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 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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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
LGroundpermissive0.272 10.485 10.184 10.106 10.778 10.676 10.932 10.479 30.572 10.718 10.399 10.265 10.453 20.085 20.745 10.446 10.726 10.232 20.622 10.901 10.512 20.826 10.786 20.178 30.549 20.277 10.659 20.381 10.518 10.295 30.323 10.777 10.599 10.028 20.321 10.363 20.000 10.708 20.858 10.746 20.063 20.022 20.457 10.077 20.476 10.243 10.402 10.397 30.233 10.077 30.720 30.610 20.103 10.629 20.437 30.626 10.446 10.702 10.190 10.005 10.058 20.322 10.702 20.244 10.768 10.000 10.134 30.552 10.279 20.395 10.147 20.000 10.207 10.612 10.000 20.000 20.000 10.000 20.658 20.566 10.323 20.525 30.229 20.179 10.467 30.154 20.000 10.002 10.000 10.051 10.000 10.127 10.703 10.000 10.000 20.216 10.112 30.358 20.547 10.187 10.092 20.156 30.055 30.296 10.252 10.143 10.000 20.014 10.398 20.000 10.028 20.173 10.000 30.265 20.348 10.415 30.179 10.019 20.218 10.000 10.597 20.274 30.565 10.000 10.012 30.000 10.039 20.022 20.000 10.117 10.000 10.000 10.000 20.000 10.000 10.324 20.000 10.384 10.000 10.000 10.251 30.000 10.566 10.000 10.000 10.066 20.404 10.886 20.199 10.000 10.000 10.059 10.000 10.136 10.540 10.127 30.295 10.085 20.143 30.514 10.413 30.000 20.000 10.498 10.000 10.000 20.000 10.623 10.000 20.000 10.000 10.132 20.000 20.000 10.000 10.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34Dpermissive0.253 20.463 20.154 30.102 20.771 20.650 30.932 10.483 20.571 20.710 20.331 20.250 20.492 10.044 30.703 20.419 30.606 30.227 30.621 20.865 30.531 10.771 30.813 10.291 10.484 30.242 20.612 30.282 30.440 30.351 10.299 20.622 20.593 20.027 30.293 20.310 30.000 10.757 10.858 10.737 30.150 10.164 10.368 30.084 10.381 30.142 30.357 20.720 10.214 20.092 20.724 20.596 30.056 30.655 10.525 20.581 30.352 30.594 20.056 30.000 30.014 30.224 20.772 10.205 30.720 20.000 10.159 10.531 20.163 30.294 20.136 30.000 10.169 20.589 20.000 20.000 20.000 10.002 10.663 10.466 30.265 30.582 10.337 10.016 20.559 10.084 30.000 10.000 30.000 10.036 30.000 10.125 20.670 20.000 10.102 10.071 30.164 20.406 10.386 20.046 30.068 30.159 20.117 10.284 20.111 30.094 20.000 20.000 30.197 30.000 10.044 10.013 20.002 20.228 30.307 30.588 10.025 30.545 10.134 30.000 10.655 10.302 20.282 30.000 10.060 10.000 10.035 30.000 30.000 10.097 30.000 10.000 10.005 10.000 10.000 10.096 30.000 10.334 20.000 10.000 10.274 20.000 10.513 30.000 10.000 10.280 10.194 20.897 10.000 20.000 10.000 10.000 20.000 10.108 30.279 30.189 20.141 30.059 30.272 10.307 30.445 10.003 10.000 10.353 20.000 10.026 10.000 10.581 30.001 10.000 10.000 10.093 30.002 10.000 10.000 10.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrainpermissive0.249 30.455 30.171 20.079 30.766 30.659 20.930 30.494 10.542 30.700 30.314 30.215 30.430 30.121 10.697 30.441 20.683 20.235 10.609 30.895 20.476 30.816 20.770 30.186 20.634 10.216 30.734 10.340 20.471 20.307 20.293 30.591 30.542 30.076 10.205 30.464 10.000 10.484 30.832 30.766 10.052 30.000 30.413 20.059 30.418 20.222 20.318 30.609 20.206 30.112 10.743 10.625 10.076 20.579 30.548 10.590 20.371 20.552 30.081 20.003 20.142 10.201 30.638 30.233 20.686 30.000 10.142 20.444 30.375 10.247 30.198 10.000 10.128 30.454 30.019 10.097 10.000 10.000 20.553 30.557 20.373 10.545 20.164 30.014 30.547 20.174 10.000 10.002 10.000 10.037 20.000 10.063 30.664 30.000 10.000 20.130 20.170 10.152 30.335 30.079 20.110 10.175 10.098 20.175 30.166 20.045 30.207 10.014 10.465 10.000 10.001 30.001 30.046 10.299 10.327 20.537 20.033 20.012 30.186 20.000 10.205 30.377 10.463 20.000 10.058 20.000 10.055 10.041 10.000 10.105 20.000 10.000 10.000 20.000 10.000 10.398 10.000 10.308 30.000 10.000 10.319 10.000 10.543 20.000 10.000 10.062 30.004 30.862 30.000 20.000 10.000 10.000 20.000 10.123 20.316 20.225 10.250 20.094 10.180 20.332 20.441 20.000 20.000 10.310 30.000 10.000 20.000 10.592 20.000 20.000 10.000 10.203 10.000 20.000 10.000 10.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


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




Method Infoavg ap 50%head ap 50%common ap 50%tail ap 50%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 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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 Scannet2000.388 10.542 10.357 10.237 10.808 10.676 10.741 10.832 30.496 10.151 20.628 10.021 10.955 10.578 10.753 10.612 10.591 10.822 40.609 20.926 10.614 20.291 10.725 30.163 10.890 10.380 40.615 10.517 10.130 20.806 10.857 10.024 10.511 10.412 40.226 10.597 10.756 11.000 10.111 10.792 10.736 10.091 10.610 10.527 10.323 31.000 10.504 10.063 11.000 10.853 10.010 10.974 20.839 10.667 10.301 10.883 10.266 10.039 10.640 10.311 10.739 10.463 11.000 10.000 10.287 10.715 10.313 10.600 11.000 10.027 10.076 30.502 40.500 10.409 10.000 10.194 10.125 20.500 10.491 10.748 10.050 30.042 10.776 10.352 10.008 10.000 10.033 10.254 10.000 10.005 10.552 10.008 10.020 10.750 10.500 10.409 10.065 20.511 10.107 10.178 10.000 11.000 10.400 10.016 10.000 10.400 10.571 10.000 10.060 10.044 10.000 10.514 10.278 11.000 10.258 10.017 20.125 40.000 10.792 20.399 21.000 10.000 10.013 10.265 10.018 10.000 11.000 10.335 10.381 10.500 10.250 10.004 10.000 10.727 10.000 10.497 10.000 10.188 10.677 20.000 10.708 20.000 10.000 10.945 10.391 10.123 30.000 10.028 10.000 11.000 10.000 10.099 10.451 10.400 10.668 10.573 10.606 10.077 40.003 30.004 10.000 10.042 20.000 10.000 11.000 11.000 10.000 10.042 10.000 10.200 10.302 10.000 11.000 10.000 1
Minkowski 34D Inst.permissive0.203 40.369 30.134 40.078 40.706 30.382 30.693 20.845 20.221 40.150 30.158 30.000 20.746 20.369 30.545 30.595 20.387 30.997 20.413 40.720 40.636 10.165 30.732 20.070 30.851 30.402 30.251 30.313 30.123 30.583 40.696 20.000 20.051 40.500 10.000 20.500 30.372 40.667 30.009 30.000 20.307 40.003 30.479 30.107 40.226 40.903 30.109 40.031 20.981 20.726 40.000 20.522 40.669 20.282 40.052 40.778 40.000 30.000 20.400 20.074 30.333 30.218 41.000 10.000 10.250 20.406 40.118 40.317 20.100 30.000 20.191 10.596 10.000 20.000 20.000 10.000 20.000 30.500 10.178 40.701 20.000 40.000 20.522 40.018 40.000 20.000 10.000 20.060 30.000 10.000 20.033 40.000 20.000 20.000 30.000 20.281 20.100 10.000 40.090 30.133 30.000 10.422 40.050 40.000 20.000 10.200 20.000 40.000 10.000 20.000 20.000 10.000 30.123 30.677 20.021 30.000 30.500 10.000 10.500 30.442 10.125 40.000 10.000 20.000 20.000 20.000 10.000 20.056 30.000 20.000 20.000 20.000 20.000 10.200 40.000 10.143 40.000 10.000 20.250 40.000 10.511 30.000 10.000 10.286 20.083 30.396 10.000 10.000 20.000 10.000 20.000 10.025 30.300 20.000 20.371 20.070 20.000 30.385 30.000 40.000 20.000 10.000 40.000 10.000 10.000 20.500 20.000 10.000 20.000 10.200 10.000 20.000 10.000 20.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
LGround Inst.permissive0.246 20.413 20.170 20.130 20.754 20.541 20.682 30.903 10.264 30.164 10.234 20.000 20.681 30.452 20.464 40.541 40.399 21.000 10.637 10.772 20.588 30.190 20.589 40.081 20.857 20.426 20.373 20.318 20.135 10.690 20.653 30.000 20.159 30.500 10.000 20.581 20.387 31.000 10.046 20.000 20.402 20.003 40.455 40.196 30.571 11.000 10.270 30.003 40.530 40.748 30.000 20.744 30.575 30.511 20.112 20.815 20.067 20.000 20.400 20.167 20.667 20.241 21.000 10.000 10.208 30.660 20.125 30.317 20.000 40.000 20.100 20.561 30.000 20.000 20.000 10.000 21.000 10.500 10.344 20.568 40.167 20.000 20.706 20.068 20.000 20.000 10.000 20.063 20.000 10.000 20.056 30.000 20.000 20.500 20.000 20.143 40.017 30.125 20.097 20.164 20.000 10.582 30.400 10.000 20.000 10.000 30.083 30.000 10.000 20.000 20.000 10.025 20.156 20.533 30.250 20.200 10.500 10.000 11.000 10.333 31.000 10.000 10.000 20.000 20.000 20.000 10.000 20.333 20.000 20.000 20.000 20.000 20.000 10.400 30.000 10.364 20.000 10.000 20.500 30.000 10.511 30.000 10.000 10.286 20.333 20.000 40.000 10.000 20.000 10.000 20.000 10.034 20.111 40.000 20.333 30.031 40.000 30.750 10.125 10.000 20.000 10.151 10.000 10.000 10.000 20.500 20.000 10.000 20.000 10.000 40.000 20.000 10.000 20.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
CSC-Pretrain Inst.permissive0.209 30.361 40.157 30.085 30.700 40.248 40.634 40.776 40.322 20.135 40.103 40.000 20.524 40.364 40.618 20.592 30.381 40.997 20.589 30.747 30.340 40.109 40.768 10.059 40.702 40.448 10.188 40.149 40.091 40.636 30.573 40.000 20.246 20.500 10.000 20.450 40.405 20.667 30.006 40.000 20.356 30.007 20.506 20.420 20.340 20.667 40.294 20.004 30.571 30.748 20.000 21.000 10.573 40.502 30.094 30.807 30.000 30.000 20.400 20.000 40.278 40.228 31.000 10.000 10.115 40.432 30.198 20.050 40.125 20.000 20.000 40.573 20.000 20.000 20.000 10.000 20.000 30.125 40.312 30.610 30.221 10.000 20.667 30.050 30.000 20.000 10.000 20.032 40.000 10.000 20.083 20.000 20.000 20.000 30.000 20.220 30.000 40.125 20.000 40.111 40.000 10.667 20.200 30.000 20.000 10.000 30.110 20.000 10.000 20.000 20.000 10.000 30.053 40.500 40.000 40.000 30.500 10.000 10.500 30.333 30.500 30.000 10.000 20.000 20.000 20.000 10.000 20.000 40.000 20.000 20.000 20.000 20.000 10.600 20.000 10.364 20.000 10.000 20.750 10.000 10.833 10.000 10.000 10.143 40.000 40.396 10.000 10.000 20.000 10.000 20.000 10.021 40.221 30.000 20.093 40.055 30.451 20.677 20.125 10.000 20.000 10.028 30.000 10.000 10.000 20.500 20.000 10.000 20.000 10.050 30.000 20.000 10.000 20.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
OccuSeg+Semantic0.764 20.758 400.796 150.839 110.746 70.907 10.562 30.850 100.680 60.672 50.978 20.610 10.335 70.777 30.819 260.847 10.830 10.691 60.972 10.885 20.727 7
IPCA0.731 140.890 50.837 30.864 20.726 130.873 20.530 90.824 170.489 650.647 80.978 20.609 20.336 60.624 300.733 390.758 70.776 230.570 450.949 20.877 50.728 5
SparseConvNet0.725 150.647 670.821 50.846 70.721 140.869 30.533 80.754 350.603 280.614 170.955 150.572 80.325 100.710 160.870 100.724 140.823 20.628 220.934 110.865 100.683 19
INS-Conv-semantic0.717 170.751 430.759 300.812 160.704 180.868 40.537 70.842 130.609 240.608 200.953 190.534 150.293 210.616 310.864 110.719 180.793 170.640 170.933 120.845 210.663 23
Virtual MVFusion0.746 90.771 340.819 60.848 60.702 190.865 50.397 620.899 30.699 20.664 60.948 330.588 60.330 80.746 130.851 180.764 60.796 140.704 40.935 100.866 90.728 5
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
PointFormerV20.752 40.742 470.809 100.872 10.758 30.860 60.552 40.891 50.610 220.687 20.960 70.559 120.304 180.766 70.926 20.767 50.797 130.644 150.942 50.876 70.722 11
Mix3Dpermissive0.781 10.964 10.855 10.843 100.781 10.858 70.575 20.831 150.685 50.714 10.979 10.594 30.310 150.801 10.892 70.841 20.819 30.723 20.940 70.887 10.725 9
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
PointConvFormer0.749 50.793 260.790 180.807 200.750 60.856 80.524 100.881 60.588 320.642 120.977 40.591 50.274 290.781 20.929 10.804 30.796 140.642 160.947 30.885 20.715 13
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
O-CNNpermissive0.762 30.924 20.823 40.844 90.770 20.852 90.577 10.847 110.711 10.640 130.958 80.592 40.217 500.762 90.888 80.758 70.813 50.726 10.932 130.868 80.744 3
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
VMNetpermissive0.746 90.870 90.838 20.858 40.729 120.850 100.501 160.874 70.587 330.658 70.956 110.564 100.299 190.765 80.900 50.716 190.812 60.631 210.939 80.858 120.709 14
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)
StratifiedFormerpermissive0.747 80.901 40.803 120.845 80.757 40.846 110.512 130.825 160.696 30.645 90.956 110.576 70.262 370.744 140.861 120.742 90.770 270.705 30.899 250.860 110.734 4
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
MinkowskiNetpermissive0.736 130.859 120.818 70.832 120.709 160.840 120.521 120.853 90.660 110.643 110.951 230.544 140.286 250.731 150.893 60.675 320.772 250.683 70.874 450.852 160.727 7
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
BPNetcopyleft0.749 50.909 30.818 70.811 170.752 50.839 130.485 240.842 130.673 70.644 100.957 100.528 190.305 170.773 50.859 130.788 40.818 40.693 50.916 150.856 140.723 10
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
PointNet2-SFPN0.631 530.771 340.692 620.672 570.524 640.837 140.440 480.706 510.538 500.446 650.944 480.421 590.219 490.552 500.751 370.591 630.737 380.543 590.901 240.768 620.557 62
PicassoNet-IIpermissive0.696 220.704 570.790 180.787 260.709 160.837 140.459 340.815 200.543 490.615 160.956 110.529 170.250 400.551 520.790 310.703 240.799 120.619 250.908 170.848 180.700 16
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
Retro-FPN0.744 110.842 160.800 130.767 340.740 90.836 160.541 60.914 10.672 80.626 140.958 80.552 130.272 300.777 30.886 90.696 260.801 100.674 90.941 60.858 120.717 12
MatchingNet0.724 160.812 230.812 90.810 180.735 100.834 170.495 210.860 80.572 390.602 240.954 170.512 220.280 260.757 110.845 210.725 130.780 210.606 300.937 90.851 170.700 16
One Thing One Click0.701 200.825 200.796 150.723 410.716 150.832 180.433 510.816 180.634 160.609 190.969 60.418 600.344 40.559 470.833 230.715 200.808 70.560 490.902 220.847 190.680 20
PointASNLpermissive0.666 350.703 590.781 220.751 400.655 270.830 190.471 270.769 310.474 680.537 390.951 230.475 320.279 270.635 250.698 480.675 320.751 340.553 540.816 660.806 370.703 15
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
RandLA-Netpermissive0.645 410.778 300.731 460.699 490.577 520.829 200.446 410.736 410.477 670.523 470.945 420.454 420.269 330.484 670.749 380.618 540.738 370.599 330.827 620.792 510.621 37
FusionNet0.688 260.704 570.741 430.754 380.656 260.829 200.501 160.741 400.609 240.548 350.950 270.522 200.371 20.633 270.756 340.715 200.771 260.623 230.861 550.814 330.658 24
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
PointConv-SFPN0.641 420.776 320.703 530.721 430.557 600.826 220.451 360.672 580.563 450.483 570.943 500.425 570.162 730.644 230.726 400.659 390.709 490.572 440.875 430.786 550.559 61
joint point-basedpermissive0.634 500.614 730.778 230.667 610.633 370.825 230.420 540.804 230.467 700.561 320.951 230.494 260.291 220.566 440.458 700.579 670.764 290.559 510.838 600.814 330.598 48
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointContrast_LA_SEM0.683 300.757 410.784 210.786 270.639 340.824 240.408 570.775 290.604 270.541 370.934 660.532 160.269 330.552 500.777 320.645 470.793 170.640 170.913 160.824 270.671 21
MSP0.748 70.623 710.804 110.859 30.745 80.824 240.501 160.912 20.690 40.685 30.956 110.567 90.320 120.768 60.918 30.720 160.802 90.676 80.921 140.881 40.779 1
EQ-Net0.743 120.620 720.799 140.849 50.730 110.822 260.493 220.897 40.664 90.681 40.955 150.562 110.378 10.760 100.903 40.738 100.801 100.673 100.907 180.877 50.745 2
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
One-Thing-One-Click0.693 230.743 460.794 170.655 650.684 220.822 260.497 200.719 450.622 190.617 150.977 40.447 470.339 50.750 120.664 540.703 240.790 190.596 340.946 40.855 150.647 29
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
PointConvpermissive0.666 350.781 290.759 300.699 490.644 330.822 260.475 250.779 280.564 440.504 530.953 190.428 540.203 570.586 380.754 350.661 380.753 330.588 410.902 220.813 350.642 30
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PD-Net0.638 460.797 250.769 270.641 710.590 470.820 290.461 330.537 770.637 150.536 400.947 360.388 670.206 540.656 190.668 520.647 450.732 410.585 420.868 520.793 480.473 79
PPCNN++permissive0.663 370.746 440.708 510.722 420.638 350.820 290.451 360.566 730.599 300.541 370.950 270.510 230.313 140.648 220.819 260.616 560.682 600.590 390.869 510.810 360.656 25
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
contrastBoundarypermissive0.705 180.769 370.775 240.809 190.687 210.820 290.439 490.812 220.661 100.591 270.945 420.515 210.171 680.633 270.856 140.720 160.796 140.668 110.889 330.847 190.689 18
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
HPGCNN0.656 390.698 600.743 410.650 660.564 570.820 290.505 150.758 330.631 170.479 580.945 420.480 300.226 460.572 420.774 330.690 280.735 390.614 260.853 580.776 600.597 49
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
RFCR0.702 190.889 60.745 390.813 150.672 240.818 330.493 220.815 200.623 180.610 180.947 360.470 340.249 420.594 350.848 190.705 230.779 220.646 140.892 310.823 280.611 39
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
Feature_GeometricNetpermissive0.690 250.884 70.754 340.795 250.647 300.818 330.422 530.802 250.612 210.604 220.945 420.462 380.189 630.563 460.853 160.726 120.765 280.632 200.904 200.821 310.606 43
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
SAFNet-segpermissive0.654 400.752 420.734 450.664 620.583 510.815 350.399 610.754 350.639 140.535 410.942 510.470 340.309 160.665 180.539 620.650 410.708 500.635 190.857 570.793 480.642 30
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
ROSMRF3D0.673 330.789 270.748 360.763 360.635 360.814 360.407 590.747 370.581 370.573 300.950 270.484 280.271 320.607 330.754 350.649 420.774 240.596 340.883 360.823 280.606 43
KP-FCNN0.684 280.847 150.758 320.784 280.647 300.814 360.473 260.772 300.605 260.594 260.935 620.450 450.181 660.587 360.805 290.690 280.785 200.614 260.882 370.819 320.632 34
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VI-PointConv0.676 320.770 360.754 340.783 290.621 380.814 360.552 40.758 330.571 410.557 330.954 170.529 170.268 350.530 560.682 490.675 320.719 440.603 310.888 340.833 230.665 22
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
DCM-Net0.658 380.778 300.702 540.806 210.619 390.813 390.468 290.693 530.494 610.524 450.941 530.449 460.298 200.510 600.821 250.675 320.727 430.568 470.826 630.803 390.637 32
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
MCCNNpermissive0.633 510.866 100.731 460.771 310.576 530.809 400.410 560.684 540.497 600.491 550.949 300.466 360.105 830.581 390.646 560.620 520.680 620.542 600.817 650.795 440.618 38
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
SALANet0.670 340.816 220.770 260.768 330.652 290.807 410.451 360.747 370.659 120.545 360.924 720.473 330.149 780.571 430.811 280.635 500.746 360.623 230.892 310.794 460.570 56
Superpoint Network0.683 300.851 140.728 480.800 240.653 280.806 420.468 290.804 230.572 390.602 240.946 390.453 440.239 450.519 580.822 240.689 300.762 300.595 360.895 280.827 260.630 35
DenSeR0.628 570.800 240.625 780.719 440.545 620.806 420.445 430.597 670.448 750.519 480.938 580.481 290.328 90.489 660.499 690.657 400.759 310.592 380.881 390.797 430.634 33
VACNN++0.684 280.728 530.757 330.776 300.690 200.804 440.464 320.816 180.577 380.587 290.945 420.508 240.276 280.671 170.710 440.663 370.750 350.589 400.881 390.832 240.653 26
PointMRNet0.640 440.717 560.701 550.692 520.576 530.801 450.467 310.716 460.563 450.459 630.953 190.429 530.169 700.581 390.854 150.605 570.710 470.550 550.894 290.793 480.575 54
PointMRNet-lite0.553 730.633 690.648 730.659 630.430 770.800 460.390 670.592 690.454 730.371 770.939 570.368 710.136 800.368 770.448 730.560 700.715 460.486 730.882 370.720 760.462 80
JSENetpermissive0.699 210.881 80.762 280.821 130.667 250.800 460.522 110.792 270.613 200.607 210.935 620.492 270.205 550.576 410.853 160.691 270.758 320.652 130.872 480.828 250.649 28
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
CU-Hybrid Net0.693 230.596 760.789 200.803 220.677 230.800 460.469 280.846 120.554 470.591 270.948 330.500 250.316 130.609 320.847 200.732 110.808 70.593 370.894 290.839 220.652 27
FPConvpermissive0.639 450.785 280.760 290.713 470.603 420.798 490.392 640.534 780.603 280.524 450.948 330.457 400.250 400.538 540.723 420.598 610.696 550.614 260.872 480.799 400.567 58
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointSPNet0.637 470.734 500.692 620.714 460.576 530.797 500.446 410.743 390.598 310.437 680.942 510.403 630.150 770.626 290.800 300.649 420.697 540.557 520.846 590.777 590.563 59
Feature-Geometry Netpermissive0.685 270.866 100.748 360.819 140.645 320.794 510.450 390.802 250.587 330.604 220.945 420.464 370.201 580.554 490.840 220.723 150.732 410.602 320.907 180.822 300.603 46
GMLPs0.538 740.495 840.693 610.647 680.471 720.793 520.300 790.477 800.505 580.358 780.903 820.327 770.081 860.472 680.529 650.448 810.710 470.509 660.746 730.737 710.554 64
AttAN0.609 620.760 390.667 690.649 670.521 650.793 520.457 350.648 610.528 530.434 700.947 360.401 640.153 760.454 690.721 430.648 440.717 450.536 630.904 200.765 630.485 75
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
APCF-Net0.631 530.742 470.687 670.672 570.557 600.792 540.408 570.665 590.545 480.508 500.952 220.428 540.186 640.634 260.702 460.620 520.706 510.555 530.873 460.798 420.581 52
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
SPH3D-GCNpermissive0.610 610.858 130.772 250.489 830.532 630.792 540.404 600.643 630.570 420.507 520.935 620.414 610.046 890.510 600.702 460.602 590.705 520.549 560.859 560.773 610.534 68
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
PointMTL0.632 520.731 510.688 650.675 560.591 460.784 560.444 460.565 740.610 220.492 540.949 300.456 410.254 390.587 360.706 450.599 600.665 670.612 290.868 520.791 540.579 53
MVPNetpermissive0.641 420.831 170.715 490.671 590.590 470.781 570.394 630.679 560.642 130.553 340.937 590.462 380.256 380.649 210.406 760.626 510.691 570.666 120.877 410.792 510.608 42
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
wsss-transformer0.600 630.634 680.743 410.697 510.601 440.781 570.437 500.585 710.493 620.446 650.933 670.394 650.011 910.654 200.661 550.603 580.733 400.526 650.832 610.761 650.480 76
SConv0.636 480.830 180.697 580.752 390.572 560.780 590.445 430.716 460.529 520.530 420.951 230.446 480.170 690.507 620.666 530.636 490.682 600.541 610.886 350.799 400.594 50
SQN_0.1%0.569 690.676 620.696 590.657 640.497 670.779 600.424 520.548 750.515 560.376 750.902 830.422 580.357 30.379 760.456 710.596 620.659 680.544 570.685 800.665 840.556 63
subcloud_weak0.516 760.676 620.591 850.609 730.442 750.774 610.335 750.597 670.422 800.357 790.932 680.341 760.094 850.298 810.528 660.473 790.676 630.495 710.602 860.721 750.349 87
LAP-D0.594 640.720 540.692 620.637 720.456 740.773 620.391 660.730 430.587 330.445 670.940 550.381 680.288 230.434 720.453 720.591 630.649 690.581 430.777 700.749 690.610 41
SIConv0.625 590.830 180.694 600.757 370.563 580.772 630.448 400.647 620.520 540.509 490.949 300.431 520.191 620.496 640.614 580.647 450.672 650.535 640.876 420.783 560.571 55
HPEIN0.618 600.729 520.668 680.647 680.597 450.766 640.414 550.680 550.520 540.525 440.946 390.432 500.215 510.493 650.599 590.638 480.617 760.570 450.897 260.806 370.605 45
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
SegGroup_sempermissive0.627 580.818 210.747 380.701 480.602 430.764 650.385 690.629 640.490 630.508 500.931 690.409 620.201 580.564 450.725 410.618 540.692 560.539 620.873 460.794 460.548 65
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
ROSMRF0.580 670.772 330.707 520.681 550.563 580.764 650.362 720.515 790.465 710.465 620.936 610.427 560.207 530.438 700.577 600.536 720.675 640.486 730.723 770.779 570.524 70
DPC0.592 650.720 540.700 560.602 760.480 700.762 670.380 700.713 490.585 360.437 680.940 550.369 700.288 230.434 720.509 680.590 650.639 740.567 480.772 710.755 670.592 51
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
Supervoxel-CNN0.635 490.656 650.711 500.719 440.613 400.757 680.444 460.765 320.534 510.566 310.928 700.478 310.272 300.636 240.531 640.664 360.645 710.508 680.864 540.792 510.611 39
3DSM_DMMF0.631 530.626 700.745 390.801 230.607 410.751 690.506 140.729 440.565 430.491 550.866 860.434 490.197 610.595 340.630 570.709 220.705 520.560 490.875 430.740 700.491 74
FusionAwareConv0.630 560.604 750.741 430.766 350.590 470.747 700.501 160.734 420.503 590.527 430.919 760.454 420.323 110.550 530.420 750.678 310.688 580.544 570.896 270.795 440.627 36
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
Pointnet++ & Featurepermissive0.557 720.735 490.661 710.686 530.491 690.744 710.392 640.539 760.451 740.375 760.946 390.376 690.205 550.403 750.356 790.553 710.643 720.497 700.824 640.756 660.515 71
CCRFNet0.589 660.766 380.659 720.683 540.470 730.740 720.387 680.620 660.490 630.476 590.922 740.355 740.245 430.511 590.511 670.571 680.643 720.493 720.872 480.762 640.600 47
TextureNetpermissive0.566 700.672 640.664 700.671 590.494 680.719 730.445 430.678 570.411 810.396 730.935 620.356 730.225 470.412 740.535 630.565 690.636 750.464 770.794 690.680 810.568 57
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
PointCNN with RGBpermissive0.458 810.577 780.611 810.356 910.321 880.715 740.299 810.376 850.328 880.319 820.944 480.285 820.164 720.216 890.229 840.484 770.545 830.456 790.755 720.709 770.475 78
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PCNN0.498 790.559 790.644 760.560 800.420 790.711 750.229 870.414 810.436 770.352 800.941 530.324 780.155 750.238 860.387 780.493 750.529 850.509 660.813 670.751 680.504 73
3DMV, FTSDF0.501 780.558 800.608 830.424 890.478 710.690 760.246 850.586 700.468 690.450 640.911 780.394 650.160 740.438 700.212 860.432 820.541 840.475 760.742 740.727 730.477 77
DVVNet0.562 710.648 660.700 560.770 320.586 500.687 770.333 760.650 600.514 570.475 600.906 800.359 720.223 480.340 790.442 740.422 830.668 660.501 690.708 780.779 570.534 68
FCPNpermissive0.447 820.679 610.604 840.578 790.380 810.682 780.291 820.106 910.483 660.258 890.920 750.258 860.025 900.231 880.325 800.480 780.560 810.463 780.725 760.666 830.231 91
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
SPLAT Netcopyleft0.393 880.472 880.511 880.606 740.311 890.656 790.245 860.405 820.328 880.197 900.927 710.227 890.000 930.001 930.249 830.271 910.510 860.383 890.593 870.699 790.267 89
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
SurfaceConvPF0.442 840.505 830.622 800.380 900.342 860.654 800.227 880.397 830.367 840.276 860.924 720.240 870.198 600.359 780.262 820.366 850.581 770.435 840.640 830.668 820.398 82
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
DGCNN_reproducecopyleft0.446 830.474 870.623 790.463 850.366 830.651 810.310 770.389 840.349 860.330 810.937 590.271 840.126 810.285 820.224 850.350 880.577 780.445 820.625 840.723 740.394 83
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
Tangent Convolutionspermissive0.438 860.437 890.646 750.474 840.369 820.645 820.353 730.258 880.282 900.279 850.918 770.298 810.147 790.283 830.294 810.487 760.562 800.427 850.619 850.633 850.352 86
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
SD-DETR0.576 680.746 440.609 820.445 870.517 660.643 830.366 710.714 480.456 720.468 610.870 850.432 500.264 360.558 480.674 500.586 660.688 580.482 750.739 750.733 720.537 67
PanopticFusion-label0.529 750.491 850.688 650.604 750.386 800.632 840.225 890.705 520.434 780.293 840.815 870.348 750.241 440.499 630.669 510.507 740.649 690.442 830.796 680.602 870.561 60
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Online SegFusion0.515 770.607 740.644 760.579 780.434 760.630 850.353 730.628 650.440 760.410 710.762 900.307 790.167 710.520 570.403 770.516 730.565 790.447 810.678 810.701 780.514 72
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
PNET20.442 840.548 810.548 860.597 770.363 840.628 860.300 790.292 860.374 830.307 830.881 840.268 850.186 640.238 860.204 880.407 840.506 890.449 800.667 820.620 860.462 80
ScanNet+FTSDF0.383 890.297 910.491 890.432 880.358 850.612 870.274 830.116 900.411 810.265 870.904 810.229 880.079 870.250 840.185 890.320 890.510 860.385 880.548 880.597 900.394 83
3DMV0.484 800.484 860.538 870.643 700.424 780.606 880.310 770.574 720.433 790.378 740.796 880.301 800.214 520.537 550.208 870.472 800.507 880.413 860.693 790.602 870.539 66
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SSC-UNetpermissive0.308 910.353 900.290 920.278 920.166 920.553 890.169 910.286 870.147 920.148 920.908 790.182 910.064 880.023 920.018 930.354 870.363 900.345 900.546 900.685 800.278 88
ScanNetpermissive0.306 920.203 920.366 910.501 820.311 890.524 900.211 900.002 930.342 870.189 910.786 890.145 920.102 840.245 850.152 900.318 900.348 910.300 910.460 910.437 920.182 92
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
3DWSSS0.425 870.525 820.647 740.522 810.324 870.488 910.077 920.712 500.353 850.401 720.636 920.281 830.176 670.340 790.565 610.175 920.551 820.398 870.370 920.602 870.361 85
PointNet++permissive0.339 900.584 770.478 900.458 860.256 910.360 920.250 840.247 890.278 910.261 880.677 910.183 900.117 820.212 900.145 910.364 860.346 920.232 920.548 880.523 910.252 90
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ERROR0.054 930.000 930.041 930.172 930.030 930.062 930.001 930.035 920.004 930.051 930.143 930.019 930.003 920.041 910.050 920.003 930.054 930.018 930.005 930.264 930.082 93


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
SphereSeg0.680 121.000 10.856 30.744 180.618 90.893 10.151 170.651 220.713 20.537 110.579 140.430 230.651 91.000 10.389 340.744 210.697 90.991 290.601 5
SPFormer0.770 20.903 320.903 10.806 80.609 110.886 20.568 10.815 60.705 30.711 10.655 40.652 80.685 81.000 10.789 30.809 100.776 41.000 10.583 7
Mask3D0.780 11.000 10.786 210.716 200.696 30.885 30.500 20.714 170.810 10.672 30.715 30.679 60.809 11.000 10.831 10.833 70.787 31.000 10.602 4
DKNet0.718 81.000 10.814 100.782 110.619 80.872 40.224 130.751 140.569 120.677 20.585 110.724 10.633 170.981 210.515 250.819 80.736 81.000 10.617 2
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SoftGroup++0.769 31.000 10.803 160.937 10.684 40.865 50.213 150.870 20.664 40.571 60.758 10.702 40.807 21.000 10.653 140.902 10.792 21.000 10.626 1
INS-Conv-instance0.657 161.000 10.760 260.667 240.581 140.863 60.323 50.655 210.477 150.473 170.549 170.432 220.650 101.000 10.655 120.738 220.585 230.944 330.472 23
SoftGrouppermissive0.761 41.000 10.808 130.845 60.716 10.862 70.243 120.824 30.655 60.620 40.734 20.699 50.791 40.981 210.716 60.844 40.769 51.000 10.594 6
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
GraphCut0.732 51.000 10.788 190.724 190.642 70.859 80.248 110.787 100.618 100.596 50.653 60.722 20.583 251.000 10.766 40.861 20.825 11.000 10.504 17
OccuSeg+instance0.672 141.000 10.758 280.682 220.576 160.842 90.477 30.504 340.524 130.567 70.585 130.451 180.557 261.000 10.751 50.797 110.563 271.000 10.467 24
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
RPGN0.643 191.000 10.758 270.582 320.539 190.826 100.046 320.765 110.372 240.436 240.588 100.539 140.650 101.000 10.577 190.750 190.653 160.997 230.495 20
Box2Mask0.677 131.000 10.847 60.771 130.509 240.816 110.277 70.558 310.482 140.562 80.640 70.448 190.700 51.000 10.666 90.852 30.578 240.997 230.488 21
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Mask-Group0.664 151.000 10.822 90.764 160.616 100.815 120.139 210.694 190.597 110.459 190.566 150.599 100.600 190.516 400.715 70.819 90.635 171.000 10.603 3
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
CSC-Pretrained0.648 171.000 10.810 110.768 140.523 230.813 130.143 200.819 50.389 220.422 260.511 220.443 200.650 101.000 10.624 170.732 230.634 181.000 10.375 30
PCJC0.578 261.000 10.810 120.583 310.449 300.813 140.042 330.603 270.341 270.490 150.465 250.410 250.650 100.835 320.264 390.694 300.561 280.889 370.504 18
HAISpermissive0.699 101.000 10.849 40.820 70.675 50.808 150.279 60.757 130.465 170.517 130.596 90.559 110.600 191.000 10.654 130.767 140.676 130.994 280.560 13
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 111.000 10.697 360.888 40.556 180.803 160.387 40.626 240.417 210.556 90.585 120.702 30.600 191.000 10.824 20.720 260.692 101.000 10.509 16
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
Dyco3Dcopyleft0.641 201.000 10.841 70.893 30.531 210.802 170.115 250.588 290.448 180.438 220.537 210.430 240.550 270.857 260.534 230.764 160.657 140.987 300.568 9
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
PointGroup0.636 221.000 10.765 240.624 260.505 260.797 180.116 240.696 180.384 230.441 210.559 160.476 160.596 231.000 10.666 90.756 180.556 310.997 230.513 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]
SSEN0.575 271.000 10.761 250.473 340.477 280.795 190.066 290.529 320.658 50.460 180.461 260.380 280.331 390.859 250.401 330.692 310.653 151.000 10.348 32
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
SSEC0.700 91.000 10.848 50.763 170.609 120.792 200.262 90.824 30.627 90.535 120.547 190.493 150.600 191.000 10.712 80.731 240.689 121.000 10.563 11
Sparse R-CNN0.515 311.000 10.538 430.282 370.468 290.790 210.173 160.345 390.429 190.413 290.484 240.176 350.595 240.591 380.522 240.668 330.476 360.986 310.327 33
IPCA-Inst0.731 61.000 10.788 200.884 50.698 20.788 220.252 100.760 120.646 70.511 140.637 80.665 70.804 31.000 10.644 150.778 120.747 71.000 10.561 12
PE0.645 181.000 10.773 230.798 100.538 200.786 230.088 280.799 90.350 260.435 250.547 180.545 120.646 160.933 240.562 210.761 170.556 320.997 230.501 19
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
DD-UNet+Group0.635 230.667 330.797 180.714 210.562 170.774 240.146 180.810 80.429 200.476 160.546 200.399 260.633 171.000 10.632 160.722 250.609 191.000 10.514 14
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
TopoSeg0.725 71.000 10.806 150.933 20.668 60.758 250.272 80.734 160.630 80.549 100.654 50.606 90.697 70.966 230.612 180.839 50.754 61.000 10.573 8
3D-MPA0.611 251.000 10.833 80.765 150.526 220.756 260.136 230.588 290.470 160.438 230.432 290.358 300.650 100.857 260.429 300.765 150.557 301.000 10.430 26
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
RWSeg0.567 280.528 420.708 350.626 250.580 150.745 270.063 300.627 230.240 320.400 310.497 230.464 170.515 281.000 10.475 270.745 200.571 251.000 10.429 27
One_Thing_One_Clickpermissive0.529 300.667 330.718 310.777 120.399 310.683 280.000 410.669 200.138 370.391 320.374 340.539 130.360 380.641 370.556 220.774 130.593 210.997 230.251 37
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
GICN0.638 211.000 10.895 20.800 90.480 270.676 290.144 190.737 150.354 250.447 200.400 310.365 290.700 51.000 10.569 200.836 60.599 201.000 10.473 22
SegGroup_inspermissive0.445 380.667 330.773 220.185 440.317 360.656 300.000 410.407 380.134 380.381 330.267 380.217 330.476 300.714 340.452 280.629 360.514 341.000 10.222 40
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
MTML0.549 291.000 10.807 140.588 300.327 350.647 310.004 380.815 70.180 340.418 270.364 350.182 340.445 321.000 10.442 290.688 320.571 261.000 10.396 28
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
MASCpermissive0.447 370.528 420.555 410.381 350.382 320.633 320.002 390.509 330.260 300.361 340.432 280.327 310.451 310.571 390.367 360.639 350.386 370.980 320.276 36
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
DENet0.629 241.000 10.797 170.608 270.589 130.627 330.219 140.882 10.310 280.402 300.383 330.396 270.650 101.000 10.663 110.543 410.691 111.000 10.568 10
Occipital-SCS0.512 321.000 10.716 320.509 330.506 250.611 340.092 270.602 280.177 350.346 350.383 320.165 360.442 330.850 310.386 350.618 370.543 330.889 370.389 29
R-PointNet0.306 420.500 440.405 460.311 360.348 340.589 350.054 310.068 470.126 390.283 390.290 370.028 450.219 430.214 440.331 370.396 470.275 430.821 420.245 38
SALoss-ResNet0.459 361.000 10.737 300.159 470.259 390.587 360.138 220.475 360.217 330.416 280.408 300.128 370.315 400.714 340.411 310.536 420.590 220.873 400.304 34
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
3D-SISpermissive0.382 391.000 10.432 450.245 390.190 410.577 370.013 360.263 410.033 440.320 370.240 400.075 400.422 360.857 260.117 430.699 280.271 450.883 390.235 39
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
SPG_WSIS0.470 350.667 330.685 370.677 230.372 330.562 380.000 410.482 350.244 310.316 380.298 360.052 440.442 340.857 260.267 380.702 270.559 291.000 10.287 35
Hier3Dcopyleft0.323 400.667 330.542 420.264 380.157 440.550 390.000 410.205 440.009 450.270 400.218 410.075 400.500 290.688 360.007 490.698 290.301 420.459 460.200 41
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
PanopticFusion-inst0.478 340.667 330.712 340.595 280.259 400.550 400.000 410.613 260.175 360.250 410.434 270.437 210.411 370.857 260.485 260.591 400.267 460.944 330.359 31
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
Region-18class0.284 430.250 480.751 290.228 420.270 380.521 410.000 410.468 370.008 470.205 420.127 430.000 490.068 470.070 470.262 400.652 340.323 400.740 430.173 42
SemRegionNet-20cls0.250 440.333 450.613 390.229 410.163 430.493 420.000 410.304 400.107 400.147 450.100 440.052 430.231 410.119 450.039 450.445 450.325 390.654 440.141 44
tmp0.248 450.667 330.437 440.188 430.153 450.491 430.000 410.208 430.094 420.153 440.099 450.057 420.217 440.119 450.039 450.466 440.302 410.640 450.140 45
3D-BoNet0.488 331.000 10.672 380.590 290.301 370.484 440.098 260.620 250.306 290.341 360.259 390.125 380.434 350.796 330.402 320.499 430.513 350.909 360.439 25
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
UNet-backbone0.319 410.667 330.715 330.233 400.189 420.479 450.008 370.218 420.067 430.201 430.173 420.107 390.123 450.438 410.150 410.615 380.355 380.916 350.093 48
ASIS0.199 470.333 450.253 480.167 460.140 460.438 460.000 410.177 450.008 460.121 460.069 460.004 480.231 420.429 420.036 470.445 460.273 440.333 480.119 47
3D-BEVIS0.248 450.667 330.566 400.076 480.035 490.394 470.027 350.035 480.098 410.099 470.030 480.025 460.098 460.375 430.126 420.604 390.181 470.854 410.171 43
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet0.143 480.208 490.390 470.169 450.065 470.275 480.029 340.069 460.000 480.087 480.043 470.014 470.027 490.000 480.112 440.351 480.168 480.438 470.138 46
MaskRCNN 2d->3d Proj0.058 490.333 450.002 490.000 490.053 480.002 490.002 400.021 490.000 480.045 490.024 490.238 320.065 480.000 480.014 480.107 490.020 490.110 490.006 49


This table lists the benchmark results for the 2D semantic label scenario.


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysorted 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 10.422 100.898 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 60.769 40.656 30.567 30.931 30.395 30.390 50.700 20.534 30.689 60.770 20.574 30.865 40.831 30.675 3
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 20.179 170.648 30.463 30.549 10.742 50.676 20.628 20.961 10.420 20.379 60.684 40.381 110.732 20.723 30.599 20.827 90.851 20.634 4
DMMF_3d0.605 50.651 70.744 60.782 30.637 40.387 40.536 20.732 60.590 50.540 40.856 140.359 70.306 110.596 80.539 20.627 130.706 40.497 70.785 130.757 120.476 14
3DMV (2d proj)0.498 140.481 160.612 150.579 80.456 140.343 50.384 130.623 140.525 110.381 150.845 150.254 140.264 150.557 100.182 160.581 160.598 110.429 140.760 150.661 170.446 16
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
AdapNet++copyleft0.503 130.613 90.722 90.418 130.358 180.337 60.370 150.479 160.443 140.368 160.907 70.207 150.213 170.464 160.525 40.618 140.657 50.450 120.788 120.721 150.408 17
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
MCA-Net0.595 60.533 130.756 50.746 40.590 60.334 70.506 30.670 80.587 60.500 90.905 80.366 60.352 70.601 70.506 50.669 100.648 60.501 50.839 80.769 100.516 13
RFBNet0.592 70.616 80.758 40.659 50.581 70.330 80.469 50.655 110.543 100.524 60.924 40.355 90.336 90.572 90.479 80.671 80.648 60.480 80.814 110.814 50.614 7
SSMAcopyleft0.577 90.695 40.716 100.439 120.563 90.314 90.444 80.719 70.551 70.503 80.887 100.346 100.348 80.603 60.353 130.709 40.600 100.457 110.901 20.786 70.599 8
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
DCRedNet0.583 80.682 50.723 80.542 90.510 120.310 100.451 60.668 90.549 80.520 70.920 60.375 40.446 20.528 120.417 100.670 90.577 120.478 90.862 50.806 60.628 6
SN_RN152pyrx8_RVCcopyleft0.546 110.572 100.663 140.638 70.518 100.298 110.366 160.633 130.510 120.446 130.864 120.296 120.267 130.542 110.346 140.704 50.575 130.431 130.853 70.766 110.630 5
CMX0.613 40.681 60.725 70.502 100.634 50.297 120.478 40.830 20.651 40.537 50.924 40.375 40.315 100.686 30.451 90.714 30.543 150.504 40.894 30.823 40.688 2
FuseNetpermissive0.535 120.570 110.681 130.182 160.512 110.290 130.431 90.659 100.504 130.495 100.903 90.308 110.428 40.523 130.365 120.676 70.621 90.470 100.762 140.779 80.541 11
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
MIX6D_RVC0.562 100.564 120.701 120.193 150.575 80.285 140.399 120.784 30.546 90.485 110.878 110.357 80.432 30.604 50.487 60.651 110.572 140.501 50.862 50.771 90.586 9
MSeg1080_RVCpermissive0.485 150.505 140.709 110.092 180.427 150.241 150.411 110.654 120.385 180.457 120.861 130.053 180.279 120.503 140.481 70.645 120.626 80.365 160.748 160.725 140.529 12
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
Enet (reimpl)0.376 170.264 180.452 180.452 110.365 160.181 160.143 180.456 170.409 170.346 170.769 180.164 160.218 160.359 170.123 180.403 180.381 180.313 180.571 170.685 160.472 15
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)permissive0.330 180.293 170.521 170.657 60.361 170.161 170.250 170.004 180.440 150.183 180.836 160.125 170.060 180.319 180.132 170.417 170.412 170.344 170.541 180.427 180.109 18
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ILC-PSPNet0.475 160.490 150.581 160.289 140.507 130.067 180.379 140.610 150.417 160.435 140.822 170.278 130.267 130.503 140.228 150.616 150.533 160.375 150.820 100.729 130.560 10
DMMF0.003 190.000 190.005 190.000 190.000 190.037 190.001 190.000 190.001 190.005 190.003 190.000 190.000 190.000 190.000 190.000 190.002 190.001 190.000 190.006 190.000 19


This table lists the benchmark results for the 2D semantic instance scenario.




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysorted 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