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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CeCo0.340 10.551 10.247 10.181 10.784 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 ap 25%head ap 25%common ap 25%tail ap 25%chairtabledoorcouchcabinetshelfdeskoffice 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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mask3D Scannet2000.445 10.653 10.392 10.254 10.844 10.746 10.818 10.888 30.556 10.262 10.890 10.025 11.000 10.608 10.930 10.694 30.721 10.930 40.686 20.966 10.615 30.440 10.725 30.201 10.890 20.414 30.827 10.552 10.158 40.806 10.924 10.042 20.512 10.412 40.226 10.604 20.830 11.000 10.125 10.792 10.815 10.097 10.648 10.551 10.354 31.000 10.630 10.241 21.000 10.853 10.204 10.974 30.841 10.778 10.358 10.927 10.300 10.045 10.640 10.363 10.745 10.710 11.000 10.000 10.330 10.943 10.315 10.600 11.000 10.027 10.080 40.556 40.500 10.409 10.000 10.194 11.000 10.500 10.493 20.761 20.053 30.042 20.780 10.454 10.009 10.333 10.050 10.321 10.000 10.084 10.552 10.008 10.027 10.750 10.500 10.442 20.657 10.765 10.120 20.183 20.021 21.000 10.510 20.016 10.000 10.400 10.619 10.000 10.396 10.290 10.000 10.741 10.699 11.000 10.260 10.017 20.125 40.000 10.792 30.399 31.000 10.000 10.049 20.265 10.063 20.000 21.000 10.335 20.381 10.500 10.250 10.004 10.000 10.727 20.000 10.538 30.000 10.188 10.677 20.000 10.930 10.000 10.000 10.966 10.391 10.908 10.000 10.028 10.000 11.000 10.000 10.152 10.451 20.458 10.971 10.573 10.606 10.167 40.625 10.004 10.000 10.058 40.000 10.000 11.000 11.000 10.000 10.056 10.000 10.200 20.309 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.permissive0.280 30.488 30.192 40.124 30.804 30.518 30.772 40.904 20.337 40.191 30.443 30.000 20.861 30.502 30.868 30.669 40.587 30.997 20.467 40.828 40.732 10.342 30.745 20.119 40.918 10.404 40.419 30.398 20.172 20.618 40.743 30.167 10.077 40.500 10.000 20.568 30.506 41.000 10.044 30.000 20.502 30.010 30.593 30.284 40.305 40.903 40.213 40.142 30.981 20.790 40.000 31.000 10.715 40.538 40.346 30.830 40.067 20.000 20.400 20.074 30.333 30.551 21.000 10.000 10.292 20.777 30.118 40.317 30.100 30.000 20.191 20.648 20.000 20.000 20.000 10.000 20.000 30.500 10.213 40.825 10.021 40.333 10.648 40.098 30.000 20.000 20.000 20.077 20.000 10.000 40.150 40.000 20.000 20.000 40.225 20.281 30.447 30.000 40.090 30.148 30.000 30.479 40.542 10.000 20.000 10.200 20.131 40.000 10.250 20.000 30.000 10.159 40.396 40.677 20.021 30.000 30.500 10.000 11.000 10.442 20.125 40.000 10.000 30.000 20.000 30.333 10.000 20.528 10.000 20.000 20.000 20.000 20.000 10.200 40.000 10.516 40.000 10.000 20.500 30.000 10.833 20.000 10.000 10.286 30.083 30.750 20.000 10.000 20.000 10.000 20.000 10.059 40.445 30.200 20.535 30.070 20.167 30.385 30.375 20.000 20.000 10.333 30.000 10.000 10.000 20.500 20.000 10.000 20.000 10.200 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.permissive0.275 40.466 40.218 30.110 40.783 40.383 40.783 30.829 40.367 30.168 40.305 40.000 20.661 40.413 40.869 20.719 10.546 40.997 20.685 30.841 30.555 40.277 40.768 10.132 20.779 40.448 20.364 40.212 40.161 30.768 20.692 40.000 30.395 20.500 10.000 20.450 40.591 21.000 10.020 40.000 20.423 40.007 40.625 20.420 20.505 21.000 10.353 20.119 40.571 30.819 20.014 21.000 10.774 20.689 30.311 40.866 20.067 20.000 20.400 20.000 40.278 40.501 31.000 10.000 10.162 40.584 40.286 20.206 40.125 20.000 20.084 30.649 10.000 20.000 20.000 10.000 20.000 30.125 40.312 30.727 30.221 10.000 30.667 30.114 20.000 20.000 20.000 20.065 40.000 10.004 30.278 20.000 20.000 20.500 20.000 40.571 10.000 40.250 30.019 40.145 40.000 30.667 20.200 40.000 20.000 10.200 20.258 30.000 10.000 30.000 30.000 10.369 30.429 30.613 30.000 40.000 30.500 10.000 10.500 40.333 40.500 30.000 10.106 10.000 20.000 30.000 20.000 20.333 30.000 20.000 20.000 20.000 20.000 10.918 10.000 10.638 10.000 10.000 20.750 10.000 10.833 20.000 10.000 10.143 40.000 40.750 20.000 10.000 20.000 10.000 20.000 10.063 30.377 40.200 20.222 40.055 30.500 20.677 20.250 30.000 20.000 10.500 20.000 10.000 10.000 20.500 20.000 10.000 20.000 10.115 40.000 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.permissive0.314 20.529 20.225 20.155 20.810 20.625 20.798 20.940 10.372 20.217 20.484 20.000 20.927 20.528 20.826 40.694 20.605 21.000 10.731 10.846 20.716 20.350 20.589 40.123 30.857 30.457 10.578 20.376 30.183 10.765 30.800 20.000 30.278 30.500 10.000 20.659 10.569 31.000 10.093 20.000 20.539 20.010 20.578 40.378 30.571 11.000 10.337 30.252 10.530 40.814 30.000 30.744 40.743 30.746 20.346 20.863 30.067 20.000 20.400 20.167 20.667 20.488 41.000 10.000 10.208 30.783 20.166 30.375 20.071 40.000 20.200 10.607 30.000 20.000 20.000 10.000 21.000 10.500 10.517 10.716 40.221 10.000 30.706 20.085 40.000 20.000 20.000 20.077 30.000 10.063 20.278 20.000 20.000 20.500 20.083 30.181 40.515 20.286 20.144 10.219 10.042 10.582 30.400 30.000 20.000 10.000 40.305 20.000 10.000 30.036 20.000 10.413 20.500 20.533 40.250 20.200 10.500 10.000 11.000 10.472 11.000 10.000 10.000 30.000 20.250 10.000 20.000 20.333 30.000 20.000 20.000 20.000 20.000 10.600 30.000 10.594 20.000 10.000 20.500 30.000 10.647 40.000 10.000 10.429 20.333 20.500 40.000 10.000 20.000 10.000 20.000 10.069 20.696 10.050 40.556 20.031 40.042 40.750 10.250 30.000 20.000 10.630 10.000 10.000 10.000 20.500 20.000 10.000 20.000 10.400 10.000 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 30.831 170.685 60.714 10.979 10.594 30.310 150.801 10.892 80.841 20.819 30.723 30.940 70.887 10.725 12
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
CU-Hybrid Net0.764 20.924 20.819 60.840 110.757 60.853 90.580 10.848 130.709 20.643 110.958 90.587 70.295 210.753 130.884 120.758 80.815 50.725 20.927 150.867 90.743 5
OccuSeg+Semantic0.764 20.758 430.796 170.839 120.746 100.907 10.562 40.850 120.680 80.672 50.978 20.610 10.335 80.777 40.819 300.847 10.830 10.691 80.972 10.885 20.727 10
O-CNNpermissive0.762 40.924 20.823 40.844 90.770 20.852 100.577 20.847 140.711 10.640 140.958 90.592 40.217 550.762 100.888 90.758 80.813 60.726 10.932 130.868 80.744 4
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
DMF-Net0.752 50.906 50.793 200.802 260.689 250.825 280.556 50.867 80.681 70.602 270.960 70.555 160.365 30.779 30.859 170.747 110.795 170.717 40.917 180.856 160.764 2
PointTransformerV20.752 50.742 500.809 120.872 10.758 50.860 60.552 60.891 50.610 270.687 20.960 70.559 140.304 180.766 80.926 20.767 60.797 130.644 190.942 50.876 70.722 14
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
PointConvFormer0.749 70.793 290.790 210.807 240.750 90.856 80.524 140.881 60.588 370.642 130.977 40.591 50.274 310.781 20.929 10.804 30.796 140.642 200.947 30.885 20.715 16
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 70.909 40.818 80.811 210.752 80.839 170.485 290.842 150.673 90.644 100.957 120.528 230.305 170.773 60.859 170.788 40.818 40.693 70.916 190.856 160.723 13
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 90.623 740.804 130.859 30.745 110.824 300.501 210.912 20.690 50.685 30.956 130.567 110.320 130.768 70.918 30.720 190.802 90.676 100.921 160.881 40.779 1
StratifiedFormerpermissive0.747 100.901 60.803 140.845 80.757 60.846 130.512 170.825 190.696 40.645 90.956 130.576 90.262 410.744 170.861 160.742 120.770 290.705 50.899 290.860 130.734 6
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 110.771 370.819 60.848 60.702 230.865 50.397 660.899 30.699 30.664 60.948 370.588 60.330 90.746 160.851 230.764 70.796 140.704 60.935 100.866 100.728 8
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 110.870 110.838 20.858 40.729 150.850 110.501 210.874 70.587 380.658 70.956 130.564 120.299 190.765 90.900 50.716 220.812 70.631 250.939 80.858 140.709 17
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Retro-FPN0.744 130.842 180.800 150.767 380.740 120.836 210.541 80.914 10.672 100.626 160.958 90.552 170.272 330.777 40.886 110.696 290.801 100.674 110.941 60.858 140.717 15
EQ-Net0.743 140.620 750.799 160.849 50.730 140.822 320.493 270.897 40.664 110.681 40.955 170.562 130.378 10.760 110.903 40.738 130.801 100.673 120.907 220.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 150.860 130.765 320.819 160.769 30.848 120.533 100.829 180.663 120.631 150.955 170.586 80.274 310.753 130.896 60.729 140.760 340.666 140.921 160.855 180.733 7
MinkowskiNetpermissive0.736 160.859 140.818 80.832 130.709 200.840 160.521 160.853 110.660 140.643 110.951 280.544 180.286 260.731 180.893 70.675 360.772 270.683 90.874 470.852 200.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 170.890 70.837 30.864 20.726 160.873 20.530 130.824 200.489 680.647 80.978 20.609 20.336 70.624 340.733 430.758 80.776 250.570 500.949 20.877 50.728 8
SparseConvNet0.725 180.647 710.821 50.846 70.721 180.869 30.533 100.754 390.603 330.614 200.955 170.572 100.325 110.710 190.870 130.724 170.823 20.628 260.934 110.865 110.683 23
PointTransformer++0.725 180.727 570.811 110.819 160.765 40.841 150.502 200.814 250.621 230.623 170.955 170.556 150.284 270.620 350.866 140.781 50.757 370.648 170.932 130.862 120.709 17
MatchingNet0.724 200.812 260.812 100.810 220.735 130.834 220.495 260.860 100.572 440.602 270.954 210.512 260.280 280.757 120.845 250.725 160.780 230.606 360.937 90.851 210.700 20
INS-Conv-semantic0.717 210.751 460.759 350.812 200.704 220.868 40.537 90.842 150.609 290.608 230.953 230.534 190.293 220.616 360.864 150.719 210.793 180.640 210.933 120.845 250.663 28
PointMetaBase0.714 220.835 190.785 230.821 140.684 270.846 130.531 120.865 90.614 240.596 300.953 230.500 290.246 470.674 200.888 90.692 300.764 310.624 270.849 610.844 260.675 25
contrastBoundarypermissive0.705 230.769 400.775 280.809 230.687 260.820 350.439 530.812 260.661 130.591 330.945 460.515 250.171 730.633 310.856 190.720 190.796 140.668 130.889 360.847 230.689 22
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 240.889 80.745 440.813 190.672 290.818 390.493 270.815 230.623 210.610 210.947 390.470 380.249 460.594 390.848 240.705 260.779 240.646 180.892 340.823 330.611 43
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click0.701 250.825 230.796 170.723 450.716 190.832 230.433 550.816 210.634 190.609 220.969 60.418 630.344 50.559 500.833 270.715 230.808 80.560 540.902 260.847 230.680 24
JSENetpermissive0.699 260.881 100.762 330.821 140.667 300.800 510.522 150.792 310.613 250.607 240.935 650.492 310.205 600.576 440.853 210.691 310.758 360.652 160.872 500.828 300.649 32
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
PicassoNet-IIpermissive0.696 270.704 610.790 210.787 300.709 200.837 190.459 380.815 230.543 530.615 190.956 130.529 210.250 440.551 550.790 350.703 270.799 120.619 310.908 210.848 220.700 20
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 280.743 490.794 190.655 680.684 270.822 320.497 250.719 490.622 220.617 180.977 40.447 500.339 60.750 150.664 580.703 270.790 200.596 400.946 40.855 180.647 33
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 290.884 90.754 390.795 290.647 350.818 390.422 570.802 290.612 260.604 250.945 460.462 410.189 680.563 490.853 210.726 150.765 300.632 240.904 240.821 360.606 47
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 300.704 610.741 480.754 420.656 310.829 250.501 210.741 440.609 290.548 400.950 320.522 240.371 20.633 310.756 380.715 230.771 280.623 280.861 570.814 380.658 29
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 310.866 120.748 410.819 160.645 370.794 540.450 430.802 290.587 380.604 250.945 460.464 400.201 630.554 520.840 260.723 180.732 460.602 380.907 220.822 350.603 50
KP-FCNN0.684 320.847 170.758 370.784 320.647 350.814 420.473 310.772 340.605 310.594 320.935 650.450 480.181 710.587 400.805 330.690 320.785 220.614 320.882 400.819 370.632 38
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 320.728 560.757 380.776 340.690 240.804 490.464 360.816 210.577 430.587 340.945 460.508 280.276 300.671 210.710 480.663 410.750 400.589 450.881 410.832 290.653 31
Superpoint Network0.683 340.851 160.728 520.800 280.653 330.806 470.468 330.804 270.572 440.602 270.946 430.453 470.239 500.519 610.822 280.689 340.762 330.595 420.895 320.827 310.630 39
PointContrast_LA_SEM0.683 340.757 440.784 240.786 310.639 390.824 300.408 600.775 330.604 320.541 420.934 690.532 200.269 370.552 530.777 360.645 510.793 180.640 210.913 200.824 320.671 26
VI-PointConv0.676 360.770 390.754 390.783 330.621 430.814 420.552 60.758 370.571 460.557 380.954 210.529 210.268 390.530 590.682 530.675 360.719 490.603 370.888 370.833 280.665 27
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 370.789 300.748 410.763 400.635 410.814 420.407 620.747 410.581 420.573 350.950 320.484 320.271 350.607 370.754 390.649 460.774 260.596 400.883 390.823 330.606 47
SALANet0.670 380.816 250.770 300.768 370.652 340.807 460.451 400.747 410.659 150.545 410.924 750.473 370.149 830.571 460.811 320.635 540.746 410.623 280.892 340.794 500.570 60
PointConvpermissive0.666 390.781 320.759 350.699 530.644 380.822 320.475 300.779 320.564 490.504 580.953 230.428 570.203 620.586 420.754 390.661 420.753 380.588 460.902 260.813 400.642 34
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 390.703 630.781 260.751 440.655 320.830 240.471 320.769 350.474 710.537 440.951 280.475 360.279 290.635 290.698 520.675 360.751 390.553 590.816 680.806 420.703 19
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 410.746 470.708 550.722 460.638 400.820 350.451 400.566 750.599 350.541 420.950 320.510 270.313 140.648 260.819 300.616 590.682 640.590 440.869 530.810 410.656 30
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 420.778 330.702 580.806 250.619 440.813 450.468 330.693 570.494 640.524 500.941 570.449 490.298 200.510 630.821 290.675 360.727 480.568 520.826 660.803 440.637 36
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 430.698 640.743 460.650 690.564 610.820 350.505 190.758 370.631 200.479 620.945 460.480 340.226 510.572 450.774 370.690 320.735 440.614 320.853 600.776 640.597 53
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 440.752 450.734 500.664 660.583 560.815 410.399 650.754 390.639 170.535 460.942 550.470 380.309 160.665 220.539 650.650 450.708 540.635 230.857 590.793 520.642 34
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 450.778 330.731 510.699 530.577 570.829 250.446 450.736 450.477 700.523 520.945 460.454 450.269 370.484 700.749 420.618 570.738 420.599 390.827 650.792 550.621 41
PointConv-SFPN0.641 460.776 350.703 570.721 470.557 640.826 270.451 400.672 610.563 500.483 610.943 540.425 600.162 780.644 270.726 440.659 430.709 530.572 490.875 450.786 590.559 65
MVPNetpermissive0.641 460.831 200.715 530.671 630.590 520.781 600.394 670.679 590.642 160.553 390.937 620.462 410.256 420.649 250.406 780.626 550.691 610.666 140.877 430.792 550.608 46
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 480.717 600.701 590.692 560.576 580.801 500.467 350.716 500.563 500.459 670.953 230.429 560.169 750.581 430.854 200.605 600.710 510.550 600.894 330.793 520.575 58
FPConvpermissive0.639 490.785 310.760 340.713 510.603 470.798 520.392 680.534 800.603 330.524 500.948 370.457 430.250 440.538 570.723 460.598 640.696 590.614 320.872 500.799 450.567 62
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 500.797 280.769 310.641 740.590 520.820 350.461 370.537 790.637 180.536 450.947 390.388 700.206 590.656 230.668 560.647 490.732 460.585 470.868 540.793 520.473 83
PointSPNet0.637 510.734 530.692 660.714 500.576 580.797 530.446 450.743 430.598 360.437 720.942 550.403 660.150 820.626 330.800 340.649 460.697 580.557 570.846 620.777 630.563 63
SConv0.636 520.830 210.697 620.752 430.572 600.780 620.445 470.716 500.529 560.530 470.951 280.446 510.170 740.507 650.666 570.636 530.682 640.541 650.886 380.799 450.594 54
Supervoxel-CNN0.635 530.656 690.711 540.719 480.613 450.757 710.444 500.765 360.534 550.566 360.928 730.478 350.272 330.636 280.531 670.664 400.645 740.508 720.864 560.792 550.611 43
joint point-basedpermissive0.634 540.614 760.778 270.667 650.633 420.825 280.420 580.804 270.467 730.561 370.951 280.494 300.291 230.566 470.458 730.579 700.764 310.559 560.838 630.814 380.598 52
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 550.731 540.688 690.675 600.591 510.784 590.444 500.565 760.610 270.492 590.949 350.456 440.254 430.587 400.706 490.599 630.665 700.612 350.868 540.791 580.579 57
3DSM_DMMF0.631 560.626 730.745 440.801 270.607 460.751 720.506 180.729 480.565 480.491 600.866 890.434 520.197 660.595 380.630 600.709 250.705 560.560 540.875 450.740 740.491 78
APCF-Net0.631 560.742 500.687 710.672 610.557 640.792 570.408 600.665 620.545 520.508 550.952 270.428 570.186 690.634 300.702 500.620 560.706 550.555 580.873 480.798 470.581 56
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN0.631 560.771 370.692 660.672 610.524 680.837 190.440 520.706 550.538 540.446 690.944 520.421 620.219 540.552 530.751 410.591 660.737 430.543 640.901 280.768 660.557 66
FusionAwareConv0.630 590.604 780.741 480.766 390.590 520.747 730.501 210.734 460.503 630.527 480.919 790.454 450.323 120.550 560.420 770.678 350.688 620.544 620.896 310.795 490.627 40
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 600.800 270.625 810.719 480.545 660.806 470.445 470.597 700.448 770.519 530.938 610.481 330.328 100.489 690.499 720.657 440.759 350.592 430.881 410.797 480.634 37
SegGroup_sempermissive0.627 610.818 240.747 430.701 520.602 480.764 680.385 720.629 670.490 660.508 550.931 720.409 650.201 630.564 480.725 450.618 570.692 600.539 660.873 480.794 500.548 69
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 620.830 210.694 640.757 410.563 620.772 660.448 440.647 650.520 580.509 540.949 350.431 550.191 670.496 670.614 610.647 490.672 680.535 680.876 440.783 600.571 59
HPEIN0.618 630.729 550.668 720.647 710.597 500.766 670.414 590.680 580.520 580.525 490.946 430.432 530.215 560.493 680.599 620.638 520.617 790.570 500.897 300.806 420.605 49
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCNpermissive0.610 640.858 150.772 290.489 860.532 670.792 570.404 640.643 660.570 470.507 570.935 650.414 640.046 920.510 630.702 500.602 620.705 560.549 610.859 580.773 650.534 72
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 650.760 420.667 730.649 700.521 690.793 550.457 390.648 640.528 570.434 740.947 390.401 670.153 810.454 720.721 470.648 480.717 500.536 670.904 240.765 670.485 79
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 660.634 720.743 460.697 550.601 490.781 600.437 540.585 730.493 650.446 690.933 700.394 680.011 940.654 240.661 590.603 610.733 450.526 690.832 640.761 690.480 80
LAP-D0.594 670.720 580.692 660.637 750.456 780.773 650.391 700.730 470.587 380.445 710.940 590.381 710.288 240.434 750.453 750.591 660.649 720.581 480.777 720.749 730.610 45
DPC0.592 680.720 580.700 600.602 790.480 740.762 700.380 730.713 530.585 410.437 720.940 590.369 730.288 240.434 750.509 710.590 680.639 770.567 530.772 730.755 710.592 55
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 690.766 410.659 760.683 580.470 770.740 750.387 710.620 690.490 660.476 630.922 770.355 760.245 480.511 620.511 700.571 710.643 750.493 760.872 500.762 680.600 51
ROSMRF0.580 700.772 360.707 560.681 590.563 620.764 680.362 750.515 810.465 740.465 660.936 640.427 590.207 580.438 730.577 630.536 740.675 670.486 770.723 790.779 610.524 74
SD-DETR0.576 710.746 470.609 850.445 900.517 700.643 860.366 740.714 520.456 750.468 650.870 880.432 530.264 400.558 510.674 540.586 690.688 620.482 780.739 770.733 760.537 71
SQN_0.1%0.569 720.676 660.696 630.657 670.497 710.779 630.424 560.548 770.515 600.376 790.902 860.422 610.357 40.379 790.456 740.596 650.659 710.544 620.685 820.665 870.556 67
TextureNetpermissive0.566 730.672 680.664 740.671 630.494 720.719 760.445 470.678 600.411 830.396 770.935 650.356 750.225 520.412 770.535 660.565 720.636 780.464 800.794 710.680 840.568 61
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet0.562 740.648 700.700 600.770 360.586 550.687 800.333 790.650 630.514 610.475 640.906 830.359 740.223 530.340 810.442 760.422 850.668 690.501 730.708 800.779 610.534 72
Pointnet++ & Featurepermissive0.557 750.735 520.661 750.686 570.491 730.744 740.392 680.539 780.451 760.375 800.946 430.376 720.205 600.403 780.356 810.553 730.643 750.497 740.824 670.756 700.515 75
GMLPs0.538 760.495 860.693 650.647 710.471 760.793 550.300 820.477 820.505 620.358 810.903 850.327 790.081 890.472 710.529 680.448 830.710 510.509 700.746 750.737 750.554 68
PanopticFusion-label0.529 770.491 870.688 690.604 780.386 830.632 870.225 920.705 560.434 800.293 870.815 900.348 770.241 490.499 660.669 550.507 760.649 720.442 860.796 700.602 900.561 64
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 780.676 660.591 880.609 760.442 790.774 640.335 780.597 700.422 820.357 820.932 710.341 780.094 880.298 830.528 690.473 810.676 660.495 750.602 880.721 790.349 90
Online SegFusion0.515 790.607 770.644 790.579 810.434 800.630 880.353 760.628 680.440 780.410 750.762 930.307 810.167 760.520 600.403 790.516 750.565 820.447 840.678 830.701 810.514 76
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF0.501 800.558 820.608 860.424 920.478 750.690 790.246 880.586 720.468 720.450 680.911 810.394 680.160 790.438 730.212 880.432 840.541 870.475 790.742 760.727 770.477 81
PCNN0.498 810.559 810.644 790.560 830.420 820.711 780.229 900.414 830.436 790.352 830.941 570.324 800.155 800.238 880.387 800.493 770.529 880.509 700.813 690.751 720.504 77
3DMV0.484 820.484 880.538 900.643 730.424 810.606 910.310 800.574 740.433 810.378 780.796 910.301 820.214 570.537 580.208 890.472 820.507 910.413 890.693 810.602 900.539 70
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 830.577 800.611 840.356 940.321 910.715 770.299 840.376 870.328 900.319 850.944 520.285 840.164 770.216 910.229 860.484 790.545 860.456 820.755 740.709 800.475 82
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 840.679 650.604 870.578 820.380 840.682 810.291 850.106 930.483 690.258 920.920 780.258 880.025 930.231 900.325 820.480 800.560 840.463 810.725 780.666 860.231 94
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 850.474 890.623 820.463 880.366 860.651 840.310 800.389 860.349 880.330 840.937 620.271 860.126 850.285 840.224 870.350 900.577 810.445 850.625 860.723 780.394 86
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET20.442 860.548 830.548 890.597 800.363 870.628 890.300 820.292 880.374 850.307 860.881 870.268 870.186 690.238 880.204 900.407 860.506 920.449 830.667 840.620 890.462 84
SurfaceConvPF0.442 860.505 850.622 830.380 930.342 890.654 830.227 910.397 850.367 860.276 890.924 750.240 890.198 650.359 800.262 840.366 870.581 800.435 870.640 850.668 850.398 85
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 880.437 910.646 780.474 870.369 850.645 850.353 760.258 900.282 920.279 880.918 800.298 830.147 840.283 850.294 830.487 780.562 830.427 880.619 870.633 880.352 89
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 890.525 840.647 770.522 840.324 900.488 940.077 950.712 540.353 870.401 760.636 950.281 850.176 720.340 810.565 640.175 940.551 850.398 900.370 940.602 900.361 88
SimConv0.410 900.000 950.782 250.772 350.722 170.838 180.407 620.000 960.000 960.595 310.947 390.000 960.270 360.000 960.000 960.000 960.786 210.621 300.000 960.841 270.621 41
SPLAT Netcopyleft0.393 910.472 900.511 910.606 770.311 920.656 820.245 890.405 840.328 900.197 930.927 740.227 910.000 960.001 950.249 850.271 930.510 890.383 920.593 890.699 820.267 92
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 920.297 930.491 920.432 910.358 880.612 900.274 860.116 920.411 830.265 900.904 840.229 900.079 900.250 860.185 910.320 910.510 890.385 910.548 900.597 930.394 86
PointNet++permissive0.339 930.584 790.478 930.458 890.256 940.360 950.250 870.247 910.278 930.261 910.677 940.183 920.117 860.212 920.145 930.364 880.346 950.232 950.548 900.523 940.252 93
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 940.353 920.290 950.278 950.166 950.553 920.169 940.286 890.147 940.148 950.908 820.182 930.064 910.023 940.018 950.354 890.363 930.345 930.546 920.685 830.278 91
ScanNetpermissive0.306 950.203 940.366 940.501 850.311 920.524 930.211 930.002 950.342 890.189 940.786 920.145 940.102 870.245 870.152 920.318 920.348 940.300 940.460 930.437 950.182 95
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 960.000 950.041 960.172 960.030 960.062 960.001 960.035 940.004 950.051 960.143 960.019 950.003 950.041 930.050 940.003 950.054 960.018 960.005 950.264 960.082 96


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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
SoftGroup++0.874 11.000 10.972 90.947 10.839 40.898 80.556 200.913 20.881 80.756 50.828 20.748 40.821 11.000 10.937 30.937 10.887 11.000 10.821 2
Mask3D0.870 21.000 10.985 60.782 260.818 60.938 30.760 30.749 200.923 30.877 20.760 40.785 10.820 21.000 10.912 60.864 190.878 30.983 340.825 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
SoftGrouppermissive0.865 31.000 10.969 100.860 100.860 10.913 50.558 180.899 30.911 40.760 40.828 10.736 50.802 40.981 260.919 50.875 100.877 41.000 10.820 3
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
SPFormerpermissive0.851 41.000 10.994 20.806 200.774 130.942 20.637 100.849 90.859 110.889 10.720 60.730 60.665 101.000 10.911 100.868 170.873 51.000 10.796 4
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023
IPCA-Inst0.851 41.000 10.968 110.884 70.842 30.862 200.693 70.812 160.888 70.677 170.783 30.698 80.807 31.000 10.911 100.865 180.865 61.000 10.757 7
SphereSeg0.835 61.000 10.963 140.891 50.794 80.954 10.822 20.710 230.961 20.721 90.693 120.530 260.653 111.000 10.867 180.857 220.859 70.991 310.771 5
GraphCut0.832 71.000 10.922 270.724 360.798 70.902 70.701 60.856 70.859 100.715 100.706 70.748 30.640 211.000 10.934 40.862 200.880 21.000 10.729 10
TopoSeg0.832 71.000 10.981 70.933 20.819 50.826 270.524 250.841 100.811 160.681 160.759 50.687 90.727 50.981 260.911 100.883 80.853 81.000 10.756 8
PBNetpermissive0.825 91.000 10.963 130.837 140.843 20.865 150.822 10.647 300.878 90.733 70.639 200.683 100.650 121.000 10.853 190.870 140.820 91.000 10.744 9
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. Arxiv
DKNet0.815 101.000 10.930 200.844 120.765 160.915 40.534 230.805 180.805 180.807 30.654 140.763 20.650 121.000 10.794 310.881 90.766 131.000 10.758 6
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
RPGN0.806 111.000 10.992 40.789 220.723 270.891 90.650 90.810 170.832 130.665 190.699 100.658 110.700 61.000 10.881 150.832 290.774 110.997 250.613 29
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
HAISpermissive0.803 121.000 10.994 20.820 180.759 170.855 210.554 210.882 40.827 150.615 260.676 130.638 140.646 191.000 10.912 60.797 390.767 120.994 300.726 11
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
Box2Mask0.803 121.000 10.962 150.874 80.707 300.887 120.686 80.598 340.961 10.715 110.694 110.469 310.700 61.000 10.912 60.902 30.753 190.997 250.637 23
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask-Group0.792 141.000 10.968 120.812 190.766 150.864 160.460 280.815 150.888 60.598 280.651 170.639 130.600 250.918 300.941 10.896 40.721 241.000 10.723 12
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.791 151.000 10.996 10.829 170.767 140.889 110.600 130.819 140.770 240.594 290.620 230.541 220.700 61.000 10.941 10.889 60.763 151.000 10.526 38
SSTNetpermissive0.789 161.000 10.840 410.888 60.717 280.835 230.717 50.684 280.627 370.724 80.652 160.727 70.600 251.000 10.912 60.822 320.757 181.000 10.691 19
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
GICN0.788 171.000 10.978 80.867 90.781 110.833 240.527 240.824 110.806 170.549 360.596 250.551 190.700 61.000 10.853 190.935 20.733 211.000 10.651 20
DENet0.786 181.000 10.929 210.736 340.750 230.720 420.755 40.934 10.794 190.590 300.561 310.537 230.650 121.000 10.882 130.804 370.789 101.000 10.719 13
SSEC0.781 191.000 10.945 170.763 310.780 120.819 290.601 120.824 110.790 200.638 220.622 220.536 240.600 251.000 10.882 130.790 400.765 141.000 10.698 17
PointGroup0.778 201.000 10.900 310.798 210.715 290.863 170.493 260.706 240.895 50.569 340.701 80.576 170.639 221.000 10.880 160.851 240.719 250.997 250.709 15
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
PE0.776 211.000 10.900 320.860 100.728 260.869 130.400 340.857 60.774 210.568 350.701 90.602 160.646 190.933 290.843 220.890 50.691 320.997 250.709 14
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
DD-UNet+Group0.764 221.000 10.897 340.837 130.753 200.830 260.459 300.824 110.699 310.629 240.653 150.438 340.650 121.000 10.880 160.858 210.690 331.000 10.650 21
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021
INS-Conv-instance0.762 231.000 10.923 240.765 290.785 100.905 60.600 130.655 290.646 360.683 150.647 180.530 250.650 121.000 10.824 240.830 300.693 310.944 380.644 22
Dyco3Dcopyleft0.761 241.000 10.935 180.893 40.752 220.863 180.600 130.588 350.742 270.641 210.633 210.546 210.550 320.857 330.789 330.853 230.762 160.987 320.699 16
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
OccuSeg+instance0.742 251.000 10.923 240.785 230.745 240.867 140.557 190.578 380.729 280.670 180.644 190.488 290.577 311.000 10.794 310.830 300.620 401.000 10.550 34
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RWSeg0.739 261.000 10.899 330.759 320.753 210.823 280.282 380.691 270.658 340.582 330.594 260.547 200.628 231.000 10.795 300.868 160.728 231.000 10.692 18
3D-MPA0.737 271.000 10.933 190.785 230.794 90.831 250.279 400.588 350.695 320.616 250.559 320.556 180.650 121.000 10.809 280.875 110.696 291.000 10.608 31
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.731 281.000 10.992 40.779 280.609 390.746 370.308 370.867 50.601 400.607 270.539 350.519 270.550 321.000 10.824 240.869 150.729 221.000 10.616 27
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
OSIS0.725 291.000 10.885 370.653 420.657 360.801 310.576 170.695 260.828 140.698 130.534 360.457 330.500 390.857 330.831 230.841 270.627 391.000 10.619 26
SSEN0.724 301.000 10.926 220.781 270.661 340.845 220.596 160.529 400.764 260.653 200.489 410.461 320.500 390.859 320.765 340.872 130.761 171.000 10.577 32
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
NeuralBF0.718 311.000 10.945 160.901 30.754 190.817 300.460 280.700 250.772 220.688 140.568 300.000 510.500 390.981 260.606 420.872 120.740 201.000 10.614 28
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023
Sparse R-CNN0.714 321.000 10.926 230.694 370.699 320.890 100.636 110.516 410.693 330.743 60.588 270.369 370.601 240.594 440.800 290.886 70.676 340.986 330.546 35
SALoss-ResNet0.695 331.000 10.855 390.579 460.589 410.735 400.484 270.588 350.856 120.634 230.571 290.298 380.500 391.000 10.824 240.818 330.702 280.935 420.545 36
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
PanopticFusion-inst0.693 341.000 10.852 400.655 410.616 380.788 320.334 360.763 190.771 230.457 460.555 330.652 120.518 360.857 330.765 340.732 460.631 370.944 380.577 33
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Occipital-SCS0.688 351.000 10.913 280.730 350.737 250.743 390.442 310.855 80.655 350.546 370.546 340.263 400.508 380.889 310.568 430.771 430.705 270.889 450.625 25
3D-BoNet0.687 361.000 10.887 360.836 150.587 420.643 490.550 220.620 310.724 290.522 410.501 390.243 410.512 371.000 10.751 360.807 360.661 360.909 440.612 30
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
PCJC0.684 371.000 10.895 350.757 330.659 350.862 190.189 470.739 210.606 390.712 120.581 280.515 280.650 120.857 330.357 480.785 410.631 380.889 450.635 24
SPG_WSIS0.678 381.000 10.880 380.836 150.701 310.727 410.273 420.607 330.706 300.541 390.515 380.174 430.600 250.857 330.716 370.846 260.711 261.000 10.506 39
One_Thing_One_Clickpermissive0.675 391.000 10.823 420.782 250.621 370.766 340.211 440.736 220.560 430.586 310.522 370.636 150.453 430.641 430.853 190.850 250.694 300.997 250.411 43
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
SegGroup_inspermissive0.637 401.000 10.923 260.593 450.561 430.746 380.143 490.504 420.766 250.485 440.442 420.372 360.530 350.714 400.815 270.775 420.673 351.000 10.431 42
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MASCpermissive0.615 410.711 470.802 430.540 470.757 180.777 330.029 500.577 390.588 420.521 420.600 240.436 350.534 340.697 410.616 410.838 280.526 420.980 350.534 37
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
UNet-backbone0.605 421.000 10.909 290.764 300.603 400.704 430.415 330.301 470.548 440.461 450.394 430.267 390.386 450.857 330.649 400.817 340.504 430.959 360.356 46
3D-SISpermissive0.558 431.000 10.773 440.614 440.503 450.691 450.200 450.412 430.498 470.546 380.311 480.103 470.600 250.857 330.382 450.799 380.445 490.938 410.371 44
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
R-PointNet0.544 440.500 500.655 500.661 400.663 330.765 350.432 320.214 490.612 380.584 320.499 400.204 420.286 490.429 470.655 390.650 510.539 410.950 370.499 40
Hier3Dcopyleft0.540 451.000 10.727 450.626 430.467 480.693 440.200 450.412 430.480 480.528 400.318 470.077 500.600 250.688 420.382 450.768 440.472 450.941 400.350 47
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
Region-18class0.497 460.250 520.902 300.689 380.540 440.747 360.276 410.610 320.268 510.489 430.348 440.000 510.243 510.220 500.663 380.814 350.459 470.928 430.496 41
tmp0.474 471.000 10.727 450.433 500.481 470.673 470.022 520.380 450.517 460.436 480.338 460.128 450.343 470.429 470.291 500.728 470.473 440.833 480.300 49
SemRegionNet-20cls0.470 481.000 10.727 450.447 490.481 460.678 460.024 510.380 450.518 450.440 470.339 450.128 450.350 460.429 470.212 510.711 480.465 460.833 480.290 50
ASIS0.422 490.333 510.707 480.676 390.401 490.650 480.350 350.177 500.594 410.376 490.202 490.077 490.404 440.571 450.197 520.674 500.447 480.500 510.260 51
3D-BEVIS0.401 500.667 480.687 490.419 510.137 520.587 500.188 480.235 480.359 500.211 510.093 520.080 480.311 480.571 450.382 450.754 450.300 510.874 470.357 45
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.390 510.556 490.636 510.493 480.353 500.539 510.271 430.160 510.450 490.359 500.178 500.146 440.250 500.143 510.347 490.698 490.436 500.667 500.331 48
MaskRCNN 2d->3d Proj0.261 520.903 460.081 520.008 520.233 510.175 520.280 390.106 520.150 520.203 520.175 510.480 300.218 520.143 510.542 440.404 520.153 520.393 520.049 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