30.167 20.090 40.732 20.000 30.000 30.443 40.000 30.500 30.542 10.772 50.396 40.077 50.385 40.044 40.118 50.777 40.000 20.000 40.200 30.000 10.000 30.000 10.148 40.502 40.500 20.419 40.159 50.281 40.404 50.317 30.000 10.000 10.200 30.000 40.077 30.000 30.000 10.750 30.200 30.715 40.021 40.551 20.828 50.000 30.000 10.743 40.059 50.000 30.000 30.000 20.000 30.125 50.648 30.000 20.191 20.500 10.669 40.502 40.000 50.568 40.000 20.516 40.000 10.000 30.000 20.305 50.000 10.000 20.000 10.825 10.833 20.021 50.918 20.000 30.000 30.191 40.346 40.100 40.981 31.000 10.286 40.000 20.000 50.000 30.868 40.648 50.292 30.000 30.375 31.000 10.000 30.500 20.000 40.333 10.000 20.538 50.000 10.000 10.213 50.518 40.098 40.528 10.250 30.997 30.284 50.677 30.398 30.167 40.790 40.000 10.000 20.618 50.903 50.200 50.000 10.333 10.333 40.000 10.442 30.083 40.213 40.587 40.131 5
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
Mask3D Scannet2000.445 10.653 10.392 10.254 10.648 10.097 10.125 50.000 10.000 10.000 10.657 10.971 10.451 21.000 11.000 10.640 10.500 10.045 11.000 10.241 20.409 10.363 10.440 10.686 30.300 10.000 10.201 10.000 10.009 10.290 10.556 11.000 10.000 10.063 30.000 10.830 10.573 10.844 20.333 10.204 10.058 50.158 50.552 20.056 10.000 11.000 10.725 40.750 10.927 11.000 10.888 40.042 30.120 20.615 40.226 10.250 10.890 10.792 10.677 20.510 20.818 10.699 10.512 20.167 50.125 10.315 20.943 10.309 10.017 30.200 30.000 10.188 10.000 10.183 30.815 11.000 10.827 10.741 10.442 30.414 40.600 10.000 10.000 10.458 10.049 30.321 10.381 10.000 10.908 20.400 10.841 10.260 10.710 10.966 10.265 10.000 10.924 10.152 10.025 20.500 10.027 10.028 11.000 10.556 50.016 10.080 50.500 10.694 30.608 10.084 10.604 30.194 10.538 30.000 10.500 10.000 20.354 40.000 11.000 10.000 10.761 20.930 10.053 40.890 31.000 10.008 20.262 10.358 21.000 11.000 10.792 40.966 11.000 10.765 20.004 20.930 10.780 20.330 20.027 20.625 10.974 40.050 10.412 50.021 20.000 30.000 20.778 10.000 10.000 10.493 20.746 20.454 10.335 20.396 10.930 50.551 21.000 10.552 10.606 10.853 10.000 10.004 10.806 11.000 10.727 20.000 10.042 30.745 20.000 10.399 40.391 10.630 10.721 10.619 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
CSC-Pretrain Inst.permissive0.275 50.466 50.218 40.110 50.625 30.007 50.500 10.000 10.000 10.000 10.000 50.222 50.377 41.000 10.661 50.400 30.000 40.000 30.000 30.119 50.000 20.000 50.277 40.685 40.067 30.000 10.132 30.000 10.000 20.000 40.367 40.000 20.000 10.000 40.000 10.591 30.055 40.783 50.000 30.014 30.500 20.161 40.278 30.000 20.000 10.667 20.768 20.500 20.866 21.000 10.829 50.000 40.019 50.555 50.000 30.000 30.305 50.000 30.750 10.200 40.783 40.429 30.395 30.677 20.020 50.286 30.584 50.000 20.000 40.115 50.000 10.000 30.000 10.145 50.423 50.500 20.364 50.369 40.571 10.448 30.206 50.000 10.000 10.200 30.106 10.065 50.000 30.000 10.750 30.200 30.774 20.000 50.501 30.841 40.000 30.000 10.692 50.063 40.000 30.000 30.000 20.000 30.500 40.649 20.000 20.084 40.125 40.719 10.413 50.004 40.450 50.000 20.638 10.000 10.000 30.000 20.505 30.000 10.000 20.000 10.727 30.833 20.221 20.779 50.000 30.000 30.168 50.311 50.125 20.571 40.500 50.143 50.000 20.250 40.000 30.869 20.667 40.162 50.000 30.250 41.000 10.000 30.500 20.000 40.000 30.000 20.689 40.000 10.000 10.312 40.383 50.114 30.333 30.000 40.997 30.420 30.613 40.212 50.500 20.819 20.000 10.000 20.768 21.000 10.918 10.000 10.000 40.278 50.000 10.333 50.000 50.353 20.546 50.258 4
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
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 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
TD3D0.875 11.000 10.976 90.877 80.783 110.970 10.889 10.828 110.945 30.803 40.713 70.720 80.709 61.000 10.936 40.934 30.873 51.000 10.791 5
SoftGroup++0.874 21.000 10.972 100.947 10.839 40.898 100.556 210.913 20.881 90.756 60.828 20.748 40.821 11.000 10.937 30.937 10.887 11.000 10.821 2
Mask3D0.870 31.000 10.985 60.782 280.818 60.938 40.760 40.749 220.923 40.877 20.760 40.785 10.820 21.000 10.912 70.864 210.878 30.983 360.825 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SoftGrouppermissive0.865 41.000 10.969 110.860 110.860 10.913 60.558 190.899 30.911 50.760 50.828 10.736 50.802 40.981 270.919 60.875 120.877 41.000 10.820 3
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
SPFormerpermissive0.851 51.000 10.994 20.806 220.774 140.942 30.637 110.849 90.859 120.889 10.720 60.730 60.665 111.000 10.911 110.868 190.873 61.000 10.796 4
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
IPCA-Inst0.851 51.000 10.968 120.884 70.842 30.862 220.693 80.812 170.888 80.677 180.783 30.698 90.807 31.000 10.911 110.865 200.865 71.000 10.757 8
SphereSeg0.835 71.000 10.963 150.891 50.794 80.954 20.822 30.710 250.961 20.721 100.693 130.530 280.653 121.000 10.867 200.857 240.859 80.991 330.771 6
GraphCut0.832 81.000 10.922 290.724 380.798 70.902 90.701 70.856 70.859 110.715 110.706 80.748 30.640 221.000 10.934 50.862 220.880 21.000 10.729 11
TopoSeg0.832 81.000 10.981 70.933 20.819 50.826 290.524 270.841 100.811 170.681 170.759 50.687 100.727 50.981 270.911 110.883 90.853 91.000 10.756 9
PBNetpermissive0.825 101.000 10.963 140.837 150.843 20.865 170.822 20.647 320.878 100.733 80.639 210.683 110.650 131.000 10.853 210.870 160.820 101.000 10.744 10
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. Arxiv
DKNet0.815 111.000 10.930 220.844 130.765 170.915 50.534 250.805 190.805 190.807 30.654 150.763 20.650 131.000 10.794 330.881 100.766 141.000 10.758 7
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN0.806 121.000 10.992 40.789 240.723 290.891 110.650 100.810 180.832 140.665 200.699 110.658 120.700 71.000 10.881 160.832 310.774 120.997 260.613 31
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Box2Mask0.803 131.000 10.962 160.874 90.707 320.887 140.686 90.598 360.961 10.715 120.694 120.469 330.700 71.000 10.912 70.902 40.753 200.997 260.637 25
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
HAISpermissive0.803 131.000 10.994 20.820 190.759 180.855 230.554 220.882 40.827 160.615 270.676 140.638 150.646 201.000 10.912 70.797 410.767 130.994 310.726 12
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Mask-Group0.792 151.000 10.968 130.812 200.766 160.864 180.460 300.815 160.888 70.598 290.651 180.639 140.600 260.918 310.941 10.896 50.721 261.000 10.723 13
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.791 161.000 10.996 10.829 180.767 150.889 130.600 140.819 150.770 250.594 300.620 250.541 240.700 71.000 10.941 10.889 70.763 161.000 10.526 40
SSTNetpermissive0.789 171.000 10.840 430.888 60.717 300.835 250.717 60.684 300.627 390.724 90.652 170.727 70.600 261.000 10.912 70.822 340.757 191.000 10.691 20
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN0.788 181.000 10.978 80.867 100.781 120.833 260.527 260.824 120.806 180.549 380.596 270.551 200.700 71.000 10.853 210.935 20.733 231.000 10.651 22
DENet0.786 191.000 10.929 230.736 360.750 240.720 440.755 50.934 10.794 200.590 310.561 330.537 250.650 131.000 10.882 140.804 390.789 111.000 10.719 14
SSEC0.781 201.000 10.945 180.763 330.780 130.819 310.601 130.824 120.790 210.638 230.622 240.536 260.600 261.000 10.882 140.790 420.765 151.000 10.698 18
PointGroup0.778 211.000 10.900 330.798 230.715 310.863 190.493 280.706 260.895 60.569 360.701 90.576 180.639 231.000 10.880 170.851 260.719 270.997 260.709 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]
PE0.776 221.000 10.900 340.860 110.728 280.869 150.400 360.857 60.774 220.568 370.701 100.602 170.646 200.933 300.843 240.890 60.691 340.997 260.709 15
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
AOIA0.767 231.000 10.937 190.810 210.740 260.906 70.550 230.800 200.706 310.577 350.624 230.544 230.596 320.857 340.879 190.880 110.750 210.992 320.658 21
DD-UNet+Group0.764 241.000 10.897 360.837 140.753 210.830 280.459 320.824 120.699 330.629 250.653 160.438 360.650 131.000 10.880 170.858 230.690 351.000 10.650 23
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
INS-Conv-instance0.762 251.000 10.923 260.765 310.785 100.905 80.600 140.655 310.646 380.683 160.647 190.530 270.650 131.000 10.824 260.830 320.693 330.944 400.644 24
Dyco3Dcopyleft0.761 261.000 10.935 200.893 40.752 230.863 200.600 140.588 370.742 280.641 220.633 220.546 220.550 340.857 340.789 350.853 250.762 170.987 340.699 17
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance0.742 271.000 10.923 260.785 250.745 250.867 160.557 200.578 400.729 290.670 190.644 200.488 310.577 331.000 10.794 330.830 320.620 421.000 10.550 36
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg0.739 281.000 10.899 350.759 340.753 220.823 300.282 400.691 290.658 360.582 340.594 280.547 210.628 241.000 10.795 320.868 180.728 251.000 10.692 19
3D-MPA0.737 291.000 10.933 210.785 250.794 90.831 270.279 420.588 370.695 340.616 260.559 340.556 190.650 131.000 10.809 300.875 130.696 311.000 10.608 33
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.731 301.000 10.992 40.779 300.609 410.746 390.308 390.867 50.601 420.607 280.539 370.519 290.550 341.000 10.824 260.869 170.729 241.000 10.616 29
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS0.725 311.000 10.885 390.653 440.657 380.801 330.576 180.695 280.828 150.698 140.534 380.457 350.500 410.857 340.831 250.841 290.627 411.000 10.619 28
SSEN0.724 321.000 10.926 240.781 290.661 360.845 240.596 170.529 420.764 270.653 210.489 430.461 340.500 410.859 330.765 360.872 150.761 181.000 10.577 34
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF0.718 331.000 10.945 170.901 30.754 200.817 320.460 300.700 270.772 230.688 150.568 320.000 530.500 410.981 270.606 440.872 140.740 221.000 10.614 30
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
Sparse R-CNN0.714 341.000 10.926 250.694 390.699 340.890 120.636 120.516 430.693 350.743 70.588 290.369 390.601 250.594 460.800 310.886 80.676 360.986 350.546 37
SALoss-ResNet0.695 351.000 10.855 410.579 480.589 430.735 420.484 290.588 370.856 130.634 240.571 310.298 400.500 411.000 10.824 260.818 350.702 300.935 440.545 38
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
PanopticFusion-inst0.693 361.000 10.852 420.655 430.616 400.788 340.334 380.763 210.771 240.457 480.555 350.652 130.518 380.857 340.765 360.732 480.631 390.944 400.577 35
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS0.688 371.000 10.913 300.730 370.737 270.743 410.442 330.855 80.655 370.546 390.546 360.263 420.508 400.889 320.568 450.771 450.705 290.889 470.625 27
3D-BoNet0.687 381.000 10.887 380.836 160.587 440.643 510.550 230.620 330.724 300.522 430.501 410.243 430.512 391.000 10.751 380.807 380.661 380.909 460.612 32
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PCJC0.684 391.000 10.895 370.757 350.659 370.862 210.189 490.739 230.606 410.712 130.581 300.515 300.650 130.857 340.357 500.785 430.631 400.889 470.635 26
SPG_WSIS0.678 401.000 10.880 400.836 160.701 330.727 430.273 440.607 350.706 320.541 410.515 400.174 450.600 260.857 340.716 390.846 280.711 281.000 10.506 41
One_Thing_One_Clickpermissive0.675 411.000 10.823 440.782 270.621 390.766 360.211 460.736 240.560 450.586 320.522 390.636 160.453 450.641 450.853 210.850 270.694 320.997 260.411 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_inspermissive0.637 421.000 10.923 280.593 470.561 450.746 400.143 510.504 440.766 260.485 460.442 440.372 380.530 370.714 420.815 290.775 440.673 371.000 10.431 44
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASCpermissive0.615 430.711 490.802 450.540 490.757 190.777 350.029 520.577 410.588 440.521 440.600 260.436 370.534 360.697 430.616 430.838 300.526 440.980 370.534 39
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone0.605 441.000 10.909 310.764 320.603 420.704 450.415 350.301 490.548 460.461 470.394 450.267 410.386 470.857 340.649 420.817 360.504 450.959 380.356 48
3D-SISpermissive0.558 451.000 10.773 460.614 460.503 470.691 470.200 470.412 450.498 490.546 400.311 500.103 490.600 260.857 340.382 470.799 400.445 510.938 430.371 46
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.544 460.500 520.655 520.661 420.663 350.765 370.432 340.214 510.612 400.584 330.499 420.204 440.286 510.429 490.655 410.650 530.539 430.950 390.499 42
Hier3Dcopyleft0.540 471.000 10.727 470.626 450.467 500.693 460.200 470.412 450.480 500.528 420.318 490.077 520.600 260.688 440.382 470.768 460.472 470.941 420.350 49
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class0.497 480.250 540.902 320.689 400.540 460.747 380.276 430.610 340.268 530.489 450.348 460.000 530.243 530.220 520.663 400.814 370.459 490.928 450.496 43
tmp0.474 491.000 10.727 470.433 520.481 490.673 490.022 540.380 470.517 480.436 500.338 480.128 470.343 490.429 490.291 520.728 490.473 460.833 500.300 51
SemRegionNet-20cls0.470 501.000 10.727 470.447 510.481 480.678 480.024 530.380 470.518 470.440 490.339 470.128 470.350 480.429 490.212 530.711 500.465 480.833 500.290 52
ASIS0.422 510.333 530.707 500.676 410.401 510.650 500.350 370.177 520.594 430.376 510.202 510.077 510.404 460.571 470.197 540.674 520.447 500.500 530.260 53
3D-BEVIS0.401 520.667 500.687 510.419 530.137 540.587 520.188 500.235 500.359 520.211 530.093 540.080 500.311 500.571 470.382 470.754 470.300 530.874 490.357 47
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.390 530.556 510.636 530.493 500.353 520.539 530.271 450.160 530.450 510.359 520.178 520.146 460.250 520.143 530.347 510.698 510.436 520.667 520.331 50
MaskRCNN 2d->3d Proj0.261 540.903 480.081 540.008 540.233 530.175 540.280 410.106 540.150 540.203 540.175 530.480 320.218 540.143 530.542 460.404 540.153 540.393 540.049 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