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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CeCo0.340 10.551 10.247 10.181 10.475 20.057 40.142 30.000 10.000 10.000 10.387 20.463 10.499 20.924 10.774 10.213 10.257 10.000 30.546 40.100 20.006 20.615 10.177 40.534 10.246 10.000 20.400 10.000 10.338 10.006 30.484 10.609 10.000 10.083 10.000 20.873 10.089 20.661 20.000 30.048 40.560 10.408 10.892 10.000 10.000 10.586 10.616 20.000 40.692 20.900 10.721 10.162 10.228 10.860 10.000 10.000 20.575 10.083 20.550 10.347 10.624 10.410 10.360 10.740 10.109 20.321 20.660 10.000 20.121 20.939 10.143 20.000 10.400 10.003 20.190 10.564 10.652 10.615 10.421 10.304 30.579 10.547 10.000 10.000 10.296 10.000 40.030 40.096 10.000 20.916 10.037 10.551 10.171 20.376 10.865 10.286 10.000 10.633 10.102 40.027 40.011 20.000 10.000 10.474 20.742 10.133 20.311 10.824 10.242 10.503 10.068 30.828 10.000 20.429 10.000 10.063 10.000 10.781 10.000 10.000 20.000 10.665 10.633 10.450 10.818 10.000 10.000 10.429 10.532 10.226 10.825 10.510 30.377 10.709 10.079 20.000 10.753 10.683 10.102 40.063 20.401 40.620 30.000 10.619 10.000 40.000 30.000 10.595 20.000 20.000 10.345 20.564 10.411 10.603 10.384 10.945 10.266 10.643 10.367 10.304 10.663 10.000 10.010 10.726 20.767 10.898 10.000 10.784 10.435 10.861 10.000 10.447 10.000 40.257 10.656 10.377 3
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
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
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


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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mask3D Scannet2000.445 10.653 10.392 10.254 10.648 10.097 10.125 40.000 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 20.300 10.000 10.201 10.000 10.009 10.290 10.556 11.000 10.000 10.063 20.000 10.830 10.573 10.844 10.333 10.204 10.058 40.158 40.552 10.056 10.000 11.000 10.725 30.750 10.927 11.000 10.888 30.042 20.120 20.615 30.226 10.250 10.890 10.792 10.677 20.510 20.818 10.699 10.512 10.167 40.125 10.315 10.943 10.309 10.017 20.200 20.000 10.188 10.000 10.183 20.815 11.000 10.827 10.741 10.442 20.414 30.600 10.000 10.000 10.458 10.049 20.321 10.381 10.000 10.908 10.400 10.841 10.260 10.710 10.966 10.265 10.000 10.924 10.152 10.025 10.500 10.027 10.028 11.000 10.556 40.016 10.080 40.500 10.694 30.608 10.084 10.604 20.194 10.538 30.000 10.500 10.000 10.354 30.000 11.000 10.000 10.761 20.930 10.053 30.890 21.000 10.008 10.262 10.358 11.000 11.000 10.792 30.966 11.000 10.765 10.004 10.930 10.780 10.330 10.027 10.625 10.974 30.050 10.412 40.021 20.000 20.000 10.778 10.000 10.000 10.493 20.746 10.454 10.335 20.396 10.930 40.551 11.000 10.552 10.606 10.853 10.000 10.004 10.806 11.000 10.727 20.000 10.042 20.745 10.000 10.399 30.391 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.
Minkowski 34D Inst.permissive0.280 30.488 30.192 40.124 30.593 30.010 30.500 10.000 10.000 10.000 10.447 30.535 30.445 31.000 10.861 30.400 20.225 20.000 20.000 30.142 30.000 20.074 30.342 30.467 40.067 20.000 10.119 40.000 10.000 20.000 30.337 40.000 20.000 10.000 30.000 10.506 40.070 20.804 30.000 20.000 30.333 30.172 20.150 40.000 20.000 10.479 40.745 20.000 40.830 41.000 10.904 20.167 10.090 30.732 10.000 20.000 20.443 30.000 20.500 30.542 10.772 40.396 40.077 40.385 30.044 30.118 40.777 30.000 20.000 30.200 20.000 10.000 20.000 10.148 30.502 30.500 20.419 30.159 40.281 30.404 40.317 30.000 10.000 10.200 20.000 30.077 20.000 20.000 10.750 20.200 20.715 40.021 30.551 20.828 40.000 20.000 10.743 30.059 40.000 20.000 20.000 20.000 20.125 40.648 20.000 20.191 20.500 10.669 40.502 30.000 40.568 30.000 20.516 40.000 10.000 20.000 10.305 40.000 10.000 20.000 10.825 10.833 20.021 40.918 10.000 20.000 20.191 30.346 30.100 30.981 21.000 10.286 30.000 20.000 40.000 20.868 30.648 40.292 20.000 20.375 21.000 10.000 20.500 10.000 30.333 10.000 10.538 40.000 10.000 10.213 40.518 30.098 30.528 10.250 20.997 20.284 40.677 20.398 20.167 30.790 40.000 10.000 20.618 40.903 40.200 40.000 10.333 10.333 30.000 10.442 20.083 30.213 40.587 30.131 4
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.275 40.466 40.218 30.110 40.625 20.007 40.500 10.000 10.000 10.000 10.000 40.222 40.377 41.000 10.661 40.400 20.000 40.000 20.000 30.119 40.000 20.000 40.277 40.685 30.067 20.000 10.132 20.000 10.000 20.000 30.367 30.000 20.000 10.000 30.000 10.591 20.055 30.783 40.000 20.014 20.500 20.161 30.278 20.000 20.000 10.667 20.768 10.500 20.866 21.000 10.829 40.000 30.019 40.555 40.000 20.000 20.305 40.000 20.750 10.200 40.783 30.429 30.395 20.677 20.020 40.286 20.584 40.000 20.000 30.115 40.000 10.000 20.000 10.145 40.423 40.500 20.364 40.369 30.571 10.448 20.206 40.000 10.000 10.200 20.106 10.065 40.000 20.000 10.750 20.200 20.774 20.000 40.501 30.841 30.000 20.000 10.692 40.063 30.000 20.000 20.000 20.000 20.500 30.649 10.000 20.084 30.125 40.719 10.413 40.004 30.450 40.000 20.638 10.000 10.000 20.000 10.505 20.000 10.000 20.000 10.727 30.833 20.221 10.779 40.000 20.000 20.168 40.311 40.125 20.571 30.500 40.143 40.000 20.250 30.000 20.869 20.667 30.162 40.000 20.250 31.000 10.000 20.500 10.000 30.000 20.000 10.689 30.000 10.000 10.312 30.383 40.114 20.333 30.000 30.997 20.420 20.613 30.212 40.500 20.819 20.000 10.000 20.768 21.000 10.918 10.000 10.000 30.278 40.000 10.333 40.000 40.353 20.546 40.258 3
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.314 20.529 20.225 20.155 20.578 40.010 20.500 10.000 10.000 10.000 10.515 20.556 20.696 11.000 10.927 20.400 20.083 30.000 21.000 10.252 10.000 20.167 20.350 20.731 10.067 20.000 10.123 30.000 10.000 20.036 20.372 20.000 20.000 10.250 10.000 10.569 30.031 40.810 20.000 20.000 30.630 10.183 10.278 20.000 20.000 10.582 30.589 40.500 20.863 31.000 10.940 10.000 30.144 10.716 20.000 20.000 20.484 20.000 20.500 30.400 30.798 20.500 20.278 30.750 10.093 20.166 30.783 20.000 20.200 10.400 10.000 10.000 20.000 10.219 10.539 20.500 20.578 20.413 20.181 40.457 10.375 20.000 10.000 10.050 40.000 30.077 30.000 20.000 10.500 40.000 40.743 30.250 20.488 40.846 20.000 20.000 10.800 20.069 20.000 20.000 20.000 20.000 21.000 10.607 30.000 20.200 10.500 10.694 20.528 20.063 20.659 10.000 20.594 20.000 10.000 20.000 10.571 10.000 10.000 20.000 10.716 40.647 40.221 10.857 30.000 20.000 20.217 20.346 20.071 40.530 41.000 10.429 20.000 20.286 20.000 20.826 40.706 20.208 30.000 20.250 30.744 40.000 20.500 10.042 10.000 20.000 10.746 20.000 10.000 10.517 10.625 20.085 40.333 30.000 31.000 10.378 30.533 40.376 30.042 40.814 30.000 10.000 20.765 31.000 10.600 30.000 10.000 30.667 20.000 10.472 10.333 20.337 30.605 20.305 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.


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 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
SoftGroup++0.874 11.000 10.972 90.947 10.839 30.898 80.556 190.913 20.881 80.756 50.828 20.748 40.821 11.000 10.937 30.937 10.887 11.000 10.821 2
Mask3D0.870 21.000 10.985 60.782 250.818 50.938 30.760 20.749 200.923 30.877 20.760 40.785 10.820 21.000 10.912 60.864 170.878 30.983 320.825 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation.
SoftGrouppermissive0.865 31.000 10.969 100.860 90.860 10.913 50.558 170.899 30.911 40.760 40.828 10.736 50.802 40.981 260.919 50.875 100.877 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]
IPCA-Inst0.851 41.000 10.968 120.884 60.842 20.862 190.693 60.812 160.888 70.677 150.783 30.698 80.807 31.000 10.911 100.865 160.865 61.000 10.757 7
SPFormerpermissive0.851 41.000 10.994 20.806 190.774 130.942 20.637 100.849 90.859 100.889 10.720 60.730 60.665 101.000 10.911 100.868 150.873 51.000 10.796 4
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023
SphereSeg0.835 61.000 10.963 140.891 40.794 80.954 10.822 10.710 230.961 20.721 80.693 120.530 260.653 111.000 10.867 180.857 200.859 70.991 290.771 5
TopoSeg0.832 71.000 10.981 70.933 20.819 40.826 270.524 240.841 100.811 150.681 140.759 50.687 90.727 50.981 260.911 100.883 80.853 81.000 10.756 8
GraphCut0.832 71.000 10.922 260.724 350.798 70.902 70.701 50.856 70.859 90.715 90.706 70.748 30.640 211.000 10.934 40.862 180.880 21.000 10.729 9
DKNet0.815 91.000 10.930 190.844 110.765 160.915 40.534 220.805 180.805 170.807 30.654 140.763 20.650 121.000 10.794 300.881 90.766 131.000 10.758 6
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN0.806 101.000 10.992 40.789 210.723 260.891 90.650 90.810 170.832 130.665 170.699 100.658 100.700 61.000 10.881 150.832 270.774 110.997 230.613 27
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
PBNetpermissive0.805 111.000 10.969 110.824 160.805 60.850 210.686 70.706 240.835 120.683 130.626 210.604 150.650 121.000 10.859 190.844 250.837 91.000 10.721 12
Box2Mask0.803 121.000 10.962 150.874 70.707 290.887 120.686 80.598 320.961 10.715 100.694 110.469 310.700 61.000 10.912 60.902 30.753 190.997 230.637 23
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
HAISpermissive0.803 121.000 10.994 20.820 170.759 170.855 200.554 200.882 40.827 140.615 240.676 130.638 130.646 191.000 10.912 60.797 370.767 120.994 280.726 10
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Mask-Group0.792 141.000 10.968 130.812 180.766 150.864 150.460 270.815 150.888 60.598 260.651 170.639 120.600 250.918 290.941 10.896 40.721 231.000 10.723 11
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.791 151.000 10.996 10.829 150.767 140.889 110.600 130.819 140.770 220.594 270.620 230.541 230.700 61.000 10.941 10.889 60.763 151.000 10.526 36
SSTNetpermissive0.789 161.000 10.840 390.888 50.717 270.835 230.717 40.684 270.627 350.724 70.652 160.727 70.600 251.000 10.912 60.822 300.757 181.000 10.691 19
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN0.788 171.000 10.978 80.867 80.781 110.833 240.527 230.824 110.806 160.549 340.596 250.551 190.700 61.000 10.853 200.935 20.733 201.000 10.651 20
DENet0.786 181.000 10.929 200.736 330.750 220.720 400.755 30.934 10.794 180.590 280.561 300.537 240.650 121.000 10.882 130.804 350.789 101.000 10.719 13
SSEC0.781 191.000 10.945 160.763 300.780 120.819 290.601 120.824 110.790 190.638 200.622 220.550 200.600 251.000 10.882 130.790 380.765 141.000 10.698 17
PointGroup0.778 201.000 10.900 300.798 200.715 280.863 160.493 250.706 240.895 50.569 320.701 80.576 170.639 221.000 10.880 160.851 220.719 240.997 230.709 15
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]
PE0.776 211.000 10.900 310.860 90.728 250.869 130.400 320.857 60.774 200.568 330.701 90.602 160.646 190.933 280.843 220.890 50.691 310.997 230.709 14
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
DD-UNet+Group0.764 221.000 10.897 330.837 120.753 190.830 260.459 280.824 110.699 290.629 220.653 150.438 330.650 121.000 10.880 160.858 190.690 321.000 10.650 21
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-instance0.762 231.000 10.923 230.765 280.785 100.905 60.600 130.655 280.646 340.683 120.647 180.530 250.650 121.000 10.824 230.830 280.693 300.944 360.644 22
Dyco3Dcopyleft0.761 241.000 10.935 170.893 30.752 210.863 170.600 130.588 330.742 250.641 190.633 200.546 220.550 320.857 320.789 320.853 210.762 160.987 300.699 16
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance0.742 251.000 10.923 230.785 220.745 230.867 140.557 180.578 360.729 260.670 160.644 190.488 290.577 311.000 10.794 300.830 280.620 381.000 10.550 32
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg0.739 261.000 10.899 320.759 310.753 200.823 280.282 360.691 260.658 320.582 310.594 260.547 210.628 231.000 10.795 290.868 140.728 221.000 10.692 18
3D-MPA0.737 271.000 10.933 180.785 220.794 90.831 250.279 380.588 330.695 300.616 230.559 310.556 180.650 121.000 10.809 270.875 110.696 281.000 10.608 29
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.731 281.000 10.992 40.779 270.609 370.746 350.308 350.867 50.601 380.607 250.539 340.519 270.550 321.000 10.824 230.869 130.729 211.000 10.616 26
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SSEN0.724 291.000 10.926 210.781 260.661 330.845 220.596 160.529 380.764 240.653 180.489 390.461 320.500 390.859 310.765 330.872 120.761 171.000 10.577 30
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
Sparse R-CNN0.714 301.000 10.926 220.694 360.699 310.890 100.636 110.516 390.693 310.743 60.588 270.369 360.601 240.594 420.800 280.886 70.676 330.986 310.546 33
SALoss-ResNet0.695 311.000 10.855 370.579 440.589 390.735 380.484 260.588 330.856 110.634 210.571 290.298 370.500 391.000 10.824 230.818 310.702 270.935 400.545 34
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-inst0.693 321.000 10.852 380.655 400.616 360.788 300.334 340.763 190.771 210.457 440.555 320.652 110.518 360.857 320.765 330.732 440.631 360.944 360.577 31
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-SCS0.688 331.000 10.913 270.730 340.737 240.743 370.442 290.855 80.655 330.546 350.546 330.263 390.508 380.889 300.568 410.771 410.705 260.889 430.625 25
3D-BoNet0.687 341.000 10.887 350.836 130.587 400.643 470.550 210.620 290.724 270.522 390.501 370.243 400.512 371.000 10.751 350.807 340.661 350.909 420.612 28
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
PCJC0.684 351.000 10.895 340.757 320.659 340.862 180.189 450.739 210.606 370.712 110.581 280.515 280.650 120.857 320.357 460.785 390.631 370.889 430.635 24
SPG_WSIS0.678 361.000 10.880 360.836 130.701 300.727 390.273 400.607 310.706 280.541 370.515 360.174 420.600 250.857 320.716 360.846 240.711 251.000 10.506 37
One_Thing_One_Clickpermissive0.675 371.000 10.823 400.782 240.621 350.766 320.211 420.736 220.560 410.586 290.522 350.636 140.453 410.641 410.853 200.850 230.694 290.997 230.411 41
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_inspermissive0.637 381.000 10.923 250.593 430.561 410.746 360.143 470.504 400.766 230.485 420.442 400.372 350.530 350.714 380.815 260.775 400.673 341.000 10.431 40
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASCpermissive0.615 390.711 450.802 410.540 450.757 180.777 310.029 480.577 370.588 400.521 400.600 240.436 340.534 340.697 390.616 400.838 260.526 400.980 330.534 35
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone0.605 401.000 10.909 280.764 290.603 380.704 410.415 310.301 450.548 420.461 430.394 410.267 380.386 430.857 320.649 390.817 320.504 410.959 340.356 44
3D-SISpermissive0.558 411.000 10.773 420.614 420.503 430.691 430.200 430.412 410.498 450.546 360.311 460.103 460.600 250.857 320.382 430.799 360.445 470.938 390.371 42
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.544 420.500 480.655 480.661 390.663 320.765 330.432 300.214 470.612 360.584 300.499 380.204 410.286 470.429 450.655 380.650 490.539 390.950 350.499 38
Hier3Dcopyleft0.540 431.000 10.727 430.626 410.467 460.693 420.200 430.412 410.480 460.528 380.318 450.077 490.600 250.688 400.382 430.768 420.472 430.941 380.350 45
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class0.497 440.250 500.902 290.689 370.540 420.747 340.276 390.610 300.268 490.489 410.348 420.000 500.243 490.220 480.663 370.814 330.459 450.928 410.496 39
tmp0.474 451.000 10.727 430.433 480.481 450.673 450.022 500.380 430.517 440.436 460.338 440.128 440.343 450.429 450.291 480.728 450.473 420.833 460.300 47
SemRegionNet-20cls0.470 461.000 10.727 430.447 470.481 440.678 440.024 490.380 430.518 430.440 450.339 430.128 440.350 440.429 450.212 490.711 460.465 440.833 460.290 48
ASIS0.422 470.333 490.707 460.676 380.401 470.650 460.350 330.177 480.594 390.376 470.202 470.077 480.404 420.571 430.197 500.674 480.447 460.500 490.260 49
3D-BEVIS0.401 480.667 460.687 470.419 490.137 500.587 480.188 460.235 460.359 480.211 490.093 500.080 470.311 460.571 430.382 430.754 430.300 490.874 450.357 43
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.390 490.556 470.636 490.493 460.353 480.539 490.271 410.160 490.450 470.359 480.178 480.146 430.250 480.143 490.347 470.698 470.436 480.667 480.331 46
MaskRCNN 2d->3d Proj0.261 500.903 440.081 500.008 500.233 490.175 500.280 370.106 500.150 500.203 500.175 490.480 300.218 500.143 490.542 420.404 500.153 500.393 500.049 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 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