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
OA-CNN-L_ScanNet2000.333 20.558 10.269 10.124 30.821 10.703 10.946 10.569 10.662 10.748 10.487 10.455 10.572 20.000 60.789 10.534 10.736 20.271 10.713 10.949 10.498 50.877 20.860 10.332 10.706 10.474 10.788 20.406 30.637 10.495 10.355 20.805 20.592 50.015 60.396 10.602 30.000 10.799 20.876 10.713 60.276 10.000 50.493 20.080 20.448 40.363 10.661 20.833 10.262 10.125 10.823 20.665 10.076 20.720 10.557 20.637 20.517 20.672 40.227 20.000 30.158 20.496 20.843 30.352 20.835 30.000 10.103 50.711 10.527 10.526 20.320 10.000 10.568 10.625 30.067 10.000 30.000 10.001 20.806 10.836 10.621 10.591 30.373 20.314 20.668 20.398 20.003 20.000 30.000 10.016 60.024 10.043 60.906 10.000 10.052 30.000 50.384 10.330 40.342 50.100 20.223 20.183 20.112 20.476 20.313 20.130 30.196 20.112 20.370 50.000 10.234 30.071 30.160 10.403 20.398 30.492 50.197 10.076 40.272 10.000 10.200 60.560 10.735 20.000 10.000 50.000 30.110 10.002 30.021 20.412 20.000 30.000 20.000 20.000 10.000 10.794 20.000 10.445 10.000 10.022 20.509 20.000 10.517 50.000 10.000 10.001 60.245 20.915 20.024 20.089 10.000 10.262 10.000 10.103 50.524 20.392 20.515 20.013 60.251 30.411 40.662 10.001 30.000 10.473 30.000 10.000 20.150 30.699 10.000 20.000 10.000 10.166 20.000 20.024 10.000 30.000 1
CeCo0.340 10.551 20.247 20.181 10.784 20.661 30.939 20.564 20.624 20.721 20.484 20.429 20.575 10.027 40.774 20.503 30.753 10.242 30.656 20.945 20.534 10.865 30.860 10.177 60.616 30.400 20.818 10.579 10.615 20.367 30.408 10.726 40.633 10.162 10.360 20.619 10.000 10.828 10.873 20.924 10.109 40.083 20.564 10.057 60.475 20.266 20.781 10.767 20.257 20.100 30.825 10.663 20.048 60.620 40.551 30.595 40.532 10.692 30.246 10.000 30.213 10.615 10.861 20.376 10.900 10.000 10.102 60.660 20.321 40.547 10.226 20.000 10.311 20.742 10.011 30.006 20.000 10.000 30.546 60.824 20.345 30.665 10.450 10.435 10.683 10.411 10.338 10.000 30.000 10.030 40.000 20.068 40.892 20.000 10.063 20.000 50.257 20.304 50.387 30.079 40.228 10.190 10.000 60.586 10.347 10.133 20.000 30.037 30.377 40.000 10.384 20.006 50.003 30.421 10.410 20.643 10.171 30.121 20.142 50.000 10.510 40.447 20.474 40.000 10.000 50.286 10.083 20.000 40.000 30.603 10.096 10.063 10.000 20.000 10.000 10.898 10.000 10.429 20.000 10.400 10.550 10.000 10.633 10.000 10.000 10.377 10.000 60.916 10.000 40.000 20.000 10.000 30.000 10.102 60.499 40.296 30.463 30.089 30.304 10.740 10.401 50.010 10.000 10.560 10.000 10.000 20.709 10.652 20.000 20.000 10.000 10.143 30.000 20.000 20.609 10.000 1
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse.
AWCS0.305 30.508 30.225 30.142 20.782 30.634 60.937 30.489 40.578 30.721 20.364 40.355 30.515 30.023 50.764 30.523 20.707 40.264 20.633 30.922 30.507 40.886 10.804 40.179 40.436 60.300 30.656 50.529 20.501 40.394 20.296 50.820 10.603 20.131 20.179 60.619 10.000 10.707 50.865 30.773 20.171 20.010 40.484 30.063 40.463 30.254 30.332 50.649 40.220 40.100 30.729 40.613 40.071 40.582 50.628 10.702 10.424 40.749 10.137 40.000 30.142 30.360 30.863 10.305 30.877 20.000 10.173 10.606 30.337 30.478 30.154 40.000 10.253 30.664 20.000 40.000 30.000 10.000 30.626 40.782 30.302 50.602 20.185 50.282 30.651 30.317 30.000 30.000 30.000 10.022 50.000 20.154 10.876 30.000 10.014 40.063 40.029 60.553 10.467 20.084 30.124 30.157 50.049 50.373 30.252 30.097 40.000 30.219 10.542 10.000 10.392 10.172 20.000 50.339 30.417 10.533 40.093 40.115 30.195 30.000 10.516 30.288 50.741 10.000 10.001 40.233 20.056 30.000 40.159 10.334 30.077 20.000 20.000 20.000 10.000 10.749 30.000 10.411 30.000 10.008 30.452 30.000 10.595 20.000 10.000 10.220 30.006 40.894 40.006 30.000 20.000 10.000 30.000 10.112 30.504 30.404 10.551 10.093 20.129 60.484 30.381 60.000 40.000 10.396 40.000 10.000 20.620 20.402 60.000 20.000 10.000 10.142 40.000 20.000 20.512 20.000 1
LGroundpermissive0.272 40.485 40.184 40.106 40.778 40.676 20.932 40.479 60.572 40.718 40.399 30.265 40.453 50.085 20.745 40.446 40.726 30.232 50.622 40.901 40.512 30.826 40.786 50.178 50.549 40.277 40.659 40.381 40.518 30.295 60.323 30.777 30.599 30.028 40.321 30.363 50.000 10.708 40.858 40.746 40.063 50.022 30.457 40.077 30.476 10.243 40.402 30.397 60.233 30.077 60.720 60.610 50.103 10.629 30.437 60.626 30.446 30.702 20.190 30.005 10.058 50.322 40.702 50.244 40.768 40.000 10.134 40.552 40.279 50.395 40.147 50.000 10.207 40.612 40.000 40.000 30.000 10.000 30.658 30.566 40.323 40.525 60.229 40.179 40.467 60.154 50.000 30.002 10.000 10.051 10.000 20.127 20.703 40.000 10.000 50.216 10.112 50.358 30.547 10.187 10.092 50.156 60.055 40.296 40.252 30.143 10.000 30.014 40.398 30.000 10.028 50.173 10.000 50.265 50.348 40.415 60.179 20.019 50.218 20.000 10.597 20.274 60.565 30.000 10.012 30.000 30.039 50.022 20.000 30.117 40.000 30.000 20.000 20.000 10.000 10.324 50.000 10.384 40.000 10.000 40.251 60.000 10.566 30.000 10.000 10.066 40.404 10.886 50.199 10.000 20.000 10.059 20.000 10.136 10.540 10.127 60.295 40.085 40.143 50.514 20.413 40.000 40.000 10.498 20.000 10.000 20.000 40.623 30.000 20.000 10.000 10.132 50.000 20.000 20.000 30.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 60.455 60.171 50.079 60.766 60.659 40.930 60.494 30.542 60.700 60.314 60.215 60.430 60.121 10.697 60.441 50.683 50.235 40.609 60.895 50.476 60.816 50.770 60.186 30.634 20.216 60.734 30.340 50.471 50.307 50.293 60.591 60.542 60.076 30.205 50.464 40.000 10.484 60.832 60.766 30.052 60.000 50.413 50.059 50.418 50.222 50.318 60.609 50.206 60.112 20.743 30.625 30.076 20.579 60.548 40.590 50.371 50.552 60.081 50.003 20.142 30.201 60.638 60.233 50.686 60.000 10.142 30.444 60.375 20.247 60.198 30.000 10.128 60.454 60.019 20.097 10.000 10.000 30.553 50.557 50.373 20.545 50.164 60.014 60.547 50.174 40.000 30.002 10.000 10.037 20.000 20.063 50.664 60.000 10.000 50.130 20.170 30.152 60.335 60.079 40.110 40.175 30.098 30.175 60.166 50.045 60.207 10.014 40.465 20.000 10.001 60.001 60.046 20.299 40.327 50.537 30.033 50.012 60.186 40.000 10.205 50.377 30.463 50.000 10.058 20.000 30.055 40.041 10.000 30.105 50.000 30.000 20.000 20.000 10.000 10.398 40.000 10.308 60.000 10.000 40.319 40.000 10.543 40.000 10.000 10.062 50.004 50.862 60.000 40.000 20.000 10.000 30.000 10.123 20.316 50.225 40.250 50.094 10.180 40.332 50.441 30.000 40.000 10.310 60.000 10.000 20.000 40.592 40.000 20.000 10.000 10.203 10.000 20.000 20.000 30.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 50.463 50.154 60.102 50.771 50.650 50.932 40.483 50.571 50.710 50.331 50.250 50.492 40.044 30.703 50.419 60.606 60.227 60.621 50.865 60.531 20.771 60.813 30.291 20.484 50.242 50.612 60.282 60.440 60.351 40.299 40.622 50.593 40.027 50.293 40.310 60.000 10.757 30.858 40.737 50.150 30.164 10.368 60.084 10.381 60.142 60.357 40.720 30.214 50.092 50.724 50.596 60.056 50.655 20.525 50.581 60.352 60.594 50.056 60.000 30.014 60.224 50.772 40.205 60.720 50.000 10.159 20.531 50.163 60.294 50.136 60.000 10.169 50.589 50.000 40.000 30.000 10.002 10.663 20.466 60.265 60.582 40.337 30.016 50.559 40.084 60.000 30.000 30.000 10.036 30.000 20.125 30.670 50.000 10.102 10.071 30.164 40.406 20.386 40.046 60.068 60.159 40.117 10.284 50.111 60.094 50.000 30.000 60.197 60.000 10.044 40.013 40.002 40.228 60.307 60.588 20.025 60.545 10.134 60.000 10.655 10.302 40.282 60.000 10.060 10.000 30.035 60.000 40.000 30.097 60.000 30.000 20.005 10.000 10.000 10.096 60.000 10.334 50.000 10.000 40.274 50.000 10.513 60.000 10.000 10.280 20.194 30.897 30.000 40.000 20.000 10.000 30.000 10.108 40.279 60.189 50.141 60.059 50.272 20.307 60.445 20.003 20.000 10.353 50.000 10.026 10.000 40.581 50.001 10.000 10.000 10.093 60.002 10.000 20.000 30.000 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
TD3D Scannet2000.211 20.332 20.177 20.103 20.662 10.413 20.463 20.705 10.192 30.145 10.266 20.215 10.452 40.209 20.222 50.219 50.315 20.893 10.380 20.617 20.439 20.047 40.646 10.080 20.610 30.253 10.237 20.293 20.135 10.379 50.494 20.048 10.252 20.451 20.184 20.483 10.395 20.852 10.083 20.551 20.278 20.036 20.337 20.266 20.544 10.963 10.079 50.039 10.740 20.604 20.000 20.586 10.283 20.282 20.059 20.633 30.028 20.004 20.559 20.309 20.420 20.028 51.000 10.000 10.456 10.411 10.372 10.060 40.046 40.000 20.040 40.694 10.083 20.000 20.000 10.000 20.000 30.083 40.252 20.260 50.200 10.160 10.669 20.111 20.000 20.000 10.006 20.169 20.000 10.007 10.296 20.032 10.074 10.139 30.000 20.321 20.031 10.108 20.088 20.157 10.000 10.231 50.026 50.000 20.000 10.356 20.052 20.000 10.240 10.147 10.000 10.015 20.046 30.144 30.073 30.414 10.222 40.000 10.806 10.343 30.486 30.000 10.008 10.038 20.083 10.002 10.028 20.074 20.032 20.150 20.039 20.008 10.000 10.250 40.000 10.125 40.000 10.052 20.260 30.000 10.143 50.000 10.000 10.543 20.207 20.404 10.000 10.003 20.000 10.000 20.000 10.037 20.093 40.272 20.342 10.039 40.281 20.249 30.224 10.000 20.000 10.074 10.000 10.000 10.000 20.278 20.000 10.000 20.889 10.323 10.000 20.014 10.000 20.000 1
Mask3D Scannet2000.278 10.383 10.263 10.168 10.661 20.465 10.572 10.665 30.391 10.121 40.304 10.015 20.647 10.349 10.474 10.489 10.321 10.816 50.351 30.722 10.402 40.195 10.515 30.082 10.795 10.215 20.396 10.377 10.082 40.724 10.586 10.015 20.277 10.377 50.201 10.475 20.572 10.778 30.089 10.759 10.556 10.068 10.506 10.467 10.323 30.778 20.427 10.027 20.789 10.744 10.003 10.570 20.561 10.337 10.265 10.711 10.258 10.031 10.569 10.311 10.441 10.179 11.000 10.000 10.233 20.411 20.283 20.380 10.667 10.016 10.048 30.418 20.139 10.173 10.000 10.086 10.014 20.500 10.384 10.497 10.044 30.032 20.752 10.287 10.003 10.000 10.007 10.208 10.000 10.001 20.349 10.008 20.014 20.509 10.500 10.323 10.023 20.176 10.107 10.105 30.000 10.605 10.378 10.016 10.000 10.400 10.192 10.000 10.048 20.037 20.000 10.275 10.119 10.810 10.258 10.006 30.083 50.000 10.568 20.377 20.708 10.000 10.005 20.147 10.014 20.000 20.556 10.085 10.325 10.500 10.083 10.004 20.000 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 40.000 10.028 10.000 10.222 10.000 10.063 10.302 10.356 10.149 40.573 10.415 10.013 50.002 40.004 10.000 10.005 40.000 10.000 10.444 10.514 10.000 10.028 10.000 20.156 20.267 10.000 21.000 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 40.246 40.083 40.043 50.547 50.236 40.415 40.672 20.141 50.133 30.067 40.000 30.521 20.114 50.238 40.289 20.232 40.883 20.182 50.373 50.486 10.076 30.488 40.022 40.529 40.199 50.110 40.217 40.100 20.460 40.319 40.000 30.025 50.472 10.000 30.394 30.210 40.537 40.004 40.000 30.083 50.000 50.299 40.061 50.201 50.761 40.084 40.008 30.720 30.557 50.000 20.317 50.280 30.094 50.020 50.564 50.000 40.000 30.400 30.048 40.259 40.101 31.000 10.000 10.190 30.142 50.094 50.137 30.089 30.000 20.101 10.355 50.000 30.000 20.000 10.000 20.000 30.444 20.082 50.384 20.000 50.000 30.334 50.004 50.000 20.000 10.000 30.041 40.000 10.000 30.026 50.000 30.000 30.000 40.000 20.082 50.022 30.000 50.021 40.088 40.000 10.241 40.033 40.000 20.000 10.067 30.000 50.000 10.000 30.000 30.000 10.000 40.026 40.262 20.016 40.000 40.278 10.000 10.500 40.394 10.028 50.000 10.000 30.000 30.000 30.000 20.000 30.019 40.000 30.000 30.000 30.000 30.000 10.156 50.000 10.032 50.000 10.000 30.194 50.000 10.248 40.000 10.000 10.099 40.019 40.308 20.000 10.000 30.000 10.000 20.000 10.007 40.122 20.000 30.175 30.063 20.000 40.271 10.000 50.000 20.000 10.000 50.000 10.000 10.000 20.278 20.000 10.000 20.000 20.111 30.000 20.000 20.000 20.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.123 50.223 50.082 50.046 40.564 40.152 50.394 50.578 50.235 20.116 50.034 50.000 30.348 50.119 40.297 20.285 30.202 50.838 40.323 40.407 40.184 50.037 50.516 20.013 50.424 50.214 30.093 50.105 50.078 50.542 30.250 50.000 30.064 40.444 30.000 30.224 50.231 30.537 40.001 50.000 30.126 40.004 30.308 30.193 30.244 40.343 50.228 20.000 50.441 40.588 30.000 20.338 40.275 40.189 40.030 40.600 40.000 40.000 30.378 40.000 50.108 50.098 41.000 10.000 10.096 50.172 40.144 30.011 50.125 20.000 20.000 50.376 40.000 30.000 20.000 10.000 20.000 30.042 50.141 40.377 30.051 20.000 30.483 30.017 40.000 20.000 10.000 30.022 50.000 10.000 30.065 30.000 30.000 30.000 40.000 20.094 40.000 50.042 30.000 50.064 50.000 10.259 30.089 30.000 20.000 10.000 40.022 40.000 10.000 30.000 30.000 10.000 40.018 50.111 50.000 50.000 40.278 10.000 10.444 50.333 40.333 40.000 10.000 30.000 30.000 30.000 20.000 30.000 50.000 30.000 30.000 30.000 30.000 10.267 30.000 10.184 30.000 10.000 30.211 40.000 10.378 20.000 10.000 10.063 50.000 50.275 30.000 10.000 30.000 10.000 20.000 10.007 50.105 30.000 30.032 50.045 30.198 30.171 40.028 20.000 20.000 10.006 30.000 10.000 10.000 20.278 20.000 10.000 20.000 20.044 40.000 20.000 20.000 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 30.275 30.108 30.060 30.573 30.381 30.434 30.654 40.190 40.141 20.097 30.000 30.503 30.180 30.252 30.242 40.242 30.881 30.448 10.494 30.429 30.078 20.364 50.024 30.654 20.213 40.222 30.239 30.099 30.616 20.363 30.000 30.092 30.444 30.000 30.383 40.209 50.815 20.030 30.000 30.166 30.002 40.295 50.099 40.364 20.778 20.177 30.001 40.427 50.585 40.000 20.470 30.268 50.205 30.045 30.642 20.007 30.000 30.333 50.148 30.407 30.130 21.000 10.000 10.156 40.189 30.097 40.169 20.000 50.000 20.056 20.400 30.000 30.000 20.000 10.000 20.556 10.278 30.203 30.323 40.019 40.000 30.402 40.026 30.000 20.000 10.000 30.044 30.000 10.000 30.037 40.000 30.000 30.181 20.000 20.127 30.006 40.028 40.023 30.115 20.000 10.327 20.267 20.000 20.000 10.000 40.028 30.000 10.000 30.000 30.000 10.003 30.048 20.135 40.222 20.089 20.278 10.000 10.514 30.333 40.611 20.000 10.000 30.000 30.000 30.000 20.000 30.037 30.000 30.000 30.000 30.000 30.000 10.322 20.000 10.209 20.000 10.000 30.278 20.000 10.302 30.000 10.000 10.143 30.148 30.000 50.000 10.000 30.000 10.000 20.000 10.015 30.064 50.000 30.272 20.031 50.000 40.257 20.028 20.000 20.000 10.041 20.000 10.000 10.000 20.222 50.000 10.000 20.000 20.000 50.000 20.000 20.000 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 10.843 110.781 10.858 80.575 30.831 210.685 70.714 10.979 10.594 30.310 180.801 10.892 80.841 20.819 30.723 30.940 80.887 10.725 14
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 90.840 120.757 70.853 100.580 10.848 150.709 20.643 130.958 110.587 70.295 240.753 150.884 120.758 120.815 60.725 20.927 180.867 110.743 6
OccuSeg+Semantic0.764 20.758 470.796 210.839 130.746 120.907 10.562 50.850 140.680 90.672 60.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 12
O-CNNpermissive0.762 40.924 20.823 60.844 100.770 30.852 110.577 20.847 170.711 10.640 170.958 110.592 40.217 590.762 110.888 90.758 120.813 70.726 10.932 160.868 100.744 5
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
OA-CNN-L_ScanNet200.756 50.783 350.826 50.858 40.776 20.837 210.548 90.896 50.649 170.675 50.962 80.586 80.335 80.771 70.802 360.770 80.787 220.691 80.936 110.880 50.761 3
PointTransformerV20.752 60.742 540.809 150.872 10.758 60.860 70.552 70.891 60.610 310.687 20.960 90.559 150.304 210.766 90.926 20.767 90.797 140.644 230.942 60.876 80.722 16
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 60.906 60.793 240.802 300.689 270.825 320.556 60.867 100.681 80.602 310.960 90.555 170.365 30.779 30.859 170.747 150.795 180.717 40.917 210.856 190.764 2
PointConvFormer0.749 80.793 320.790 250.807 250.750 110.856 90.524 170.881 80.588 420.642 160.977 40.591 50.274 350.781 20.929 10.804 30.796 150.642 240.947 30.885 20.715 19
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 80.909 40.818 110.811 220.752 90.839 200.485 340.842 180.673 100.644 120.957 140.528 260.305 200.773 60.859 170.788 40.818 50.693 70.916 220.856 190.723 15
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 100.623 800.804 170.859 30.745 130.824 340.501 250.912 20.690 60.685 30.956 150.567 120.320 140.768 80.918 30.720 230.802 110.676 130.921 190.881 40.779 1
StratifiedFormerpermissive0.747 110.901 70.803 180.845 90.757 70.846 150.512 210.825 240.696 50.645 110.956 150.576 100.262 450.744 190.861 160.742 160.770 310.705 50.899 340.860 160.734 7
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 MVFusion0.746 120.771 410.819 90.848 70.702 250.865 60.397 710.899 30.699 30.664 80.948 420.588 60.330 100.746 180.851 240.764 100.796 150.704 60.935 120.866 120.728 10
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 120.870 120.838 20.858 40.729 180.850 130.501 250.874 90.587 430.658 90.956 150.564 130.299 220.765 100.900 50.716 260.812 80.631 290.939 90.858 170.709 20
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-FPN0.744 140.842 190.800 190.767 420.740 140.836 230.541 110.914 10.672 110.626 200.958 110.552 180.272 370.777 40.886 110.696 320.801 120.674 150.941 70.858 170.717 17
EQ-Net0.743 150.620 810.799 200.849 60.730 170.822 360.493 320.897 40.664 120.681 40.955 180.562 140.378 10.760 120.903 40.738 170.801 120.673 160.907 260.877 60.745 4
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 160.860 140.765 360.819 170.769 40.848 140.533 130.829 220.663 130.631 190.955 180.586 80.274 350.753 150.896 60.729 180.760 380.666 180.921 190.855 210.733 8
LRPNet0.742 160.816 270.806 160.807 250.752 90.828 300.575 30.839 200.699 30.637 180.954 230.520 280.320 140.755 140.834 280.760 110.772 280.676 130.915 230.862 140.717 17
TXC0.740 180.842 190.832 40.805 290.715 220.846 150.473 360.885 70.615 270.671 70.971 60.547 190.320 140.697 230.799 380.777 60.819 30.682 110.946 40.871 90.696 24
LargeKernel3D0.739 190.909 40.820 80.806 270.740 140.852 110.545 100.826 230.594 410.643 130.955 180.541 210.263 440.723 210.858 190.775 70.767 320.678 120.933 140.848 260.694 25
MinkowskiNetpermissive0.736 200.859 150.818 110.832 140.709 230.840 190.521 190.853 130.660 150.643 130.951 320.544 200.286 290.731 200.893 70.675 400.772 280.683 100.874 520.852 240.727 12
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 210.890 80.837 30.864 20.726 190.873 20.530 160.824 250.489 740.647 100.978 20.609 20.336 70.624 380.733 460.758 120.776 260.570 530.949 20.877 60.728 10
SparseConvNet0.725 220.647 770.821 70.846 80.721 200.869 30.533 130.754 440.603 370.614 240.955 180.572 110.325 120.710 220.870 130.724 210.823 20.628 300.934 130.865 130.683 28
PointTransformer++0.725 220.727 620.811 140.819 170.765 50.841 180.502 240.814 300.621 260.623 220.955 180.556 160.284 300.620 390.866 140.781 50.757 410.648 210.932 160.862 140.709 20
MatchingNet0.724 240.812 290.812 130.810 230.735 160.834 250.495 310.860 120.572 490.602 310.954 230.512 300.280 320.757 130.845 260.725 200.780 240.606 390.937 100.851 250.700 23
INS-Conv-semantic0.717 250.751 500.759 390.812 210.704 240.868 40.537 120.842 180.609 330.608 270.953 260.534 230.293 250.616 400.864 150.719 250.793 190.640 250.933 140.845 300.663 33
PointMetaBase0.714 260.835 210.785 260.821 150.684 290.846 150.531 150.865 110.614 280.596 350.953 260.500 330.246 500.674 240.888 90.692 330.764 340.624 310.849 670.844 310.675 30
contrastBoundarypermissive0.705 270.769 440.775 310.809 240.687 280.820 390.439 590.812 310.661 140.591 370.945 500.515 290.171 770.633 350.856 200.720 230.796 150.668 170.889 410.847 270.689 26
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 280.889 90.745 480.813 200.672 320.818 430.493 320.815 290.623 240.610 250.947 440.470 430.249 490.594 430.848 250.705 300.779 250.646 220.892 390.823 370.611 47
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 Click0.701 290.825 250.796 210.723 490.716 210.832 260.433 610.816 270.634 220.609 260.969 70.418 680.344 50.559 550.833 290.715 270.808 100.560 580.902 310.847 270.680 29
JSENetpermissive0.699 300.881 110.762 370.821 150.667 330.800 560.522 180.792 360.613 290.607 280.935 700.492 350.205 640.576 480.853 220.691 340.758 400.652 200.872 550.828 340.649 37
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
One-Thing-One-Click0.693 310.743 530.794 230.655 730.684 290.822 360.497 300.719 540.622 250.617 230.977 40.447 550.339 60.750 170.664 620.703 310.790 210.596 430.946 40.855 210.647 38
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PicassoNet-IIpermissive0.692 320.732 580.772 320.786 340.677 310.866 50.517 200.848 150.509 660.626 200.952 300.536 220.225 550.545 610.704 530.689 370.810 90.564 570.903 300.854 230.729 9
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Feature_GeometricNetpermissive0.690 330.884 100.754 430.795 330.647 390.818 430.422 630.802 340.612 300.604 290.945 500.462 460.189 720.563 540.853 220.726 190.765 330.632 280.904 280.821 400.606 51
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 340.704 670.741 520.754 460.656 350.829 280.501 250.741 490.609 330.548 440.950 360.522 270.371 20.633 350.756 410.715 270.771 300.623 320.861 630.814 420.658 34
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 350.866 130.748 450.819 170.645 410.794 590.450 480.802 340.587 430.604 290.945 500.464 450.201 670.554 570.840 270.723 220.732 500.602 410.907 260.822 390.603 54
VACNN++0.684 360.728 610.757 420.776 390.690 260.804 530.464 420.816 270.577 480.587 380.945 500.508 320.276 340.671 250.710 510.663 450.750 440.589 480.881 460.832 330.653 36
KP-FCNN0.684 360.847 180.758 410.784 360.647 390.814 460.473 360.772 390.605 350.594 360.935 700.450 530.181 750.587 440.805 350.690 350.785 230.614 350.882 450.819 410.632 43
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet0.684 360.712 660.784 270.782 380.658 340.835 240.499 290.823 260.641 190.597 340.950 360.487 360.281 310.575 490.619 650.647 530.764 340.620 340.871 580.846 290.688 27
Superpoint Network0.683 390.851 170.728 560.800 320.653 370.806 510.468 390.804 320.572 490.602 310.946 470.453 520.239 530.519 660.822 300.689 370.762 370.595 450.895 370.827 350.630 44
PointContrast_LA_SEM0.683 390.757 480.784 270.786 340.639 430.824 340.408 660.775 380.604 360.541 460.934 740.532 240.269 400.552 580.777 390.645 560.793 190.640 250.913 240.824 360.671 31
VI-PointConv0.676 410.770 430.754 430.783 370.621 470.814 460.552 70.758 420.571 510.557 420.954 230.529 250.268 420.530 640.682 570.675 400.719 530.603 400.888 420.833 320.665 32
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 420.789 330.748 450.763 440.635 450.814 460.407 680.747 460.581 470.573 390.950 360.484 370.271 390.607 410.754 420.649 500.774 270.596 430.883 440.823 370.606 51
SALANet0.670 430.816 270.770 340.768 410.652 380.807 500.451 450.747 460.659 160.545 450.924 800.473 420.149 870.571 510.811 340.635 590.746 450.623 320.892 390.794 540.570 64
PointConvpermissive0.666 440.781 360.759 390.699 580.644 420.822 360.475 350.779 370.564 540.504 620.953 260.428 620.203 660.586 460.754 420.661 460.753 420.588 490.902 310.813 440.642 39
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 440.703 680.781 290.751 480.655 360.830 270.471 380.769 400.474 770.537 480.951 320.475 410.279 330.635 330.698 560.675 400.751 430.553 630.816 740.806 460.703 22
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++permissive0.663 460.746 510.708 600.722 500.638 440.820 390.451 450.566 810.599 390.541 460.950 360.510 310.313 170.648 300.819 320.616 640.682 690.590 470.869 590.810 450.656 35
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 470.778 370.702 630.806 270.619 480.813 490.468 390.693 620.494 690.524 540.941 610.449 540.298 230.510 680.821 310.675 400.727 520.568 550.826 720.803 480.637 41
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 480.698 690.743 500.650 740.564 650.820 390.505 230.758 420.631 230.479 670.945 500.480 390.226 540.572 500.774 400.690 350.735 480.614 350.853 660.776 690.597 57
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 490.752 490.734 540.664 710.583 600.815 450.399 700.754 440.639 200.535 500.942 590.470 430.309 190.665 260.539 710.650 490.708 580.635 270.857 650.793 560.642 39
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 500.778 370.731 550.699 580.577 610.829 280.446 500.736 500.477 760.523 560.945 500.454 500.269 400.484 750.749 450.618 620.738 460.599 420.827 710.792 590.621 46
MVPNetpermissive0.641 510.831 220.715 580.671 680.590 560.781 650.394 720.679 640.642 180.553 430.937 670.462 460.256 460.649 290.406 840.626 600.691 660.666 180.877 480.792 590.608 50
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 510.776 390.703 620.721 510.557 680.826 310.451 450.672 660.563 550.483 660.943 580.425 650.162 820.644 310.726 470.659 470.709 570.572 520.875 500.786 640.559 69
PointMRNet0.640 530.717 650.701 640.692 610.576 620.801 550.467 410.716 550.563 550.459 720.953 260.429 610.169 790.581 470.854 210.605 650.710 550.550 640.894 380.793 560.575 62
FPConvpermissive0.639 540.785 340.760 380.713 560.603 510.798 570.392 730.534 860.603 370.524 540.948 420.457 480.250 480.538 620.723 490.598 690.696 640.614 350.872 550.799 490.567 66
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 550.797 310.769 350.641 790.590 560.820 390.461 430.537 850.637 210.536 490.947 440.388 760.206 630.656 270.668 600.647 530.732 500.585 500.868 600.793 560.473 88
PointSPNet0.637 560.734 570.692 710.714 550.576 620.797 580.446 500.743 480.598 400.437 770.942 590.403 720.150 860.626 370.800 370.649 500.697 630.557 610.846 680.777 680.563 67
SConv0.636 570.830 230.697 670.752 470.572 640.780 670.445 520.716 550.529 600.530 510.951 320.446 560.170 780.507 700.666 610.636 580.682 690.541 690.886 430.799 490.594 58
Supervoxel-CNN0.635 580.656 750.711 590.719 520.613 490.757 760.444 550.765 410.534 590.566 400.928 780.478 400.272 370.636 320.531 730.664 440.645 790.508 770.864 620.792 590.611 47
joint point-basedpermissive0.634 590.614 820.778 300.667 700.633 460.825 320.420 640.804 320.467 790.561 410.951 320.494 340.291 260.566 520.458 790.579 760.764 340.559 600.838 690.814 420.598 56
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 600.731 590.688 740.675 650.591 550.784 640.444 550.565 820.610 310.492 640.949 400.456 490.254 470.587 440.706 520.599 680.665 750.612 380.868 600.791 630.579 61
3DSM_DMMF0.631 610.626 790.745 480.801 310.607 500.751 770.506 220.729 530.565 530.491 650.866 940.434 570.197 700.595 420.630 640.709 290.705 600.560 580.875 500.740 790.491 83
PointNet2-SFPN0.631 610.771 410.692 710.672 660.524 730.837 210.440 580.706 600.538 580.446 740.944 560.421 670.219 580.552 580.751 440.591 720.737 470.543 680.901 330.768 710.557 70
APCF-Net0.631 610.742 540.687 760.672 660.557 680.792 620.408 660.665 670.545 570.508 590.952 300.428 620.186 730.634 340.702 540.620 610.706 590.555 620.873 530.798 510.581 60
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 640.604 840.741 520.766 430.590 560.747 780.501 250.734 510.503 680.527 520.919 840.454 500.323 130.550 600.420 830.678 390.688 670.544 660.896 360.795 530.627 45
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 650.800 300.625 860.719 520.545 710.806 510.445 520.597 760.448 830.519 570.938 660.481 380.328 110.489 740.499 780.657 480.759 390.592 460.881 460.797 520.634 42
SegGroup_sempermissive0.627 660.818 260.747 470.701 570.602 520.764 730.385 770.629 730.490 720.508 590.931 770.409 700.201 670.564 530.725 480.618 620.692 650.539 700.873 530.794 540.548 73
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 670.830 230.694 690.757 450.563 660.772 710.448 490.647 700.520 620.509 580.949 400.431 600.191 710.496 720.614 660.647 530.672 730.535 720.876 490.783 650.571 63
HPEIN0.618 680.729 600.668 770.647 760.597 540.766 720.414 650.680 630.520 620.525 530.946 470.432 580.215 600.493 730.599 670.638 570.617 840.570 530.897 350.806 460.605 53
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-GCNpermissive0.610 690.858 160.772 320.489 910.532 720.792 620.404 690.643 720.570 520.507 610.935 700.414 690.046 960.510 680.702 540.602 670.705 600.549 650.859 640.773 700.534 76
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 700.760 460.667 780.649 750.521 740.793 600.457 440.648 690.528 610.434 790.947 440.401 730.153 850.454 770.721 500.648 520.717 540.536 710.904 280.765 720.485 84
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 710.634 780.743 500.697 600.601 530.781 650.437 600.585 790.493 700.446 740.933 750.394 740.011 980.654 280.661 630.603 660.733 490.526 730.832 700.761 740.480 85
dtc_net0.596 720.683 700.725 570.715 540.549 700.803 540.444 550.647 700.493 700.495 630.941 610.409 700.000 1000.424 820.544 700.598 690.703 620.522 740.912 250.792 590.520 79
LAP-D0.594 730.720 630.692 710.637 800.456 830.773 700.391 750.730 520.587 430.445 760.940 640.381 770.288 270.434 800.453 810.591 720.649 770.581 510.777 780.749 780.610 49
DPC0.592 740.720 630.700 650.602 840.480 790.762 750.380 780.713 580.585 460.437 770.940 640.369 790.288 270.434 800.509 770.590 740.639 820.567 560.772 790.755 760.592 59
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 750.766 450.659 810.683 630.470 820.740 800.387 760.620 750.490 720.476 680.922 820.355 820.245 510.511 670.511 760.571 770.643 800.493 810.872 550.762 730.600 55
ROSMRF0.580 760.772 400.707 610.681 640.563 660.764 730.362 800.515 870.465 800.465 710.936 690.427 640.207 620.438 780.577 680.536 800.675 720.486 820.723 850.779 660.524 78
SD-DETR0.576 770.746 510.609 900.445 950.517 750.643 910.366 790.714 570.456 810.468 700.870 930.432 580.264 430.558 560.674 580.586 750.688 670.482 830.739 830.733 810.537 75
SQN_0.1%0.569 780.676 720.696 680.657 720.497 760.779 680.424 620.548 830.515 640.376 840.902 910.422 660.357 40.379 850.456 800.596 710.659 760.544 660.685 880.665 920.556 71
TextureNetpermissive0.566 790.672 740.664 790.671 680.494 770.719 810.445 520.678 650.411 890.396 820.935 700.356 810.225 550.412 830.535 720.565 780.636 830.464 850.794 770.680 890.568 65
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
DVVNet0.562 800.648 760.700 650.770 400.586 590.687 850.333 840.650 680.514 650.475 690.906 880.359 800.223 570.340 870.442 820.422 910.668 740.501 780.708 860.779 660.534 76
Pointnet++ & Featurepermissive0.557 810.735 560.661 800.686 620.491 780.744 790.392 730.539 840.451 820.375 850.946 470.376 780.205 640.403 840.356 870.553 790.643 800.497 790.824 730.756 750.515 80
GMLPs0.538 820.495 920.693 700.647 760.471 810.793 600.300 870.477 880.505 670.358 860.903 900.327 850.081 930.472 760.529 740.448 890.710 550.509 750.746 810.737 800.554 72
PanopticFusion-label0.529 830.491 930.688 740.604 830.386 880.632 920.225 970.705 610.434 860.293 920.815 950.348 830.241 520.499 710.669 590.507 820.649 770.442 910.796 760.602 950.561 68
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_weak0.516 840.676 720.591 930.609 810.442 840.774 690.335 830.597 760.422 880.357 870.932 760.341 840.094 920.298 890.528 750.473 870.676 710.495 800.602 940.721 840.349 95
Online SegFusion0.515 850.607 830.644 840.579 860.434 850.630 930.353 810.628 740.440 840.410 800.762 980.307 870.167 800.520 650.403 850.516 810.565 870.447 890.678 890.701 860.514 81
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, FTSDF0.501 860.558 880.608 910.424 970.478 800.690 840.246 930.586 780.468 780.450 730.911 860.394 740.160 830.438 780.212 940.432 900.541 920.475 840.742 820.727 820.477 86
PCNN0.498 870.559 870.644 840.560 880.420 870.711 830.229 950.414 890.436 850.352 880.941 610.324 860.155 840.238 940.387 860.493 830.529 930.509 750.813 750.751 770.504 82
3DMV0.484 880.484 940.538 950.643 780.424 860.606 960.310 850.574 800.433 870.378 830.796 960.301 880.214 610.537 630.208 950.472 880.507 960.413 940.693 870.602 950.539 74
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 890.577 860.611 890.356 990.321 960.715 820.299 890.376 930.328 960.319 900.944 560.285 900.164 810.216 970.229 920.484 850.545 910.456 870.755 800.709 850.475 87
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 900.679 710.604 920.578 870.380 890.682 860.291 900.106 990.483 750.258 970.920 830.258 940.025 970.231 960.325 880.480 860.560 890.463 860.725 840.666 910.231 99
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 910.474 950.623 870.463 930.366 910.651 890.310 850.389 920.349 940.330 890.937 670.271 920.126 890.285 900.224 930.350 960.577 860.445 900.625 920.723 830.394 91
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
SurfaceConvPF0.442 920.505 910.622 880.380 980.342 940.654 880.227 960.397 910.367 920.276 940.924 800.240 950.198 690.359 860.262 900.366 930.581 850.435 920.640 910.668 900.398 90
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 920.548 890.548 940.597 850.363 920.628 940.300 870.292 940.374 910.307 910.881 920.268 930.186 730.238 940.204 960.407 920.506 970.449 880.667 900.620 940.462 89
Tangent Convolutionspermissive0.438 940.437 970.646 830.474 920.369 900.645 900.353 810.258 960.282 980.279 930.918 850.298 890.147 880.283 910.294 890.487 840.562 880.427 930.619 930.633 930.352 94
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 950.525 900.647 820.522 890.324 950.488 990.077 1000.712 590.353 930.401 810.636 1000.281 910.176 760.340 870.565 690.175 1000.551 900.398 950.370 1000.602 950.361 93
SPLAT Netcopyleft0.393 960.472 960.511 960.606 820.311 970.656 870.245 940.405 900.328 960.197 980.927 790.227 970.000 1000.001 1010.249 910.271 990.510 940.383 970.593 950.699 870.267 97
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+FTSDF0.383 970.297 990.491 970.432 960.358 930.612 950.274 910.116 980.411 890.265 950.904 890.229 960.079 940.250 920.185 970.320 970.510 940.385 960.548 960.597 980.394 91
PointNet++permissive0.339 980.584 850.478 980.458 940.256 990.360 1000.250 920.247 970.278 990.261 960.677 990.183 980.117 900.212 980.145 990.364 940.346 1000.232 1000.548 960.523 990.252 98
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 990.353 980.290 1000.278 1000.166 1000.553 970.169 990.286 950.147 1000.148 1000.908 870.182 990.064 950.023 1000.018 1010.354 950.363 980.345 980.546 980.685 880.278 96
ScanNetpermissive0.306 1000.203 1000.366 990.501 900.311 970.524 980.211 980.002 1010.342 950.189 990.786 970.145 1000.102 910.245 930.152 980.318 980.348 990.300 990.460 990.437 1000.182 100
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 1010.000 1010.041 1010.172 1010.030 1010.062 1010.001 1010.035 1000.004 1010.051 1010.143 1010.019 1010.003 990.041 990.050 1000.003 1010.054 1010.018 1010.005 1010.264 1010.082 101


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
Queryformer0.583 10.926 20.702 10.393 210.504 10.733 60.276 20.527 190.373 40.479 10.534 30.533 80.697 30.720 150.436 70.745 20.592 10.958 30.363 7
PBNetpermissive0.573 20.926 20.575 100.619 10.472 20.736 40.239 50.487 250.383 20.459 30.506 60.533 70.585 60.767 70.404 80.717 30.559 50.969 20.381 4
Mask3D0.566 30.926 20.597 50.408 180.420 40.737 30.239 40.598 80.386 10.458 40.549 10.568 50.716 20.601 250.480 30.646 90.575 30.922 50.364 6
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
ISBNetpermissive0.559 40.926 20.597 60.390 220.436 30.722 70.276 30.556 150.380 30.450 50.505 70.583 20.730 10.575 260.455 50.603 150.573 40.979 10.332 13
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023
GraphCut0.552 51.000 10.611 40.438 130.392 70.714 80.139 80.598 90.327 70.389 70.510 50.598 10.427 230.754 100.463 40.761 10.588 20.903 90.329 14
SPFormerpermissive0.549 60.745 140.640 20.484 60.395 60.739 20.311 10.566 130.335 60.468 20.492 80.555 60.478 140.747 120.436 60.712 40.540 60.893 130.343 12
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
DKNet0.532 70.815 90.624 30.517 30.377 90.749 10.107 100.509 220.304 90.437 60.475 90.581 30.539 90.775 60.339 130.640 110.506 90.901 100.385 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
IPCA-Inst0.520 80.889 60.551 140.548 20.418 50.665 180.064 190.585 100.260 170.277 190.471 110.500 90.644 40.785 40.369 90.591 180.511 70.878 180.362 8
SoftGroup++0.513 90.704 200.578 90.398 200.363 140.704 90.061 200.647 40.297 140.378 100.537 20.343 110.614 50.828 30.295 170.710 60.505 110.875 200.394 1
SSTNetpermissive0.506 100.738 170.549 150.497 50.316 190.693 120.178 70.377 330.198 220.330 120.463 130.576 40.515 110.857 20.494 10.637 120.457 150.943 40.290 23
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SoftGrouppermissive0.504 110.667 270.579 70.372 250.381 80.694 110.072 160.677 20.303 100.387 80.531 40.319 150.582 70.754 90.318 140.643 100.492 120.907 80.388 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]
TD3D0.489 120.852 70.511 230.434 140.322 180.735 50.101 130.512 210.355 50.349 110.468 120.283 190.514 120.676 210.268 220.671 70.510 80.908 70.329 15
OccuSeg+instance0.486 130.802 110.536 170.428 160.369 110.702 100.205 60.331 380.301 110.379 90.474 100.327 120.437 190.862 10.485 20.601 160.394 250.846 290.273 25
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
TopoSeg0.479 140.704 200.564 110.467 90.366 120.633 260.068 170.554 160.262 160.328 130.447 150.323 130.534 100.722 140.288 190.614 130.482 130.912 60.358 10
DualGroup0.469 150.815 90.552 130.398 190.374 100.683 140.130 90.539 180.310 80.327 140.407 180.276 200.447 180.535 300.342 120.659 80.455 160.900 120.301 19
SSEC0.465 160.667 270.578 80.502 40.362 150.641 250.035 280.605 60.291 150.323 150.451 140.296 170.417 250.677 200.245 260.501 340.506 100.900 110.366 5
HAISpermissive0.457 170.704 200.561 120.457 100.364 130.673 150.046 270.547 170.194 230.308 160.426 160.288 180.454 170.711 160.262 230.563 250.434 190.889 150.344 11
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
DD-UNet+Group0.436 180.630 340.508 260.480 70.310 200.624 290.065 180.638 50.174 240.256 230.384 210.194 310.428 210.759 80.289 180.574 220.400 230.849 270.291 22
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-instance0.435 190.716 190.495 280.355 270.331 160.689 130.102 120.394 320.208 210.280 170.395 200.250 230.544 80.741 130.309 160.536 310.391 260.842 320.258 29
Mask-Group0.434 200.778 120.516 210.471 80.330 170.658 190.029 300.526 200.249 180.256 220.400 190.309 160.384 290.296 460.368 100.575 210.425 200.877 190.362 9
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
Box2Mask0.433 210.741 150.463 330.433 150.283 220.625 280.103 110.298 420.125 320.260 210.424 170.322 140.472 150.701 180.363 110.711 50.309 400.882 160.272 27
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
RPGN0.428 220.630 340.508 250.367 260.249 290.658 200.016 370.673 30.131 300.234 260.383 220.270 210.434 200.748 110.274 210.609 140.406 220.842 310.267 28
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
DENet0.413 230.741 150.520 190.237 380.284 210.523 360.097 140.691 10.138 270.209 360.229 380.238 250.390 270.707 170.310 150.448 410.470 140.892 140.310 17
PointGroup0.407 240.639 330.496 270.415 170.243 310.645 240.021 350.570 120.114 330.211 340.359 240.217 290.428 220.660 220.256 240.562 260.341 320.860 230.291 21
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-Pretrained0.405 250.738 170.465 320.331 310.205 350.655 210.051 240.601 70.092 360.211 350.329 270.198 300.459 160.775 50.195 330.524 330.400 240.878 170.184 37
PE0.396 260.667 270.467 310.446 120.243 300.624 300.022 340.577 110.106 340.219 290.340 250.239 240.487 130.475 370.225 280.541 300.350 300.818 330.273 26
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
Dyco3Dcopyleft0.395 270.642 320.518 200.447 110.259 280.666 170.050 250.251 460.166 250.231 270.362 230.232 260.331 320.535 290.229 270.587 190.438 180.850 250.317 16
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OSIS0.392 280.778 120.530 180.220 400.278 230.567 330.083 150.330 390.299 120.270 200.310 300.143 360.260 360.624 240.277 200.568 240.361 280.865 220.301 18
AOIA0.387 290.704 200.515 220.385 230.225 340.669 160.005 430.482 260.126 310.181 390.269 350.221 280.426 240.478 360.218 290.592 170.371 270.851 240.242 31
SSEN0.384 300.852 70.494 290.192 410.226 330.648 230.022 330.398 310.299 130.277 180.317 290.231 270.194 430.514 330.196 310.586 200.444 170.843 300.184 36
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
PCJC0.375 310.704 200.542 160.284 350.197 370.649 220.006 410.426 270.138 280.242 240.304 310.183 340.388 280.629 230.141 430.546 290.344 310.738 390.283 24
SphereSeg0.357 320.651 310.411 350.345 280.264 270.630 270.059 210.289 440.212 190.240 250.336 260.158 350.305 330.557 270.159 390.455 400.341 330.726 410.294 20
3D-MPA0.355 330.457 450.484 300.299 330.277 240.591 320.047 260.332 360.212 200.217 300.278 320.193 320.413 260.410 400.195 320.574 230.352 290.849 260.213 34
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
NeuralBF0.353 340.593 360.511 240.375 240.264 260.597 310.008 390.332 370.160 260.229 280.274 340.000 560.206 400.678 190.155 400.485 360.422 210.816 340.254 30
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
RWSeg0.348 350.475 420.456 340.320 320.275 250.476 380.020 360.491 240.056 430.212 330.320 280.261 220.302 340.520 310.182 350.557 270.285 420.867 210.197 35
GICN0.341 360.580 370.371 370.344 290.198 360.469 390.052 230.564 140.093 350.212 320.212 400.127 380.347 310.537 280.206 300.525 320.329 350.729 400.241 32
One_Thing_One_Clickpermissive0.326 370.472 430.361 380.232 390.183 380.555 340.000 490.498 230.038 450.195 370.226 390.362 100.168 440.469 380.251 250.553 280.335 340.846 280.117 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Occipital-SCS0.320 380.679 260.352 390.334 300.229 320.436 400.025 310.412 300.058 410.161 440.240 370.085 400.262 350.496 350.187 340.467 380.328 360.775 350.231 33
Sparse R-CNN0.292 390.704 200.213 490.153 430.154 400.551 350.053 220.212 470.132 290.174 410.274 330.070 420.363 300.441 390.176 360.424 430.234 440.758 370.161 41
MTML0.282 400.577 380.380 360.182 420.107 460.430 410.001 460.422 280.057 420.179 400.162 430.070 430.229 380.511 340.161 370.491 350.313 370.650 460.162 39
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
SALoss-ResNet0.262 410.667 270.335 400.067 500.123 440.427 420.022 320.280 450.058 400.216 310.211 410.039 460.142 460.519 320.106 470.338 470.310 390.721 420.138 42
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)
MASCpermissive0.254 420.463 440.249 480.113 440.167 390.412 440.000 480.374 340.073 370.173 420.243 360.130 370.228 390.368 420.160 380.356 450.208 450.711 430.136 43
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-BoNet0.253 430.519 400.324 430.251 370.137 430.345 490.031 290.419 290.069 380.162 430.131 450.052 440.202 420.338 440.147 420.301 500.303 410.651 450.178 38
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_WSIS0.251 440.380 470.274 460.289 340.144 410.413 430.000 490.311 400.065 390.113 460.130 460.029 480.204 410.388 410.108 460.459 390.311 380.769 360.127 44
SegGroup_inspermissive0.246 450.556 390.335 410.062 520.115 450.490 370.000 490.297 430.018 490.186 380.142 440.083 410.233 370.216 480.153 410.469 370.251 430.744 380.083 48
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
PanopticFusion-inst0.214 460.250 510.330 420.275 360.103 470.228 550.000 490.345 350.024 470.088 480.203 420.186 330.167 450.367 430.125 440.221 530.112 550.666 440.162 40
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-backbone0.161 470.519 400.259 470.084 460.059 490.325 510.002 440.093 520.009 510.077 500.064 490.045 450.044 530.161 500.045 490.331 480.180 470.566 470.033 56
3D-SISpermissive0.161 470.407 460.155 530.068 490.043 530.346 480.001 450.134 490.005 520.088 470.106 480.037 470.135 480.321 450.028 520.339 460.116 540.466 500.093 47
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.158 490.356 480.173 510.113 450.140 420.359 450.012 380.023 540.039 440.134 450.123 470.008 520.089 490.149 510.117 450.221 520.128 520.563 480.094 46
Region-18class0.146 500.175 550.321 440.080 470.062 480.357 460.000 490.307 410.002 530.066 510.044 510.000 560.018 550.036 550.054 480.447 420.133 500.472 490.060 51
SemRegionNet-20cls0.121 510.296 500.203 500.071 480.058 500.349 470.000 490.150 480.019 480.054 520.034 530.017 510.052 510.042 540.013 550.209 540.183 460.371 510.057 52
3D-BEVIS0.117 520.250 510.308 450.020 560.009 570.269 540.006 420.008 550.029 460.037 550.014 560.003 540.036 540.147 520.042 500.381 440.118 530.362 520.069 50
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Hier3Dcopyleft0.117 520.222 530.161 520.054 540.027 540.289 520.000 490.124 500.001 550.079 490.061 500.027 490.141 470.240 470.005 560.310 490.129 510.153 560.081 49
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
tmp0.113 540.333 490.151 540.056 530.053 510.344 500.000 490.105 510.016 500.049 530.035 520.020 500.053 500.048 530.013 540.183 550.173 480.344 530.054 53
ASIS0.085 550.037 560.080 560.066 510.047 520.282 530.000 490.052 530.002 540.047 540.026 540.001 550.046 520.194 490.031 510.264 510.140 490.167 550.047 55
Sgpn_scannet0.049 560.023 570.134 550.031 550.013 560.144 560.006 400.008 560.000 560.028 560.017 550.003 530.009 570.000 560.021 530.122 560.095 560.175 540.054 54
MaskRCNN 2d->3d Proj0.022 570.185 540.000 570.000 570.015 550.000 570.000 470.006 570.000 560.010 570.006 570.107 390.012 560.000 560.002 570.027 570.004 570.022 570.001 57


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 10.422 150.898 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 20.451 110.769 30.656 30.567 30.931 30.395 40.390 40.700 30.534 30.689 90.770 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 20.179 200.648 30.463 30.549 20.742 60.676 20.628 20.961 10.420 20.379 50.684 60.381 150.732 20.723 30.599 20.827 130.851 20.634 6
CMX0.613 40.681 70.725 90.502 120.634 50.297 150.478 90.830 20.651 40.537 60.924 40.375 50.315 120.686 50.451 120.714 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 50.856 180.359 90.306 130.596 110.539 20.627 180.706 40.497 70.785 180.757 160.476 19
MCA-Net0.595 60.533 170.756 60.746 40.590 80.334 70.506 60.670 120.587 70.500 100.905 80.366 80.352 80.601 100.506 60.669 150.648 70.501 60.839 120.769 120.516 18
RFBNet0.592 70.616 90.758 50.659 50.581 90.330 80.469 100.655 150.543 120.524 70.924 40.355 100.336 100.572 140.479 80.671 130.648 70.480 90.814 160.814 50.614 9
FAN_NV_RVC0.586 80.510 180.764 40.079 230.620 70.330 80.494 70.753 40.573 80.556 40.884 130.405 30.303 140.718 20.452 110.672 120.658 50.509 40.898 30.813 60.727 2
DCRedNet0.583 90.682 60.723 100.542 110.510 170.310 120.451 110.668 130.549 110.520 80.920 60.375 50.446 20.528 170.417 130.670 140.577 150.478 100.862 70.806 70.628 8
MIX6D_RVC0.582 100.695 40.687 140.225 180.632 60.328 100.550 10.748 50.623 50.494 130.890 110.350 120.254 200.688 40.454 100.716 30.597 140.489 80.881 50.768 130.575 12
SSMAcopyleft0.577 110.695 40.716 120.439 140.563 110.314 110.444 130.719 80.551 100.503 90.887 120.346 130.348 90.603 90.353 170.709 50.600 120.457 120.901 20.786 80.599 11
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE0.566 120.569 160.686 160.435 150.524 140.294 160.421 160.712 90.543 120.463 150.872 140.320 140.363 70.611 80.477 90.686 100.627 90.443 150.862 70.775 110.639 5
EMSAFormer0.564 130.581 130.736 80.564 100.546 130.219 200.517 40.675 110.486 170.427 190.904 90.352 110.320 110.589 120.528 40.708 60.464 210.413 190.847 110.786 80.611 10
SN_RN152pyrx8_RVCcopyleft0.546 140.572 140.663 180.638 70.518 150.298 140.366 210.633 180.510 150.446 170.864 160.296 170.267 170.542 160.346 180.704 70.575 160.431 160.853 100.766 140.630 7
UDSSEG_RVC0.545 150.610 110.661 190.588 80.556 120.268 180.482 80.642 170.572 90.475 140.836 200.312 150.367 60.630 70.189 200.639 170.495 200.452 130.826 140.756 170.541 14
segfomer with 6d0.542 160.594 120.687 140.146 210.579 100.308 130.515 50.703 100.472 180.498 110.868 150.369 70.282 150.589 120.390 140.701 80.556 170.416 180.860 90.759 150.539 16
FuseNetpermissive0.535 170.570 150.681 170.182 190.512 160.290 170.431 140.659 140.504 160.495 120.903 100.308 160.428 30.523 180.365 160.676 110.621 110.470 110.762 190.779 100.541 14
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 180.613 100.722 110.418 160.358 230.337 60.370 200.479 210.443 190.368 210.907 70.207 200.213 220.464 210.525 50.618 190.657 60.450 140.788 170.721 200.408 22
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 190.481 210.612 200.579 90.456 190.343 50.384 180.623 190.525 140.381 200.845 190.254 190.264 190.557 150.182 210.581 210.598 130.429 170.760 200.661 220.446 21
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 200.505 190.709 130.092 220.427 200.241 190.411 170.654 160.385 230.457 160.861 170.053 230.279 160.503 190.481 70.645 160.626 100.365 210.748 210.725 190.529 17
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 210.490 200.581 210.289 170.507 180.067 230.379 190.610 200.417 210.435 180.822 220.278 180.267 170.503 190.228 190.616 200.533 190.375 200.820 150.729 180.560 13
Enet (reimpl)0.376 220.264 230.452 230.452 130.365 210.181 210.143 230.456 220.409 220.346 220.769 230.164 210.218 210.359 220.123 230.403 230.381 230.313 230.571 220.685 210.472 20
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)permissive0.330 230.293 220.521 220.657 60.361 220.161 220.250 220.004 230.440 200.183 230.836 200.125 220.060 230.319 230.132 220.417 220.412 220.344 220.541 230.427 230.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF0.003 240.000 240.005 240.000 240.000 240.037 240.001 240.000 240.001 240.005 240.003 240.000 240.000 240.000 240.000 240.000 240.002 240.001 240.000 240.006 240.000 24


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