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 iouwallchairfloortabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
CeCo0.340 10.551 10.247 10.181 10.784 10.661 20.939 10.564 10.624 10.721 10.484 10.429 10.575 10.027 40.774 10.503 10.753 10.242 10.656 10.945 10.534 10.865 10.860 10.177 40.616 20.400 10.818 10.579 10.615 10.367 10.408 10.726 20.633 10.162 10.360 10.619 10.000 10.828 10.873 10.924 10.109 20.083 20.564 10.057 40.475 20.266 10.781 10.767 10.257 10.100 20.825 10.663 10.048 40.620 30.551 10.595 20.532 10.692 20.246 10.000 30.213 10.615 10.861 10.376 10.900 10.000 10.102 40.660 10.321 20.547 10.226 10.000 10.311 10.742 10.011 20.006 20.000 10.000 20.546 40.824 10.345 20.665 10.450 10.435 10.683 10.411 10.338 10.000 30.000 10.030 40.000 10.068 30.892 10.000 10.063 20.000 40.257 10.304 30.387 20.079 20.228 10.190 10.000 40.586 10.347 10.133 20.000 20.037 10.377 30.000 10.384 10.006 30.003 20.421 10.410 10.643 10.171 20.121 20.142 30.000 10.510 30.447 10.474 20.000 10.000 40.286 10.083 10.000 30.000 10.603 10.096 10.063 10.000 20.000 10.000 10.898 10.000 10.429 10.000 10.400 10.550 10.000 10.633 10.000 10.000 10.377 10.000 40.916 10.000 20.000 10.000 10.000 20.000 10.102 40.499 20.296 10.463 10.089 20.304 10.740 10.401 40.010 10.000 10.560 10.000 10.000 20.709 10.652 10.000 20.000 10.000 10.143 20.000 20.000 10.609 10.000 1
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
LGroundpermissive0.272 20.485 20.184 20.106 20.778 20.676 10.932 20.479 40.572 20.718 20.399 20.265 20.453 30.085 20.745 20.446 20.726 20.232 30.622 20.901 20.512 30.826 20.786 30.178 30.549 30.277 20.659 30.381 20.518 20.295 40.323 20.777 10.599 20.028 30.321 20.363 30.000 10.708 30.858 20.746 30.063 30.022 30.457 20.077 20.476 10.243 20.402 20.397 40.233 20.077 40.720 40.610 30.103 10.629 20.437 40.626 10.446 20.702 10.190 20.005 10.058 30.322 20.702 30.244 20.768 20.000 10.134 30.552 20.279 30.395 20.147 30.000 10.207 20.612 20.000 30.000 30.000 10.000 20.658 20.566 20.323 30.525 40.229 30.179 20.467 40.154 30.000 20.002 10.000 10.051 10.000 10.127 10.703 20.000 10.000 30.216 10.112 40.358 20.547 10.187 10.092 30.156 40.055 30.296 20.252 20.143 10.000 20.014 20.398 20.000 10.028 30.173 10.000 40.265 30.348 20.415 40.179 10.019 30.218 10.000 10.597 20.274 40.565 10.000 10.012 30.000 20.039 30.022 20.000 10.117 20.000 20.000 20.000 20.000 10.000 10.324 30.000 10.384 20.000 10.000 20.251 40.000 10.566 20.000 10.000 10.066 30.404 10.886 30.199 10.000 10.000 10.059 10.000 10.136 10.540 10.127 40.295 20.085 30.143 40.514 20.413 30.000 30.000 10.498 20.000 10.000 20.000 20.623 20.000 20.000 10.000 10.132 30.000 20.000 10.000 20.000 1
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 40.766 40.659 30.930 40.494 20.542 40.700 40.314 40.215 40.430 40.121 10.697 40.441 30.683 30.235 20.609 40.895 30.476 40.816 30.770 40.186 20.634 10.216 40.734 20.340 30.471 30.307 30.293 40.591 40.542 40.076 20.205 40.464 20.000 10.484 40.832 40.766 20.052 40.000 40.413 30.059 30.418 30.222 30.318 40.609 30.206 40.112 10.743 20.625 20.076 20.579 40.548 20.590 30.371 30.552 40.081 30.003 20.142 20.201 40.638 40.233 30.686 40.000 10.142 20.444 40.375 10.247 40.198 20.000 10.128 40.454 40.019 10.097 10.000 10.000 20.553 30.557 30.373 10.545 30.164 40.014 40.547 30.174 20.000 20.002 10.000 10.037 20.000 10.063 40.664 40.000 10.000 30.130 20.170 20.152 40.335 40.079 20.110 20.175 20.098 20.175 40.166 30.045 40.207 10.014 20.465 10.000 10.001 40.001 40.046 10.299 20.327 30.537 30.033 30.012 40.186 20.000 10.205 40.377 20.463 30.000 10.058 20.000 20.055 20.041 10.000 10.105 30.000 20.000 20.000 20.000 10.000 10.398 20.000 10.308 40.000 10.000 20.319 20.000 10.543 30.000 10.000 10.062 40.004 30.862 40.000 20.000 10.000 10.000 20.000 10.123 20.316 30.225 20.250 30.094 10.180 30.332 30.441 20.000 30.000 10.310 40.000 10.000 20.000 20.592 30.000 20.000 10.000 10.203 10.000 20.000 10.000 20.000 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 30.771 30.650 40.932 20.483 30.571 30.710 30.331 30.250 30.492 20.044 30.703 30.419 40.606 40.227 40.621 30.865 40.531 20.771 40.813 20.291 10.484 40.242 30.612 40.282 40.440 40.351 20.299 30.622 30.593 30.027 40.293 30.310 40.000 10.757 20.858 20.737 40.150 10.164 10.368 40.084 10.381 40.142 40.357 30.720 20.214 30.092 30.724 30.596 40.056 30.655 10.525 30.581 40.352 40.594 30.056 40.000 30.014 40.224 30.772 20.205 40.720 30.000 10.159 10.531 30.163 40.294 30.136 40.000 10.169 30.589 30.000 30.000 30.000 10.002 10.663 10.466 40.265 40.582 20.337 20.016 30.559 20.084 40.000 20.000 30.000 10.036 30.000 10.125 20.670 30.000 10.102 10.071 30.164 30.406 10.386 30.046 40.068 40.159 30.117 10.284 30.111 40.094 30.000 20.000 40.197 40.000 10.044 20.013 20.002 30.228 40.307 40.588 20.025 40.545 10.134 40.000 10.655 10.302 30.282 40.000 10.060 10.000 20.035 40.000 30.000 10.097 40.000 20.000 20.005 10.000 10.000 10.096 40.000 10.334 30.000 10.000 20.274 30.000 10.513 40.000 10.000 10.280 20.194 20.897 20.000 20.000 10.000 10.000 20.000 10.108 30.279 40.189 30.141 40.059 40.272 20.307 40.445 10.003 20.000 10.353 30.000 10.026 10.000 20.581 40.001 10.000 10.000 10.093 40.002 10.000 10.000 20.000 1
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 aphead apcommon aptail apchairtabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
Mask3D Scannet2000.278 10.383 10.263 10.168 10.661 10.465 10.572 10.665 20.391 10.121 30.304 10.015 10.647 10.349 10.474 10.489 10.321 10.816 40.351 20.722 10.402 30.195 10.515 20.082 10.795 10.215 10.396 10.377 10.082 30.724 10.586 10.015 10.277 10.377 40.201 10.475 10.572 10.778 20.089 10.759 10.556 10.068 10.506 10.467 10.323 20.778 10.427 10.027 10.789 10.744 10.003 10.570 10.561 10.337 10.265 10.711 10.258 10.031 10.569 10.311 10.441 10.179 11.000 10.000 10.233 10.411 10.283 10.380 10.667 10.016 10.048 30.418 10.139 10.173 10.000 10.086 10.014 20.500 10.384 10.497 10.044 20.032 10.752 10.287 10.003 10.000 10.007 10.208 10.000 10.001 10.349 10.008 10.014 10.509 10.500 10.323 10.023 10.176 10.107 10.105 20.000 10.605 10.378 10.016 10.000 10.400 10.192 10.000 10.048 10.037 10.000 10.275 10.119 10.810 10.258 10.006 20.083 40.000 10.568 10.377 20.708 10.000 10.005 10.147 10.014 10.000 10.556 10.085 10.325 10.500 10.083 10.004 10.000 10.590 10.000 10.365 10.000 10.116 10.491 10.000 10.626 10.000 10.000 10.579 10.391 10.050 30.000 10.028 10.000 10.222 10.000 10.063 10.302 10.356 10.149 30.573 10.415 10.013 40.002 30.004 10.000 10.005 30.000 10.000 10.444 10.514 10.000 10.028 10.000 10.156 10.267 10.000 11.000 10.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.permissive0.130 30.246 30.083 30.043 40.547 40.236 30.415 30.672 10.141 40.133 20.067 30.000 20.521 20.114 40.238 40.289 20.232 30.883 10.182 40.373 40.486 10.076 30.488 30.022 30.529 30.199 40.110 30.217 30.100 10.460 40.319 30.000 20.025 40.472 10.000 20.394 20.210 30.537 30.004 30.000 20.083 40.000 40.299 30.061 40.201 40.761 30.084 40.008 20.720 20.557 40.000 20.317 40.280 20.094 40.020 40.564 40.000 30.000 20.400 20.048 30.259 30.101 31.000 10.000 10.190 20.142 40.094 40.137 30.089 30.000 20.101 10.355 40.000 20.000 20.000 10.000 20.000 30.444 20.082 40.384 20.000 40.000 20.334 40.004 40.000 20.000 10.000 20.041 30.000 10.000 20.026 40.000 20.000 20.000 30.000 20.082 40.022 20.000 40.021 30.088 30.000 10.241 40.033 40.000 20.000 10.067 20.000 40.000 10.000 20.000 20.000 10.000 30.026 30.262 20.016 30.000 30.278 10.000 10.500 30.394 10.028 40.000 10.000 20.000 20.000 20.000 10.000 20.019 30.000 20.000 20.000 20.000 20.000 10.156 40.000 10.032 40.000 10.000 20.194 40.000 10.248 40.000 10.000 10.099 30.019 30.308 10.000 10.000 20.000 10.000 20.000 10.007 30.122 20.000 20.175 20.063 20.000 30.271 10.000 40.000 20.000 10.000 40.000 10.000 10.000 20.278 20.000 10.000 20.000 10.111 20.000 20.000 10.000 20.000 1
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 30.564 30.152 40.394 40.578 40.235 20.116 40.034 40.000 20.348 40.119 30.297 20.285 30.202 40.838 30.323 30.407 30.184 40.037 40.516 10.013 40.424 40.214 20.093 40.105 40.078 40.542 30.250 40.000 20.064 30.444 20.000 20.224 40.231 20.537 30.001 40.000 20.126 30.004 20.308 20.193 20.244 30.343 40.228 20.000 40.441 30.588 20.000 20.338 30.275 30.189 30.030 30.600 30.000 30.000 20.378 30.000 40.108 40.098 41.000 10.000 10.096 40.172 30.144 20.011 40.125 20.000 20.000 40.376 30.000 20.000 20.000 10.000 20.000 30.042 40.141 30.377 30.051 10.000 20.483 20.017 30.000 20.000 10.000 20.022 40.000 10.000 20.065 20.000 20.000 20.000 30.000 20.094 30.000 40.042 20.000 40.064 40.000 10.259 30.089 30.000 20.000 10.000 30.022 30.000 10.000 20.000 20.000 10.000 30.018 40.111 40.000 40.000 30.278 10.000 10.444 40.333 30.333 30.000 10.000 20.000 20.000 20.000 10.000 20.000 40.000 20.000 20.000 20.000 20.000 10.267 30.000 10.184 30.000 10.000 20.211 30.000 10.378 20.000 10.000 10.063 40.000 40.275 20.000 10.000 20.000 10.000 20.000 10.007 40.105 30.000 20.032 40.045 30.198 20.171 30.028 10.000 20.000 10.006 20.000 10.000 10.000 20.278 20.000 10.000 20.000 10.044 30.000 20.000 10.000 20.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.154 20.275 20.108 20.060 20.573 20.381 20.434 20.654 30.190 30.141 10.097 20.000 20.503 30.180 20.252 30.242 40.242 20.881 20.448 10.494 20.429 20.078 20.364 40.024 20.654 20.213 30.222 20.239 20.099 20.616 20.363 20.000 20.092 20.444 20.000 20.383 30.209 40.815 10.030 20.000 20.166 20.002 30.295 40.099 30.364 10.778 10.177 30.001 30.427 40.585 30.000 20.470 20.268 40.205 20.045 20.642 20.007 20.000 20.333 40.148 20.407 20.130 21.000 10.000 10.156 30.189 20.097 30.169 20.000 40.000 20.056 20.400 20.000 20.000 20.000 10.000 20.556 10.278 30.203 20.323 40.019 30.000 20.402 30.026 20.000 20.000 10.000 20.044 20.000 10.000 20.037 30.000 20.000 20.181 20.000 20.127 20.006 30.028 30.023 20.115 10.000 10.327 20.267 20.000 20.000 10.000 30.028 20.000 10.000 20.000 20.000 10.003 20.048 20.135 30.222 20.089 10.278 10.000 10.514 20.333 30.611 20.000 10.000 20.000 20.000 20.000 10.000 20.037 20.000 20.000 20.000 20.000 20.000 10.322 20.000 10.209 20.000 10.000 20.278 20.000 10.302 30.000 10.000 10.143 20.148 20.000 40.000 10.000 20.000 10.000 20.000 10.015 20.064 40.000 20.272 10.031 40.000 30.257 20.028 10.000 20.000 10.041 10.000 10.000 10.000 20.222 40.000 10.000 20.000 10.000 40.000 20.000 10.000 20.000 1
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 30.831 180.685 70.714 10.979 10.594 30.310 160.801 10.892 80.841 20.819 30.723 30.940 70.887 10.725 12
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
CU-Hybrid Net0.764 20.924 20.819 60.840 110.757 60.853 90.580 10.848 130.709 20.643 110.958 90.587 70.295 220.753 140.884 120.758 90.815 50.725 20.927 150.867 90.743 5
OccuSeg+Semantic0.764 20.758 440.796 180.839 120.746 110.907 10.562 50.850 120.680 90.672 50.978 20.610 10.335 80.777 40.819 310.847 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 40.217 560.762 100.888 90.758 90.813 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 50.793 210.802 270.689 260.825 290.556 60.867 80.681 80.602 280.960 70.555 160.365 30.779 30.859 170.747 120.795 170.717 40.917 180.856 170.764 2
PointTransformerV20.752 50.742 510.809 120.872 10.758 50.860 60.552 70.891 50.610 280.687 20.960 70.559 140.304 190.766 80.926 20.767 60.797 130.644 200.942 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
BPNetcopyleft0.749 70.909 40.818 80.811 210.752 80.839 170.485 300.842 150.673 100.644 100.957 120.528 230.305 180.773 60.859 170.788 40.818 40.693 70.916 190.856 170.723 13
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointConvFormer0.749 70.793 300.790 220.807 240.750 100.856 80.524 150.881 60.588 380.642 130.977 40.591 50.274 320.781 20.929 10.804 30.796 140.642 210.947 30.885 20.715 17
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
MSP0.748 90.623 750.804 140.859 30.745 120.824 310.501 220.912 20.690 60.685 30.956 130.567 110.320 130.768 70.918 30.720 200.802 90.676 100.921 160.881 40.779 1
StratifiedFormerpermissive0.747 100.901 60.803 150.845 80.757 60.846 130.512 180.825 200.696 50.645 90.956 130.576 90.262 420.744 180.861 160.742 130.770 300.705 50.899 300.860 140.734 6
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 110.771 380.819 60.848 60.702 240.865 50.397 670.899 30.699 30.664 60.948 380.588 60.330 90.746 170.851 230.764 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 40.729 160.850 110.501 220.874 70.587 390.658 70.956 130.564 120.299 200.765 90.900 50.716 230.812 70.631 260.939 80.858 150.709 18
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Retro-FPN0.744 130.842 180.800 160.767 390.740 130.836 210.541 90.914 10.672 110.626 170.958 90.552 170.272 340.777 40.886 110.696 300.801 100.674 120.941 60.858 150.717 15
EQ-Net0.743 140.620 760.799 170.849 50.730 150.822 330.493 280.897 40.664 120.681 40.955 170.562 130.378 10.760 110.903 40.738 140.801 100.673 130.907 230.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 150.816 250.806 130.807 240.752 80.828 270.575 30.839 170.699 30.637 150.954 210.520 250.320 130.755 130.834 270.760 80.772 270.676 100.915 200.862 120.717 15
SAT0.742 150.860 130.765 330.819 160.769 30.848 120.533 110.829 190.663 130.631 160.955 170.586 80.274 320.753 140.896 60.729 150.760 350.666 150.921 160.855 190.733 7
MinkowskiNetpermissive0.736 170.859 140.818 80.832 130.709 210.840 160.521 170.853 110.660 150.643 110.951 290.544 180.286 270.731 190.893 70.675 370.772 270.683 90.874 480.852 210.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 180.890 70.837 30.864 20.726 170.873 20.530 140.824 210.489 690.647 80.978 20.609 20.336 70.624 350.733 440.758 90.776 250.570 510.949 20.877 50.728 8
SparseConvNet0.725 190.647 720.821 50.846 70.721 190.869 30.533 110.754 400.603 340.614 210.955 170.572 100.325 110.710 200.870 130.724 180.823 20.628 270.934 110.865 110.683 24
PointTransformer++0.725 190.727 580.811 110.819 160.765 40.841 150.502 210.814 260.621 240.623 180.955 170.556 150.284 280.620 360.866 140.781 50.757 380.648 180.932 130.862 120.709 18
MatchingNet0.724 210.812 270.812 100.810 220.735 140.834 220.495 270.860 100.572 450.602 280.954 210.512 270.280 290.757 120.845 250.725 170.780 230.606 370.937 90.851 220.700 21
INS-Conv-semantic0.717 220.751 470.759 360.812 200.704 230.868 40.537 100.842 150.609 300.608 240.953 240.534 190.293 230.616 370.864 150.719 220.793 180.640 220.933 120.845 260.663 29
PointMetaBase0.714 230.835 190.785 240.821 140.684 280.846 130.531 130.865 90.614 250.596 310.953 240.500 300.246 480.674 210.888 90.692 310.764 320.624 280.849 620.844 270.675 26
contrastBoundarypermissive0.705 240.769 410.775 290.809 230.687 270.820 360.439 540.812 270.661 140.591 340.945 470.515 260.171 740.633 320.856 190.720 200.796 140.668 140.889 370.847 240.689 23
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 250.889 80.745 450.813 190.672 300.818 400.493 280.815 240.623 220.610 220.947 400.470 390.249 470.594 400.848 240.705 270.779 240.646 190.892 350.823 340.611 44
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 Click0.701 260.825 230.796 180.723 460.716 200.832 230.433 560.816 220.634 200.609 230.969 60.418 640.344 50.559 510.833 280.715 240.808 80.560 550.902 270.847 240.680 25
JSENetpermissive0.699 270.881 100.762 340.821 140.667 310.800 520.522 160.792 320.613 260.607 250.935 660.492 320.205 610.576 450.853 210.691 320.758 370.652 170.872 510.828 310.649 33
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-IIpermissive0.696 280.704 620.790 220.787 310.709 210.837 190.459 390.815 240.543 540.615 200.956 130.529 210.250 450.551 560.790 360.703 280.799 120.619 320.908 220.848 230.700 21
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 290.743 500.794 200.655 690.684 280.822 330.497 260.719 500.622 230.617 190.977 40.447 510.339 60.750 160.664 590.703 280.790 200.596 410.946 40.855 190.647 34
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 300.884 90.754 400.795 300.647 360.818 400.422 580.802 300.612 270.604 260.945 470.462 420.189 690.563 500.853 210.726 160.765 310.632 250.904 250.821 370.606 48
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 310.704 620.741 490.754 430.656 320.829 250.501 220.741 450.609 300.548 410.950 330.522 240.371 20.633 320.756 390.715 240.771 290.623 290.861 580.814 390.658 30
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 320.866 120.748 420.819 160.645 380.794 550.450 440.802 300.587 390.604 260.945 470.464 410.201 640.554 530.840 260.723 190.732 470.602 390.907 230.822 360.603 51
KP-FCNN0.684 330.847 170.758 380.784 330.647 360.814 430.473 320.772 350.605 320.594 330.935 660.450 490.181 720.587 410.805 340.690 330.785 220.614 330.882 410.819 380.632 39
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 330.728 570.757 390.776 350.690 250.804 500.464 370.816 220.577 440.587 350.945 470.508 290.276 310.671 220.710 490.663 420.750 410.589 460.881 420.832 300.653 32
Superpoint Network0.683 350.851 160.728 530.800 290.653 340.806 480.468 340.804 280.572 450.602 280.946 440.453 480.239 510.519 620.822 290.689 350.762 340.595 430.895 330.827 320.630 40
PointContrast_LA_SEM0.683 350.757 450.784 250.786 320.639 400.824 310.408 610.775 340.604 330.541 430.934 700.532 200.269 380.552 540.777 370.645 520.793 180.640 220.913 210.824 330.671 27
VI-PointConv0.676 370.770 400.754 400.783 340.621 440.814 430.552 70.758 380.571 470.557 390.954 210.529 210.268 400.530 600.682 540.675 370.719 500.603 380.888 380.833 290.665 28
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 380.789 310.748 420.763 410.635 420.814 430.407 630.747 420.581 430.573 360.950 330.484 330.271 360.607 380.754 400.649 470.774 260.596 410.883 400.823 340.606 48
SALANet0.670 390.816 250.770 310.768 380.652 350.807 470.451 410.747 420.659 160.545 420.924 760.473 380.149 840.571 470.811 330.635 550.746 420.623 290.892 350.794 510.570 61
PointASNLpermissive0.666 400.703 640.781 270.751 450.655 330.830 240.471 330.769 360.474 720.537 450.951 290.475 370.279 300.635 300.698 530.675 370.751 400.553 600.816 690.806 430.703 20
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 400.781 330.759 360.699 540.644 390.822 330.475 310.779 330.564 500.504 590.953 240.428 580.203 630.586 430.754 400.661 430.753 390.588 470.902 270.813 410.642 35
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 420.746 480.708 560.722 470.638 410.820 360.451 410.566 760.599 360.541 430.950 330.510 280.313 150.648 270.819 310.616 600.682 650.590 450.869 540.810 420.656 31
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 430.778 340.702 590.806 260.619 450.813 460.468 340.693 580.494 650.524 510.941 580.449 500.298 210.510 640.821 300.675 370.727 490.568 530.826 670.803 450.637 37
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 440.698 650.743 470.650 700.564 620.820 360.505 200.758 380.631 210.479 630.945 470.480 350.226 520.572 460.774 380.690 330.735 450.614 330.853 610.776 650.597 54
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 450.752 460.734 510.664 670.583 570.815 420.399 660.754 400.639 180.535 470.942 560.470 390.309 170.665 230.539 660.650 460.708 550.635 240.857 600.793 530.642 35
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 460.778 340.731 520.699 540.577 580.829 250.446 460.736 460.477 710.523 530.945 470.454 460.269 380.484 710.749 430.618 580.738 430.599 400.827 660.792 560.621 42
MVPNetpermissive0.641 470.831 200.715 540.671 640.590 530.781 610.394 680.679 600.642 170.553 400.937 630.462 420.256 430.649 260.406 790.626 560.691 620.666 150.877 440.792 560.608 47
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 470.776 360.703 580.721 480.557 650.826 280.451 410.672 620.563 510.483 620.943 550.425 610.162 790.644 280.726 450.659 440.709 540.572 500.875 460.786 600.559 66
PointMRNet0.640 490.717 610.701 600.692 570.576 590.801 510.467 360.716 510.563 510.459 680.953 240.429 570.169 760.581 440.854 200.605 610.710 520.550 610.894 340.793 530.575 59
FPConvpermissive0.639 500.785 320.760 350.713 520.603 480.798 530.392 690.534 810.603 340.524 510.948 380.457 440.250 450.538 580.723 470.598 650.696 600.614 330.872 510.799 460.567 63
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 510.797 290.769 320.641 750.590 530.820 360.461 380.537 800.637 190.536 460.947 400.388 710.206 600.656 240.668 570.647 500.732 470.585 480.868 550.793 530.473 84
PointSPNet0.637 520.734 540.692 670.714 510.576 590.797 540.446 460.743 440.598 370.437 730.942 560.403 670.150 830.626 340.800 350.649 470.697 590.557 580.846 630.777 640.563 64
SConv0.636 530.830 210.697 630.752 440.572 610.780 630.445 480.716 510.529 570.530 480.951 290.446 520.170 750.507 660.666 580.636 540.682 650.541 660.886 390.799 460.594 55
Supervoxel-CNN0.635 540.656 700.711 550.719 490.613 460.757 720.444 510.765 370.534 560.566 370.928 740.478 360.272 340.636 290.531 680.664 410.645 750.508 730.864 570.792 560.611 44
joint point-basedpermissive0.634 550.614 770.778 280.667 660.633 430.825 290.420 590.804 280.467 740.561 380.951 290.494 310.291 240.566 480.458 740.579 710.764 320.559 570.838 640.814 390.598 53
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 560.731 550.688 700.675 610.591 520.784 600.444 510.565 770.610 280.492 600.949 360.456 450.254 440.587 410.706 500.599 640.665 710.612 360.868 550.791 590.579 58
PointNet2-SFPN0.631 570.771 380.692 670.672 620.524 690.837 190.440 530.706 560.538 550.446 700.944 530.421 630.219 550.552 540.751 420.591 670.737 440.543 650.901 290.768 670.557 67
APCF-Net0.631 570.742 510.687 720.672 620.557 650.792 580.408 610.665 630.545 530.508 560.952 280.428 580.186 700.634 310.702 510.620 570.706 560.555 590.873 490.798 480.581 57
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
3DSM_DMMF0.631 570.626 740.745 450.801 280.607 470.751 730.506 190.729 490.565 490.491 610.866 900.434 530.197 670.595 390.630 610.709 260.705 570.560 550.875 460.740 750.491 79
FusionAwareConv0.630 600.604 790.741 490.766 400.590 530.747 740.501 220.734 470.503 640.527 490.919 800.454 460.323 120.550 570.420 780.678 360.688 630.544 630.896 320.795 500.627 41
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 610.800 280.625 820.719 490.545 670.806 480.445 480.597 710.448 780.519 540.938 620.481 340.328 100.489 700.499 730.657 450.759 360.592 440.881 420.797 490.634 38
SegGroup_sempermissive0.627 620.818 240.747 440.701 530.602 490.764 690.385 730.629 680.490 670.508 560.931 730.409 660.201 640.564 490.725 460.618 580.692 610.539 670.873 490.794 510.548 70
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 630.830 210.694 650.757 420.563 630.772 670.448 450.647 660.520 590.509 550.949 360.431 560.191 680.496 680.614 620.647 500.672 690.535 690.876 450.783 610.571 60
HPEIN0.618 640.729 560.668 730.647 720.597 510.766 680.414 600.680 590.520 590.525 500.946 440.432 540.215 570.493 690.599 630.638 530.617 800.570 510.897 310.806 430.605 50
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-GCNpermissive0.610 650.858 150.772 300.489 870.532 680.792 580.404 650.643 670.570 480.507 580.935 660.414 650.046 930.510 640.702 510.602 630.705 570.549 620.859 590.773 660.534 73
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 660.760 430.667 740.649 710.521 700.793 560.457 400.648 650.528 580.434 750.947 400.401 680.153 820.454 730.721 480.648 490.717 510.536 680.904 250.765 680.485 80
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 670.634 730.743 470.697 560.601 500.781 610.437 550.585 740.493 660.446 700.933 710.394 690.011 950.654 250.661 600.603 620.733 460.526 700.832 650.761 700.480 81
LAP-D0.594 680.720 590.692 670.637 760.456 790.773 660.391 710.730 480.587 390.445 720.940 600.381 720.288 250.434 760.453 760.591 670.649 730.581 490.777 730.749 740.610 46
DPC0.592 690.720 590.700 610.602 800.480 750.762 710.380 740.713 540.585 420.437 730.940 600.369 740.288 250.434 760.509 720.590 690.639 780.567 540.772 740.755 720.592 56
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 700.766 420.659 770.683 590.470 780.740 760.387 720.620 700.490 670.476 640.922 780.355 770.245 490.511 630.511 710.571 720.643 760.493 770.872 510.762 690.600 52
ROSMRF0.580 710.772 370.707 570.681 600.563 630.764 690.362 760.515 820.465 750.465 670.936 650.427 600.207 590.438 740.577 640.536 750.675 680.486 780.723 800.779 620.524 75
SD-DETR0.576 720.746 480.609 860.445 910.517 710.643 870.366 750.714 530.456 760.468 660.870 890.432 540.264 410.558 520.674 550.586 700.688 630.482 790.739 780.733 770.537 72
SQN_0.1%0.569 730.676 670.696 640.657 680.497 720.779 640.424 570.548 780.515 610.376 800.902 870.422 620.357 40.379 800.456 750.596 660.659 720.544 630.685 830.665 880.556 68
TextureNetpermissive0.566 740.672 690.664 750.671 640.494 730.719 770.445 480.678 610.411 840.396 780.935 660.356 760.225 530.412 780.535 670.565 730.636 790.464 810.794 720.680 850.568 62
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
DVVNet0.562 750.648 710.700 610.770 370.586 560.687 810.333 800.650 640.514 620.475 650.906 840.359 750.223 540.340 820.442 770.422 860.668 700.501 740.708 810.779 620.534 73
Pointnet++ & Featurepermissive0.557 760.735 530.661 760.686 580.491 740.744 750.392 690.539 790.451 770.375 810.946 440.376 730.205 610.403 790.356 820.553 740.643 760.497 750.824 680.756 710.515 76
GMLPs0.538 770.495 870.693 660.647 720.471 770.793 560.300 830.477 830.505 630.358 820.903 860.327 800.081 900.472 720.529 690.448 840.710 520.509 710.746 760.737 760.554 69
PanopticFusion-label0.529 780.491 880.688 700.604 790.386 840.632 880.225 930.705 570.434 810.293 880.815 910.348 780.241 500.499 670.669 560.507 770.649 730.442 870.796 710.602 910.561 65
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_weak0.516 790.676 670.591 890.609 770.442 800.774 650.335 790.597 710.422 830.357 830.932 720.341 790.094 890.298 840.528 700.473 820.676 670.495 760.602 890.721 800.349 91
Online SegFusion0.515 800.607 780.644 800.579 820.434 810.630 890.353 770.628 690.440 790.410 760.762 940.307 820.167 770.520 610.403 800.516 760.565 830.447 850.678 840.701 820.514 77
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, FTSDF0.501 810.558 830.608 870.424 930.478 760.690 800.246 890.586 730.468 730.450 690.911 820.394 690.160 800.438 740.212 890.432 850.541 880.475 800.742 770.727 780.477 82
PCNN0.498 820.559 820.644 800.560 840.420 830.711 790.229 910.414 840.436 800.352 840.941 580.324 810.155 810.238 890.387 810.493 780.529 890.509 710.813 700.751 730.504 78
3DMV0.484 830.484 890.538 910.643 740.424 820.606 920.310 810.574 750.433 820.378 790.796 920.301 830.214 580.537 590.208 900.472 830.507 920.413 900.693 820.602 910.539 71
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 840.577 810.611 850.356 950.321 920.715 780.299 850.376 880.328 910.319 860.944 530.285 850.164 780.216 920.229 870.484 800.545 870.456 830.755 750.709 810.475 83
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 850.679 660.604 880.578 830.380 850.682 820.291 860.106 940.483 700.258 930.920 790.258 890.025 940.231 910.325 830.480 810.560 850.463 820.725 790.666 870.231 95
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 860.474 900.623 830.463 890.366 870.651 850.310 810.389 870.349 890.330 850.937 630.271 870.126 860.285 850.224 880.350 910.577 820.445 860.625 870.723 790.394 87
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
SurfaceConvPF0.442 870.505 860.622 840.380 940.342 900.654 840.227 920.397 860.367 870.276 900.924 760.240 900.198 660.359 810.262 850.366 880.581 810.435 880.640 860.668 860.398 86
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 870.548 840.548 900.597 810.363 880.628 900.300 830.292 890.374 860.307 870.881 880.268 880.186 700.238 890.204 910.407 870.506 930.449 840.667 850.620 900.462 85
Tangent Convolutionspermissive0.438 890.437 920.646 790.474 880.369 860.645 860.353 770.258 910.282 930.279 890.918 810.298 840.147 850.283 860.294 840.487 790.562 840.427 890.619 880.633 890.352 90
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 900.525 850.647 780.522 850.324 910.488 950.077 960.712 550.353 880.401 770.636 960.281 860.176 730.340 820.565 650.175 950.551 860.398 910.370 950.602 910.361 89
SimConv0.410 910.000 960.782 260.772 360.722 180.838 180.407 630.000 970.000 970.595 320.947 400.000 970.270 370.000 970.000 970.000 970.786 210.621 310.000 970.841 280.621 42
SPLAT Netcopyleft0.393 920.472 910.511 920.606 780.311 930.656 830.245 900.405 850.328 910.197 940.927 750.227 920.000 970.001 960.249 860.271 940.510 900.383 930.593 900.699 830.267 93
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+FTSDF0.383 930.297 940.491 930.432 920.358 890.612 910.274 870.116 930.411 840.265 910.904 850.229 910.079 910.250 870.185 920.320 920.510 900.385 920.548 910.597 940.394 87
PointNet++permissive0.339 940.584 800.478 940.458 900.256 950.360 960.250 880.247 920.278 940.261 920.677 950.183 930.117 870.212 930.145 940.364 890.346 960.232 960.548 910.523 950.252 94
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 950.353 930.290 960.278 960.166 960.553 930.169 950.286 900.147 950.148 960.908 830.182 940.064 920.023 950.018 960.354 900.363 940.345 940.546 930.685 840.278 92
ScanNetpermissive0.306 960.203 950.366 950.501 860.311 930.524 940.211 940.002 960.342 900.189 950.786 930.145 950.102 880.245 880.152 930.318 930.348 950.300 950.460 940.437 960.182 96
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 970.000 960.041 970.172 970.030 970.062 970.001 970.035 950.004 960.051 970.143 970.019 960.003 960.041 940.050 950.003 960.054 970.018 970.005 960.264 970.082 97


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