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 bysorted bysort bysort bysort bysort bysort bysort by
CeCo0.340 10.551 10.247 10.181 10.475 20.057 40.142 30.000 10.000 10.000 10.387 20.463 10.499 20.924 10.774 10.213 10.257 10.000 30.546 40.100 20.006 20.615 10.177 40.534 10.246 10.000 20.400 10.000 10.338 10.006 30.484 10.609 10.000 10.083 10.000 20.873 10.089 20.661 20.000 30.048 40.560 10.408 10.892 10.000 10.000 10.586 10.616 20.000 40.692 20.900 10.721 10.162 10.228 10.860 10.000 10.000 20.575 10.083 20.550 10.347 10.624 10.410 10.360 10.740 10.109 20.321 20.660 10.000 20.121 20.939 10.143 20.000 10.400 10.003 20.190 10.564 10.652 10.615 10.421 10.304 30.579 10.547 10.000 10.000 10.296 10.000 40.030 40.096 10.000 20.916 10.037 10.551 10.171 20.376 10.865 10.286 10.000 10.633 10.102 40.027 40.011 20.000 10.000 10.474 20.742 10.133 20.311 10.824 10.242 10.503 10.068 30.828 10.000 20.429 10.000 10.063 10.000 10.781 10.000 10.000 20.000 10.665 10.633 10.450 10.818 10.000 10.000 10.429 10.532 10.226 10.825 10.510 30.377 10.709 10.079 20.000 10.753 10.683 10.102 40.063 20.401 40.620 30.000 10.619 10.000 40.000 30.000 10.595 20.000 20.000 10.345 20.564 10.411 10.603 10.384 10.945 10.266 10.643 10.367 10.304 10.663 10.000 10.010 10.726 20.767 10.898 10.000 10.784 10.435 10.861 10.000 10.447 10.000 40.257 10.656 10.377 3
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
Minkowski 34Dpermissive0.253 30.463 30.154 40.102 30.381 40.084 10.134 40.000 10.000 10.000 10.386 30.141 40.279 40.737 40.703 30.014 40.164 30.000 30.663 10.092 30.000 30.224 30.291 10.531 20.056 40.000 20.242 30.000 10.000 20.013 20.331 30.000 20.000 10.035 40.001 10.858 20.059 40.650 40.000 30.056 30.353 30.299 30.670 30.000 10.000 10.284 30.484 40.071 30.594 30.720 30.710 30.027 40.068 40.813 20.000 10.005 10.492 20.164 10.274 30.111 40.571 30.307 40.293 30.307 40.150 10.163 40.531 30.002 10.545 10.932 20.093 40.000 10.000 20.002 30.159 30.368 40.581 40.440 40.228 40.406 10.282 40.294 30.000 10.000 10.189 30.060 10.036 30.000 20.000 20.897 20.000 40.525 30.025 40.205 40.771 40.000 20.000 10.593 30.108 30.044 30.000 30.000 10.000 10.282 40.589 30.094 30.169 30.466 40.227 40.419 40.125 20.757 20.002 10.334 30.000 10.000 20.000 10.357 30.000 10.000 20.000 10.582 20.513 40.337 20.612 40.000 10.000 10.250 30.352 40.136 40.724 30.655 10.280 20.000 20.046 40.000 10.606 40.559 20.159 10.102 10.445 10.655 10.000 10.310 40.117 10.000 30.000 10.581 40.026 10.000 10.265 40.483 30.084 40.097 40.044 20.865 40.142 40.588 20.351 20.272 20.596 40.000 10.003 20.622 30.720 20.096 40.000 10.771 30.016 30.772 20.000 10.302 30.194 20.214 30.621 30.197 4
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
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


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




Method Infoavg aphead apcommon aptail apalarm 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 bysorted bysort bysort bysort bysort bysort bysort by
Mask3D Scannet2000.278 10.383 10.263 10.168 10.506 10.068 10.083 40.000 10.000 10.000 10.023 10.149 30.302 10.778 20.647 10.569 10.500 10.031 10.014 20.027 10.173 10.311 10.195 10.351 20.258 10.000 10.082 10.000 10.003 10.037 10.391 11.000 10.000 10.014 10.000 10.572 10.573 10.661 10.000 10.003 10.005 30.082 30.349 10.028 10.000 10.605 10.515 20.509 10.711 11.000 10.665 20.015 10.107 10.402 30.201 10.083 10.304 10.759 10.491 10.378 10.572 10.119 10.277 10.013 40.089 10.283 10.411 10.267 10.006 20.156 10.000 10.116 10.000 10.105 20.556 10.514 10.396 10.275 10.323 10.215 10.380 10.000 10.000 10.356 10.005 10.208 10.325 10.000 10.050 30.400 10.561 10.258 10.179 10.722 10.147 10.000 10.586 10.063 10.015 10.139 10.016 10.028 10.708 10.418 10.016 10.048 30.500 10.489 10.349 10.001 10.475 10.086 10.365 10.000 10.500 10.000 10.323 20.000 10.222 10.000 10.497 10.626 10.044 20.795 10.556 10.008 10.121 30.265 10.667 10.789 10.568 10.579 10.444 10.176 10.004 10.474 10.752 10.233 10.014 10.002 30.570 10.007 10.377 40.000 10.000 10.000 10.337 10.000 10.000 10.384 10.465 10.287 10.085 10.048 10.816 40.467 10.810 10.377 10.415 10.744 10.000 10.004 10.724 10.778 10.590 10.000 10.032 10.441 10.000 10.377 20.391 10.427 10.321 10.192 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
LGround Inst.permissive0.154 20.275 20.108 20.060 20.295 40.002 30.278 10.000 10.000 10.000 10.006 30.272 10.064 40.815 10.503 30.333 40.000 20.000 20.556 10.001 30.000 20.148 20.078 20.448 10.007 20.000 10.024 20.000 10.000 20.000 20.190 30.000 20.000 10.000 20.000 10.209 40.031 40.573 20.000 10.000 20.041 10.099 20.037 30.000 20.000 10.327 20.364 40.181 20.642 21.000 10.654 30.000 20.023 20.429 20.000 20.000 20.097 20.000 20.278 20.267 20.434 20.048 20.092 20.257 20.030 20.097 30.189 20.000 20.089 10.000 40.000 10.000 20.000 10.115 10.166 20.222 40.222 20.003 20.127 20.213 30.169 20.000 10.000 10.000 20.000 20.044 20.000 20.000 10.000 40.000 30.268 40.222 20.130 20.494 20.000 20.000 10.363 20.015 20.000 20.000 20.000 20.000 20.611 20.400 20.000 20.056 20.278 30.242 40.180 20.000 20.383 30.000 20.209 20.000 10.000 20.000 10.364 10.000 10.000 20.000 10.323 40.302 30.019 30.654 20.000 20.000 20.141 10.045 20.000 40.427 40.514 20.143 20.000 20.028 30.000 20.252 30.402 30.156 30.000 20.028 10.470 20.000 20.444 20.000 10.000 10.000 10.205 20.000 10.000 10.203 20.381 20.026 20.037 20.000 20.881 20.099 30.135 30.239 20.000 30.585 30.000 10.000 20.616 20.778 10.322 20.000 10.000 20.407 20.000 10.333 30.148 20.177 30.242 20.028 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.permissive0.130 30.246 30.083 30.043 40.299 30.000 40.278 10.000 10.000 10.000 10.022 20.175 20.122 20.537 30.521 20.400 20.000 20.000 20.000 30.008 20.000 20.048 30.076 30.182 40.000 30.000 10.022 30.000 10.000 20.000 20.141 40.000 20.000 10.000 20.000 10.210 30.063 20.547 40.000 10.000 20.000 40.100 10.026 40.000 20.000 10.241 40.488 30.000 30.564 41.000 10.672 10.000 20.021 30.486 10.000 20.000 20.067 30.000 20.194 40.033 40.415 30.026 30.025 40.271 10.004 30.094 40.142 40.000 20.000 30.111 20.000 10.000 20.000 10.088 30.083 40.278 20.110 30.000 30.082 40.199 40.137 30.000 10.000 10.000 20.000 20.041 30.000 20.000 10.308 10.067 20.280 20.016 30.101 30.373 40.000 20.000 10.319 30.007 30.000 20.000 20.000 20.000 20.028 40.355 40.000 20.101 10.444 20.289 20.114 40.000 20.394 20.000 20.032 40.000 10.000 20.000 10.201 40.000 10.000 20.000 10.384 20.248 40.000 40.529 30.000 20.000 20.133 20.020 40.089 30.720 20.500 30.099 30.000 20.000 40.000 20.238 40.334 40.190 20.000 20.000 40.317 40.000 20.472 10.000 10.000 10.000 10.094 40.000 10.000 10.082 40.236 30.004 40.019 30.000 20.883 10.061 40.262 20.217 30.000 30.557 40.000 10.000 20.460 40.761 30.156 40.000 10.000 20.259 30.000 10.394 10.019 30.084 40.232 30.000 4
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.123 40.223 40.082 40.046 30.308 20.004 20.278 10.000 10.000 10.000 10.000 40.032 40.105 30.537 30.348 40.378 30.000 20.000 20.000 30.000 40.000 20.000 40.037 40.323 30.000 30.000 10.013 40.000 10.000 20.000 20.235 20.000 20.000 10.000 20.000 10.231 20.045 30.564 30.000 10.000 20.006 20.078 40.065 20.000 20.000 10.259 30.516 10.000 30.600 31.000 10.578 40.000 20.000 40.184 40.000 20.000 20.034 40.000 20.211 30.089 30.394 40.018 40.064 30.171 30.001 40.144 20.172 30.000 20.000 30.044 30.000 10.000 20.000 10.064 40.126 30.278 20.093 40.000 30.094 30.214 20.011 40.000 10.000 10.000 20.000 20.022 40.000 20.000 10.275 20.000 30.275 30.000 40.098 40.407 30.000 20.000 10.250 40.007 40.000 20.000 20.000 20.000 20.333 30.376 30.000 20.000 40.042 40.285 30.119 30.000 20.224 40.000 20.184 30.000 10.000 20.000 10.244 30.000 10.000 20.000 10.377 30.378 20.051 10.424 40.000 20.000 20.116 40.030 30.125 20.441 30.444 40.063 40.000 20.042 20.000 20.297 20.483 20.096 40.000 20.028 10.338 30.000 20.444 20.000 10.000 10.000 10.189 30.000 10.000 10.141 30.152 40.017 30.000 40.000 20.838 30.193 20.111 40.105 40.198 20.588 20.000 10.000 20.542 30.343 40.267 30.000 10.000 20.108 40.000 10.333 30.000 40.228 20.202 40.022 3
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 30.831 170.685 60.714 10.979 10.594 30.310 150.801 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 60.840 110.757 60.853 90.580 10.848 130.709 20.643 110.958 90.587 70.295 210.753 130.884 120.758 80.815 50.725 20.927 150.867 90.743 5
OccuSeg+Semantic0.764 20.758 430.796 170.839 120.746 100.907 10.562 40.850 120.680 80.672 50.978 20.610 10.335 80.777 40.819 300.847 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 10.640 140.958 90.592 40.217 550.762 100.888 90.758 80.813 60.726 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 50.906 50.793 200.802 260.689 250.825 280.556 50.867 80.681 70.602 270.960 70.555 160.365 30.779 30.859 170.747 110.795 170.717 40.917 180.856 160.764 2
PointTransformerV20.752 50.742 500.809 120.872 10.758 50.860 60.552 60.891 50.610 270.687 20.960 70.559 140.304 180.766 80.926 20.767 60.797 130.644 190.942 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 70.793 290.790 210.807 240.750 90.856 80.524 140.881 60.588 370.642 130.977 40.591 50.274 310.781 20.929 10.804 30.796 140.642 200.947 30.885 20.715 16
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 70.909 40.818 80.811 210.752 80.839 170.485 290.842 150.673 90.644 100.957 120.528 230.305 170.773 60.859 170.788 40.818 40.693 70.916 190.856 160.723 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 90.623 740.804 130.859 30.745 110.824 300.501 210.912 20.690 50.685 30.956 130.567 110.320 130.768 70.918 30.720 190.802 90.676 100.921 160.881 40.779 1
StratifiedFormerpermissive0.747 100.901 60.803 140.845 80.757 60.846 130.512 170.825 190.696 40.645 90.956 130.576 90.262 410.744 170.861 160.742 120.770 290.705 50.899 290.860 130.734 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
Virtual MVFusion0.746 110.771 370.819 60.848 60.702 230.865 50.397 660.899 30.699 30.664 60.948 370.588 60.330 90.746 160.851 230.764 70.796 140.704 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
VMNetpermissive0.746 110.870 110.838 20.858 40.729 150.850 110.501 210.874 70.587 380.658 70.956 130.564 120.299 190.765 90.900 50.716 220.812 70.631 250.939 80.858 140.709 17
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-FPN0.744 130.842 180.800 150.767 380.740 120.836 210.541 80.914 10.672 100.626 160.958 90.552 170.272 330.777 40.886 110.696 290.801 100.674 110.941 60.858 140.717 15
EQ-Net0.743 140.620 750.799 160.849 50.730 140.822 320.493 270.897 40.664 110.681 40.955 170.562 130.378 10.760 110.903 40.738 130.801 100.673 120.907 220.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 150.860 130.765 320.819 160.769 30.848 120.533 100.829 180.663 120.631 150.955 170.586 80.274 310.753 130.896 60.729 140.760 340.666 140.921 160.855 180.733 7
MinkowskiNetpermissive0.736 160.859 140.818 80.832 130.709 200.840 160.521 160.853 110.660 140.643 110.951 280.544 180.286 260.731 180.893 70.675 360.772 270.683 90.874 470.852 200.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 170.890 70.837 30.864 20.726 160.873 20.530 130.824 200.489 680.647 80.978 20.609 20.336 70.624 340.733 430.758 80.776 250.570 500.949 20.877 50.728 8
SparseConvNet0.725 180.647 710.821 50.846 70.721 180.869 30.533 100.754 390.603 330.614 200.955 170.572 100.325 110.710 190.870 130.724 170.823 20.628 260.934 110.865 110.683 23
PointTransformer++0.725 180.727 570.811 110.819 160.765 40.841 150.502 200.814 250.621 230.623 170.955 170.556 150.284 270.620 350.866 140.781 50.757 370.648 170.932 130.862 120.709 17
MatchingNet0.724 200.812 260.812 100.810 220.735 130.834 220.495 260.860 100.572 440.602 270.954 210.512 260.280 280.757 120.845 250.725 160.780 230.606 360.937 90.851 210.700 20
INS-Conv-semantic0.717 210.751 460.759 350.812 200.704 220.868 40.537 90.842 150.609 290.608 230.953 230.534 190.293 220.616 360.864 150.719 210.793 180.640 210.933 120.845 250.663 28
PointMetaBase0.714 220.835 190.785 230.821 140.684 270.846 130.531 120.865 90.614 240.596 300.953 230.500 290.246 470.674 200.888 90.692 300.764 310.624 270.849 610.844 260.675 25
contrastBoundarypermissive0.705 230.769 400.775 280.809 230.687 260.820 350.439 530.812 260.661 130.591 330.945 460.515 250.171 730.633 310.856 190.720 190.796 140.668 130.889 360.847 230.689 22
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 240.889 80.745 440.813 190.672 290.818 390.493 270.815 230.623 210.610 210.947 390.470 380.249 460.594 390.848 240.705 260.779 240.646 180.892 340.823 330.611 43
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 Click0.701 250.825 230.796 170.723 450.716 190.832 230.433 550.816 210.634 190.609 220.969 60.418 630.344 50.559 500.833 270.715 230.808 80.560 540.902 260.847 230.680 24
JSENetpermissive0.699 260.881 100.762 330.821 140.667 300.800 510.522 150.792 310.613 250.607 240.935 650.492 310.205 600.576 440.853 210.691 310.758 360.652 160.872 500.828 300.649 32
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-IIpermissive0.696 270.704 610.790 210.787 300.709 200.837 190.459 380.815 230.543 530.615 190.956 130.529 210.250 440.551 550.790 350.703 270.799 120.619 310.908 210.848 220.700 20
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 280.743 490.794 190.655 680.684 270.822 320.497 250.719 490.622 220.617 180.977 40.447 500.339 60.750 150.664 580.703 270.790 200.596 400.946 40.855 180.647 33
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 290.884 90.754 390.795 290.647 350.818 390.422 570.802 290.612 260.604 250.945 460.462 410.189 680.563 490.853 210.726 150.765 300.632 240.904 240.821 360.606 47
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 300.704 610.741 480.754 420.656 310.829 250.501 210.741 440.609 290.548 400.950 320.522 240.371 20.633 310.756 380.715 230.771 280.623 280.861 570.814 380.658 29
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 310.866 120.748 410.819 160.645 370.794 540.450 430.802 290.587 380.604 250.945 460.464 400.201 630.554 520.840 260.723 180.732 460.602 380.907 220.822 350.603 50
KP-FCNN0.684 320.847 170.758 370.784 320.647 350.814 420.473 310.772 340.605 310.594 320.935 650.450 480.181 710.587 400.805 330.690 320.785 220.614 320.882 400.819 370.632 38
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 320.728 560.757 380.776 340.690 240.804 490.464 360.816 210.577 430.587 340.945 460.508 280.276 300.671 210.710 480.663 410.750 400.589 450.881 410.832 290.653 31
Superpoint Network0.683 340.851 160.728 520.800 280.653 330.806 470.468 330.804 270.572 440.602 270.946 430.453 470.239 500.519 610.822 280.689 340.762 330.595 420.895 320.827 310.630 39
PointContrast_LA_SEM0.683 340.757 440.784 240.786 310.639 390.824 300.408 600.775 330.604 320.541 420.934 690.532 200.269 370.552 530.777 360.645 510.793 180.640 210.913 200.824 320.671 26
VI-PointConv0.676 360.770 390.754 390.783 330.621 430.814 420.552 60.758 370.571 460.557 380.954 210.529 210.268 390.530 590.682 530.675 360.719 490.603 370.888 370.833 280.665 27
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 370.789 300.748 410.763 400.635 410.814 420.407 620.747 410.581 420.573 350.950 320.484 320.271 350.607 370.754 390.649 460.774 260.596 400.883 390.823 330.606 47
SALANet0.670 380.816 250.770 300.768 370.652 340.807 460.451 400.747 410.659 150.545 410.924 750.473 370.149 830.571 460.811 320.635 540.746 410.623 280.892 340.794 500.570 60
PointConvpermissive0.666 390.781 320.759 350.699 530.644 380.822 320.475 300.779 320.564 490.504 580.953 230.428 570.203 620.586 420.754 390.661 420.753 380.588 460.902 260.813 400.642 34
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 390.703 630.781 260.751 440.655 320.830 240.471 320.769 350.474 710.537 440.951 280.475 360.279 290.635 290.698 520.675 360.751 390.553 590.816 680.806 420.703 19
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++permissive0.663 410.746 470.708 550.722 460.638 400.820 350.451 400.566 750.599 350.541 420.950 320.510 270.313 140.648 260.819 300.616 590.682 640.590 440.869 530.810 410.656 30
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 420.778 330.702 580.806 250.619 440.813 450.468 330.693 570.494 640.524 500.941 570.449 490.298 200.510 630.821 290.675 360.727 480.568 520.826 660.803 440.637 36
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 430.698 640.743 460.650 690.564 610.820 350.505 190.758 370.631 200.479 620.945 460.480 340.226 510.572 450.774 370.690 320.735 440.614 320.853 600.776 640.597 53
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 440.752 450.734 500.664 660.583 560.815 410.399 650.754 390.639 170.535 460.942 550.470 380.309 160.665 220.539 650.650 450.708 540.635 230.857 590.793 520.642 34
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 450.778 330.731 510.699 530.577 570.829 250.446 450.736 450.477 700.523 520.945 460.454 450.269 370.484 700.749 420.618 570.738 420.599 390.827 650.792 550.621 41
PointConv-SFPN0.641 460.776 350.703 570.721 470.557 640.826 270.451 400.672 610.563 500.483 610.943 540.425 600.162 780.644 270.726 440.659 430.709 530.572 490.875 450.786 590.559 65
MVPNetpermissive0.641 460.831 200.715 530.671 630.590 520.781 600.394 670.679 590.642 160.553 390.937 620.462 410.256 420.649 250.406 780.626 550.691 610.666 140.877 430.792 550.608 46
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 480.717 600.701 590.692 560.576 580.801 500.467 350.716 500.563 500.459 670.953 230.429 560.169 750.581 430.854 200.605 600.710 510.550 600.894 330.793 520.575 58
FPConvpermissive0.639 490.785 310.760 340.713 510.603 470.798 520.392 680.534 800.603 330.524 500.948 370.457 430.250 440.538 570.723 460.598 640.696 590.614 320.872 500.799 450.567 62
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 500.797 280.769 310.641 740.590 520.820 350.461 370.537 790.637 180.536 450.947 390.388 700.206 590.656 230.668 560.647 490.732 460.585 470.868 540.793 520.473 83
PointSPNet0.637 510.734 530.692 660.714 500.576 580.797 530.446 450.743 430.598 360.437 720.942 550.403 660.150 820.626 330.800 340.649 460.697 580.557 570.846 620.777 630.563 63
SConv0.636 520.830 210.697 620.752 430.572 600.780 620.445 470.716 500.529 560.530 470.951 280.446 510.170 740.507 650.666 570.636 530.682 640.541 650.886 380.799 450.594 54
Supervoxel-CNN0.635 530.656 690.711 540.719 480.613 450.757 710.444 500.765 360.534 550.566 360.928 730.478 350.272 330.636 280.531 670.664 400.645 740.508 720.864 560.792 550.611 43
joint point-basedpermissive0.634 540.614 760.778 270.667 650.633 420.825 280.420 580.804 270.467 730.561 370.951 280.494 300.291 230.566 470.458 730.579 700.764 310.559 560.838 630.814 380.598 52
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 550.731 540.688 690.675 600.591 510.784 590.444 500.565 760.610 270.492 590.949 350.456 440.254 430.587 400.706 490.599 630.665 700.612 350.868 540.791 580.579 57
3DSM_DMMF0.631 560.626 730.745 440.801 270.607 460.751 720.506 180.729 480.565 480.491 600.866 890.434 520.197 660.595 380.630 600.709 250.705 560.560 540.875 450.740 740.491 78
APCF-Net0.631 560.742 500.687 710.672 610.557 640.792 570.408 600.665 620.545 520.508 550.952 270.428 570.186 690.634 300.702 500.620 560.706 550.555 580.873 480.798 470.581 56
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN0.631 560.771 370.692 660.672 610.524 680.837 190.440 520.706 550.538 540.446 690.944 520.421 620.219 540.552 530.751 410.591 660.737 430.543 640.901 280.768 660.557 66
FusionAwareConv0.630 590.604 780.741 480.766 390.590 520.747 730.501 210.734 460.503 630.527 480.919 790.454 450.323 120.550 560.420 770.678 350.688 620.544 620.896 310.795 490.627 40
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 600.800 270.625 810.719 480.545 660.806 470.445 470.597 700.448 770.519 530.938 610.481 330.328 100.489 690.499 720.657 440.759 350.592 430.881 410.797 480.634 37
SegGroup_sempermissive0.627 610.818 240.747 430.701 520.602 480.764 680.385 720.629 670.490 660.508 550.931 720.409 650.201 630.564 480.725 450.618 570.692 600.539 660.873 480.794 500.548 69
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 620.830 210.694 640.757 410.563 620.772 660.448 440.647 650.520 580.509 540.949 350.431 550.191 670.496 670.614 610.647 490.672 680.535 680.876 440.783 600.571 59
HPEIN0.618 630.729 550.668 720.647 710.597 500.766 670.414 590.680 580.520 580.525 490.946 430.432 530.215 560.493 680.599 620.638 520.617 790.570 500.897 300.806 420.605 49
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-GCNpermissive0.610 640.858 150.772 290.489 860.532 670.792 570.404 640.643 660.570 470.507 570.935 650.414 640.046 920.510 630.702 500.602 620.705 560.549 610.859 580.773 650.534 72
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 650.760 420.667 730.649 700.521 690.793 550.457 390.648 640.528 570.434 740.947 390.401 670.153 810.454 720.721 470.648 480.717 500.536 670.904 240.765 670.485 79
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 660.634 720.743 460.697 550.601 490.781 600.437 540.585 730.493 650.446 690.933 700.394 680.011 940.654 240.661 590.603 610.733 450.526 690.832 640.761 690.480 80
LAP-D0.594 670.720 580.692 660.637 750.456 780.773 650.391 700.730 470.587 380.445 710.940 590.381 710.288 240.434 750.453 750.591 660.649 720.581 480.777 720.749 730.610 45
DPC0.592 680.720 580.700 600.602 790.480 740.762 700.380 730.713 530.585 410.437 720.940 590.369 730.288 240.434 750.509 710.590 680.639 770.567 530.772 730.755 710.592 55
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 690.766 410.659 760.683 580.470 770.740 750.387 710.620 690.490 660.476 630.922 770.355 760.245 480.511 620.511 700.571 710.643 750.493 760.872 500.762 680.600 51
ROSMRF0.580 700.772 360.707 560.681 590.563 620.764 680.362 750.515 810.465 740.465 660.936 640.427 590.207 580.438 730.577 630.536 740.675 670.486 770.723 790.779 610.524 74
SD-DETR0.576 710.746 470.609 850.445 900.517 700.643 860.366 740.714 520.456 750.468 650.870 880.432 530.264 400.558 510.674 540.586 690.688 620.482 780.739 770.733 760.537 71
SQN_0.1%0.569 720.676 660.696 630.657 670.497 710.779 630.424 560.548 770.515 600.376 790.902 860.422 610.357 40.379 790.456 740.596 650.659 710.544 620.685 820.665 870.556 67
TextureNetpermissive0.566 730.672 680.664 740.671 630.494 720.719 760.445 470.678 600.411 830.396 770.935 650.356 750.225 520.412 770.535 660.565 720.636 780.464 800.794 710.680 840.568 61
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
DVVNet0.562 740.648 700.700 600.770 360.586 550.687 800.333 790.650 630.514 610.475 640.906 830.359 740.223 530.340 810.442 760.422 850.668 690.501 730.708 800.779 610.534 72
Pointnet++ & Featurepermissive0.557 750.735 520.661 750.686 570.491 730.744 740.392 680.539 780.451 760.375 800.946 430.376 720.205 600.403 780.356 810.553 730.643 750.497 740.824 670.756 700.515 75
GMLPs0.538 760.495 860.693 650.647 710.471 760.793 550.300 820.477 820.505 620.358 810.903 850.327 790.081 890.472 710.529 680.448 830.710 510.509 700.746 750.737 750.554 68
PanopticFusion-label0.529 770.491 870.688 690.604 780.386 830.632 870.225 920.705 560.434 800.293 870.815 900.348 770.241 490.499 660.669 550.507 760.649 720.442 860.796 700.602 900.561 64
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_weak0.516 780.676 660.591 880.609 760.442 790.774 640.335 780.597 700.422 820.357 820.932 710.341 780.094 880.298 830.528 690.473 810.676 660.495 750.602 880.721 790.349 90
Online SegFusion0.515 790.607 770.644 790.579 810.434 800.630 880.353 760.628 680.440 780.410 750.762 930.307 810.167 760.520 600.403 790.516 750.565 820.447 840.678 830.701 810.514 76
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, FTSDF0.501 800.558 820.608 860.424 920.478 750.690 790.246 880.586 720.468 720.450 680.911 810.394 680.160 790.438 730.212 880.432 840.541 870.475 790.742 760.727 770.477 81
PCNN0.498 810.559 810.644 790.560 830.420 820.711 780.229 900.414 830.436 790.352 830.941 570.324 800.155 800.238 880.387 800.493 770.529 880.509 700.813 690.751 720.504 77
3DMV0.484 820.484 880.538 900.643 730.424 810.606 910.310 800.574 740.433 810.378 780.796 910.301 820.214 570.537 580.208 890.472 820.507 910.413 890.693 810.602 900.539 70
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 830.577 800.611 840.356 940.321 910.715 770.299 840.376 870.328 900.319 850.944 520.285 840.164 770.216 910.229 860.484 790.545 860.456 820.755 740.709 800.475 82
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 840.679 650.604 870.578 820.380 840.682 810.291 850.106 930.483 690.258 920.920 780.258 880.025 930.231 900.325 820.480 800.560 840.463 810.725 780.666 860.231 94
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 850.474 890.623 820.463 880.366 860.651 840.310 800.389 860.349 880.330 840.937 620.271 860.126 850.285 840.224 870.350 900.577 810.445 850.625 860.723 780.394 86
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
PNET20.442 860.548 830.548 890.597 800.363 870.628 890.300 820.292 880.374 850.307 860.881 870.268 870.186 690.238 880.204 900.407 860.506 920.449 830.667 840.620 890.462 84
SurfaceConvPF0.442 860.505 850.622 830.380 930.342 890.654 830.227 910.397 850.367 860.276 890.924 750.240 890.198 650.359 800.262 840.366 870.581 800.435 870.640 850.668 850.398 85
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 880.437 910.646 780.474 870.369 850.645 850.353 760.258 900.282 920.279 880.918 800.298 830.147 840.283 850.294 830.487 780.562 830.427 880.619 870.633 880.352 89
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 890.525 840.647 770.522 840.324 900.488 940.077 950.712 540.353 870.401 760.636 950.281 850.176 720.340 810.565 640.175 940.551 850.398 900.370 940.602 900.361 88
SimConv0.410 900.000 950.782 250.772 350.722 170.838 180.407 620.000 960.000 960.595 310.947 390.000 960.270 360.000 960.000 960.000 960.786 210.621 300.000 960.841 270.621 41
SPLAT Netcopyleft0.393 910.472 900.511 910.606 770.311 920.656 820.245 890.405 840.328 900.197 930.927 740.227 910.000 960.001 950.249 850.271 930.510 890.383 920.593 890.699 820.267 92
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+FTSDF0.383 920.297 930.491 920.432 910.358 880.612 900.274 860.116 920.411 830.265 900.904 840.229 900.079 900.250 860.185 910.320 910.510 890.385 910.548 900.597 930.394 86
PointNet++permissive0.339 930.584 790.478 930.458 890.256 940.360 950.250 870.247 910.278 930.261 910.677 940.183 920.117 860.212 920.145 930.364 880.346 950.232 950.548 900.523 940.252 93
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 940.353 920.290 950.278 950.166 950.553 920.169 940.286 890.147 940.148 950.908 820.182 930.064 910.023 940.018 950.354 890.363 930.345 930.546 920.685 830.278 91
ScanNetpermissive0.306 950.203 940.366 940.501 850.311 920.524 930.211 930.002 950.342 890.189 940.786 920.145 940.102 870.245 870.152 920.318 920.348 940.300 940.460 930.437 950.182 95
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 960.000 950.041 960.172 960.030 960.062 960.001 960.035 940.004 950.051 960.143 960.019 950.003 950.041 930.050 940.003 950.054 960.018 960.005 950.264 960.082 96


This table lists the benchmark results for the 3D 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
PBNetpermissive0.573 10.926 20.575 80.619 10.472 10.736 40.239 30.487 210.383 20.459 20.506 50.533 60.585 40.767 70.404 60.717 20.559 30.969 10.381 4
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. Arxiv
Mask3D0.566 20.926 20.597 40.408 160.420 20.737 30.239 20.598 70.386 10.458 30.549 10.568 40.716 10.601 230.480 30.646 60.575 20.922 30.364 5
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
GraphCut0.552 31.000 10.611 30.438 120.392 50.714 50.139 60.598 80.327 40.389 50.510 40.598 10.427 190.754 100.463 40.761 10.588 10.903 60.329 12
SPFormerpermissive0.549 40.745 110.640 10.484 50.395 40.739 20.311 10.566 120.335 30.468 10.492 60.555 50.478 110.747 120.436 50.712 30.540 40.893 80.343 11
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023
DKNet0.532 50.815 60.624 20.517 30.377 70.749 10.107 80.509 180.304 60.437 40.475 70.581 20.539 70.775 60.339 100.640 80.506 60.901 70.385 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst0.520 60.889 40.551 110.548 20.418 30.665 140.064 160.585 90.260 130.277 150.471 90.500 70.644 20.785 40.369 70.591 130.511 50.878 140.362 6
SoftGroup++0.513 70.704 170.578 70.398 170.363 110.704 60.061 170.647 40.297 110.378 80.537 20.343 90.614 30.828 30.295 150.710 50.505 70.875 160.394 1
SSTNetpermissive0.506 80.738 140.549 120.497 40.316 150.693 90.178 50.377 280.198 180.330 90.463 100.576 30.515 90.857 20.494 10.637 90.457 120.943 20.290 19
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGrouppermissive0.504 90.667 230.579 60.372 200.381 60.694 80.072 130.677 20.303 70.387 60.531 30.319 130.582 50.754 90.318 110.643 70.492 80.907 50.388 2
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
OccuSeg+instance0.486 100.802 70.536 140.428 140.369 80.702 70.205 40.331 330.301 80.379 70.474 80.327 100.437 150.862 10.485 20.601 120.394 210.846 240.273 21
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg0.479 110.704 170.564 90.467 80.366 90.633 210.068 140.554 150.262 120.328 100.447 110.323 110.534 80.722 140.288 170.614 100.482 90.912 40.358 8
SSEC0.462 120.778 80.586 50.394 180.341 120.674 110.114 70.556 140.313 50.303 120.430 120.264 170.358 250.616 220.295 140.589 140.467 110.880 120.355 9
HAISpermissive0.457 130.704 170.561 100.457 90.364 100.673 120.046 240.547 160.194 190.308 110.426 130.288 150.454 140.711 150.262 200.563 210.434 150.889 100.344 10
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group0.436 140.630 290.508 210.480 60.310 160.624 240.065 150.638 50.174 200.256 190.384 170.194 260.428 170.759 80.289 160.574 180.400 190.849 220.291 18
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-instance0.435 150.716 160.495 230.355 220.331 130.689 100.102 100.394 270.208 170.280 130.395 160.250 190.544 60.741 130.309 130.536 270.391 220.842 270.258 25
Mask-Group0.434 160.778 80.516 180.471 70.330 140.658 150.029 260.526 170.249 140.256 180.400 150.309 140.384 230.296 410.368 80.575 170.425 160.877 150.362 7
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask0.433 170.741 120.463 280.433 130.283 180.625 230.103 90.298 370.125 270.260 170.424 140.322 120.472 120.701 170.363 90.711 40.309 350.882 110.272 23
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN0.428 180.630 290.508 200.367 210.249 250.658 160.016 330.673 30.131 260.234 220.383 180.270 160.434 160.748 110.274 190.609 110.406 180.842 260.267 24
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet0.413 190.741 120.520 160.237 330.284 170.523 310.097 110.691 10.138 230.209 320.229 330.238 210.390 210.707 160.310 120.448 360.470 100.892 90.310 14
PointGroup0.407 200.639 280.496 220.415 150.243 270.645 200.021 310.570 110.114 280.211 300.359 200.217 240.428 180.660 190.256 210.562 220.341 270.860 190.291 17
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]
CSC-Pretrained0.405 210.738 140.465 270.331 260.205 300.655 170.051 210.601 60.092 310.211 310.329 230.198 250.459 130.775 50.195 280.524 290.400 200.878 130.184 32
PE0.396 220.667 230.467 260.446 110.243 260.624 250.022 300.577 100.106 290.219 250.340 210.239 200.487 100.475 320.225 240.541 260.350 250.818 280.273 22
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3Dcopyleft0.395 230.642 270.518 170.447 100.259 240.666 130.050 220.251 410.166 210.231 230.362 190.232 220.331 270.535 260.229 230.587 150.438 140.850 200.317 13
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS0.392 240.778 80.530 150.220 350.278 190.567 280.083 120.330 340.299 90.270 160.310 260.143 310.260 310.624 210.277 180.568 200.361 230.865 180.301 15
SSEN0.384 250.852 50.494 240.192 360.226 290.648 190.022 290.398 260.299 100.277 140.317 250.231 230.194 380.514 290.196 260.586 160.444 130.843 250.184 31
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
PCJC0.375 260.704 170.542 130.284 300.197 320.649 180.006 370.426 220.138 240.242 200.304 270.183 290.388 220.629 200.141 380.546 250.344 260.738 340.283 20
SphereSeg0.357 270.651 260.411 300.345 230.264 230.630 220.059 180.289 390.212 150.240 210.336 220.158 300.305 280.557 240.159 340.455 350.341 280.726 360.294 16
3D-MPA0.355 280.457 400.484 250.299 280.277 200.591 270.047 230.332 310.212 160.217 260.278 280.193 270.413 200.410 350.195 270.574 190.352 240.849 210.213 29
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF0.353 290.593 310.511 190.375 190.264 220.597 260.008 350.332 320.160 220.229 240.274 300.000 510.206 350.678 180.155 350.485 310.422 170.816 290.254 26
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
RWSeg0.348 300.475 370.456 290.320 270.275 210.476 330.020 320.491 200.056 380.212 290.320 240.261 180.302 290.520 270.182 300.557 230.285 370.867 170.197 30
GICN0.341 310.580 320.371 320.344 240.198 310.469 340.052 200.564 130.093 300.212 280.212 350.127 330.347 260.537 250.206 250.525 280.329 300.729 350.241 27
One_Thing_One_Clickpermissive0.326 320.472 380.361 330.232 340.183 330.555 290.000 440.498 190.038 400.195 330.226 340.362 80.168 390.469 330.251 220.553 240.335 290.846 230.117 40
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS0.320 330.679 220.352 340.334 250.229 280.436 350.025 270.412 250.058 360.161 390.240 320.085 350.262 300.496 310.187 290.467 330.328 310.775 300.231 28
Sparse R-CNN0.292 340.704 170.213 440.153 380.154 350.551 300.053 190.212 420.132 250.174 360.274 290.070 370.363 240.441 340.176 310.424 380.234 390.758 320.161 36
MTML0.282 350.577 330.380 310.182 370.107 410.430 360.001 410.422 230.057 370.179 350.162 380.070 380.229 330.511 300.161 320.491 300.313 320.650 410.162 34
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet0.262 360.667 230.335 350.067 450.123 390.427 370.022 280.280 400.058 350.216 270.211 360.039 410.142 410.519 280.106 420.338 420.310 340.721 370.138 37
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.254 370.463 390.249 430.113 390.167 340.412 390.000 430.374 290.073 320.173 370.243 310.130 320.228 340.368 370.160 330.356 400.208 400.711 380.136 38
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet0.253 380.519 350.324 380.251 320.137 380.345 440.031 250.419 240.069 330.162 380.131 400.052 390.202 370.338 390.147 370.301 450.303 360.651 400.178 33
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
SPG_WSIS0.251 390.380 420.274 410.289 290.144 360.413 380.000 440.311 350.065 340.113 410.130 410.029 430.204 360.388 360.108 410.459 340.311 330.769 310.127 39
SegGroup_inspermissive0.246 400.556 340.335 360.062 470.115 400.490 320.000 440.297 380.018 440.186 340.142 390.083 360.233 320.216 430.153 360.469 320.251 380.744 330.083 43
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst0.214 410.250 460.330 370.275 310.103 420.228 500.000 440.345 300.024 420.088 430.203 370.186 280.167 400.367 380.125 390.221 480.112 500.666 390.162 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)
UNet-backbone0.161 420.519 350.259 420.084 410.059 440.325 460.002 390.093 470.009 460.077 450.064 440.045 400.044 480.161 450.045 440.331 430.180 420.566 420.033 51
3D-SISpermissive0.161 420.407 410.155 480.068 440.043 480.346 430.001 400.134 440.005 470.088 420.106 430.037 420.135 430.321 400.028 470.339 410.116 490.466 450.093 42
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.158 440.356 430.173 460.113 400.140 370.359 400.012 340.023 490.039 390.134 400.123 420.008 470.089 440.149 460.117 400.221 470.128 470.563 430.094 41
Region-18class0.146 450.175 500.321 390.080 420.062 430.357 410.000 440.307 360.002 480.066 460.044 460.000 510.018 500.036 500.054 430.447 370.133 450.472 440.060 46
SemRegionNet-20cls0.121 460.296 450.203 450.071 430.058 450.349 420.000 440.150 430.019 430.054 470.034 480.017 460.052 460.042 490.013 500.209 490.183 410.371 460.057 47
3D-BEVIS0.117 470.250 460.308 400.020 510.009 520.269 490.006 380.008 500.029 410.037 500.014 510.003 490.036 490.147 470.042 450.381 390.118 480.362 470.069 45
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3Dcopyleft0.117 470.222 480.161 470.054 490.027 490.289 470.000 440.124 450.001 500.079 440.061 450.027 440.141 420.240 420.005 510.310 440.129 460.153 510.081 44
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp0.113 490.333 440.151 490.056 480.053 460.344 450.000 440.105 460.016 450.049 480.035 470.020 450.053 450.048 480.013 490.183 500.173 430.344 480.054 48
ASIS0.085 500.037 510.080 510.066 460.047 470.282 480.000 440.052 480.002 490.047 490.026 490.001 500.046 470.194 440.031 460.264 460.140 440.167 500.047 50
Sgpn_scannet0.049 510.023 520.134 500.031 500.013 510.144 510.006 360.008 510.000 510.028 510.017 500.003 480.009 520.000 510.021 480.122 510.095 510.175 490.054 49
MaskRCNN 2d->3d Proj0.022 520.185 490.000 520.000 520.015 500.000 520.000 420.006 520.000 510.010 520.006 520.107 340.012 510.000 510.002 520.027 520.004 520.022 520.001 52


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