Presenting the ScanNet200 Benchmark

We present the ScanNet200 benchmark, which studies an order of magnitude more class categories than previous version of ScanNet. The scene geometry is shared within the two tasks, but the parsing of surface annotation allows for a larger vocabulary and more realistic setting for in the wild 3D understanding methods.

The ScanNet200 benchmark includes both finer-grained categories as well as a large number of previously unaddressed classes. This induces a much more challenging setting regarding the diversity of naturally observed semantic classes seen in the raw ScanNet RGB-D observations, where the data also reflects naturally encountered class imbalances. The difference in category frequencies between ScanNet and ScanNet200 can be seen in the Figure above.

ScanNet200 Benchmark

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




Method Infoavg iouhead ioucommon ioutail iouwallchairfloortabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 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 Infoavgchairtabledoorcouchcabinetshelfdeskoffice chairbedpillowsinkpicturewindowtoiletbookshelfmonitorcurtainbookarmchaircoffee tableboxrefrigeratorlampkitchen cabinettowelclothestvnightstandcounterdresserstoolcushionplantceilingbathtubend tabledining tablekeyboardbagbackpacktoilet paperprintertv standwhiteboardblanketshower curtaintrash canclosetstairsmicrowavestoveshoecomputer towerbottlebinottomanbenchboardwashing machinemirrorcopierbasketsofa chairfile cabinetfanlaptopshowerpaperpersonpaper towel dispenserovenblindsrackplateblackboardpianosuitcaserailradiatorrecycling bincontainerwardrobesoap dispensertelephonebucketclockstandlightlaundry basketpipeclothes dryerguitartoilet paper holderseatspeakercolumnbicycleladderbathroom stallshower wallcupjacketstorage bincoffee makerdishwasherpaper towel rollmachinematwindowsillbartoasterbulletin boardironing boardfireplacesoap dishkitchen counterdoorframetoilet paper dispensermini fridgefire extinguisherballhatshower curtain rodwater coolerpaper cuttertrayshower doorpillarledgetoaster ovenmousetoilet seat cover dispenserfurniturecartstorage containerscaletissue boxlight switchcratepower outletdecorationsignprojectorcloset doorvacuum cleanercandleplungerstuffed animalheadphonesdish rackbroomguitar caserange hooddustpanhair dryerwater bottlehandicap barpurseventshower floorwater pitchermailboxbowlpaper bagalarm clockmusic standprojector screendividerlaundry detergentbathroom counterobjectbathroom vanitycloset walllaundry hamperbathroom stall doorceiling lighttrash bindumbbellstair railtubebathroom cabinetcd casecloset rodcoffee kettlestructureshower headkeyboard pianocase of water bottlescoat rackstorage organizerfolded chairfire alarmpower stripcalendarposterpotted plantluggagemattress
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort 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 by
TD3D Scannet2000.320 20.501 20.264 20.164 20.841 10.679 10.716 20.879 20.280 30.192 10.634 10.231 10.733 30.459 20.565 30.498 50.560 21.000 10.686 10.890 20.708 10.123 40.820 10.152 20.967 10.456 10.458 20.387 20.194 10.435 50.906 10.077 10.396 20.509 10.217 20.715 10.619 21.000 10.099 20.792 10.513 20.062 20.506 30.549 10.605 11.000 10.123 40.106 11.000 10.744 40.000 21.000 10.504 50.525 20.185 20.790 40.101 20.008 20.587 20.356 10.817 10.083 51.000 10.000 10.621 10.842 10.415 10.268 40.083 40.000 20.098 30.881 10.125 20.000 20.000 10.000 20.000 30.125 40.332 30.448 50.202 20.196 10.798 10.264 20.000 20.000 10.017 20.233 20.000 10.063 10.333 20.038 10.111 10.250 30.000 20.516 10.208 10.470 20.094 30.218 10.000 10.667 20.033 50.000 20.000 10.400 10.156 20.000 10.267 10.226 10.000 10.104 20.159 20.299 50.095 30.458 10.500 10.000 11.000 10.472 10.792 30.000 10.022 10.061 20.250 10.008 10.250 20.333 20.143 20.396 20.049 20.012 10.000 10.283 40.000 10.241 40.000 10.101 20.331 40.000 10.629 30.000 10.000 10.857 20.222 30.677 10.000 10.003 20.000 10.000 20.000 10.076 20.252 30.400 10.431 20.061 30.328 30.331 40.500 10.000 20.000 10.167 10.000 10.000 10.000 20.500 20.000 10.000 21.000 10.542 10.000 20.063 10.000 20.000 1
Mask3D Scannet2000.388 10.542 10.357 10.237 10.808 20.676 20.741 10.832 40.496 10.151 30.628 20.021 20.955 10.578 10.753 10.612 10.591 10.822 50.609 30.926 10.614 30.291 10.725 40.163 10.890 20.380 50.615 10.517 10.130 30.806 10.857 20.024 20.511 10.412 50.226 10.597 20.756 11.000 10.111 10.792 10.736 10.091 10.610 10.527 20.323 41.000 10.504 10.063 21.000 10.853 10.010 10.974 30.839 10.667 10.301 10.883 10.266 10.039 10.640 10.311 20.739 20.463 11.000 10.000 10.287 20.715 20.313 20.600 11.000 10.027 10.076 40.502 50.500 10.409 10.000 10.194 10.125 20.500 10.491 10.748 10.050 40.042 20.776 20.352 10.008 10.000 10.033 10.254 10.000 10.005 20.552 10.008 20.020 20.750 10.500 10.409 20.065 30.511 10.107 10.178 20.000 11.000 10.400 10.016 10.000 10.400 10.571 10.000 10.060 20.044 20.000 10.514 10.278 11.000 10.258 10.017 30.125 50.000 10.792 30.399 31.000 10.000 10.013 20.265 10.018 20.000 21.000 10.335 10.381 10.500 10.250 10.004 20.000 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 40.000 10.028 10.000 11.000 10.000 10.099 10.451 10.400 10.668 10.573 10.606 10.077 50.003 40.004 10.000 10.042 30.000 10.000 11.000 11.000 10.000 10.042 10.000 20.200 20.302 10.000 21.000 10.000 1
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Minkowski 34D Inst.permissive0.203 50.369 40.134 50.078 50.706 40.382 40.693 30.845 30.221 50.150 40.158 40.000 30.746 20.369 40.545 40.595 20.387 40.997 30.413 50.720 50.636 20.165 30.732 30.070 40.851 40.402 40.251 40.313 40.123 40.583 40.696 30.000 30.051 50.500 20.000 30.500 40.372 50.667 40.009 40.000 30.307 50.003 40.479 40.107 50.226 50.903 40.109 50.031 30.981 30.726 50.000 20.522 50.669 20.282 50.052 50.778 50.000 40.000 30.400 30.074 40.333 40.218 41.000 10.000 10.250 30.406 50.118 50.317 20.100 30.000 20.191 10.596 20.000 30.000 20.000 10.000 20.000 30.500 10.178 50.701 20.000 50.000 30.522 50.018 50.000 20.000 10.000 30.060 40.000 10.000 30.033 50.000 30.000 30.000 40.000 20.281 30.100 20.000 50.090 40.133 40.000 10.422 50.050 40.000 20.000 10.200 30.000 50.000 10.000 30.000 30.000 10.000 40.123 40.677 20.021 40.000 40.500 10.000 10.500 40.442 20.125 50.000 10.000 30.000 30.000 30.000 20.000 30.056 40.000 30.000 30.000 30.000 30.000 10.200 50.000 10.143 50.000 10.000 30.250 50.000 10.511 40.000 10.000 10.286 30.083 40.396 20.000 10.000 30.000 10.000 20.000 10.025 40.300 20.000 30.371 30.070 20.000 40.385 30.000 50.000 20.000 10.000 50.000 10.000 10.000 20.500 20.000 10.000 20.000 20.200 20.000 20.000 20.000 20.000 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.209 40.361 50.157 40.085 40.700 50.248 50.634 50.776 50.322 20.135 50.103 50.000 30.524 50.364 50.618 20.592 30.381 50.997 30.589 40.747 40.340 50.109 50.768 20.059 50.702 50.448 20.188 50.149 50.091 50.636 30.573 50.000 30.246 30.500 20.000 30.450 50.405 30.667 40.006 50.000 30.356 40.007 30.506 20.420 30.340 30.667 50.294 20.004 40.571 40.748 20.000 21.000 10.573 40.502 40.094 40.807 30.000 40.000 30.400 30.000 50.278 50.228 31.000 10.000 10.115 50.432 40.198 30.050 50.125 20.000 20.000 50.573 30.000 30.000 20.000 10.000 20.000 30.125 40.312 40.610 30.221 10.000 30.667 40.050 40.000 20.000 10.000 30.032 50.000 10.000 30.083 30.000 30.000 30.000 40.000 20.220 40.000 50.125 30.000 50.111 50.000 10.667 20.200 30.000 20.000 10.000 40.110 30.000 10.000 30.000 30.000 10.000 40.053 50.500 40.000 50.000 40.500 10.000 10.500 40.333 40.500 40.000 10.000 30.000 30.000 30.000 20.000 30.000 50.000 30.000 30.000 30.000 30.000 10.600 20.000 10.364 20.000 10.000 30.750 10.000 10.833 10.000 10.000 10.143 50.000 50.396 20.000 10.000 30.000 10.000 20.000 10.021 50.221 40.000 30.093 50.055 40.451 20.677 20.125 20.000 20.000 10.028 40.000 10.000 10.000 20.500 20.000 10.000 20.000 20.050 40.000 20.000 20.000 20.000 1
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
LGround Inst.permissive0.246 30.413 30.170 30.130 30.754 30.541 30.682 40.903 10.264 40.164 20.234 30.000 30.681 40.452 30.464 50.541 40.399 31.000 10.637 20.772 30.588 40.190 20.589 50.081 30.857 30.426 30.373 30.318 30.135 20.690 20.653 40.000 30.159 40.500 20.000 30.581 30.387 41.000 10.046 30.000 30.402 30.003 50.455 50.196 40.571 21.000 10.270 30.003 50.530 50.748 30.000 20.744 40.575 30.511 30.112 30.815 20.067 30.000 30.400 30.167 30.667 30.241 21.000 10.000 10.208 40.660 30.125 40.317 20.000 50.000 20.100 20.561 40.000 30.000 20.000 10.000 21.000 10.500 10.344 20.568 40.167 30.000 30.706 30.068 30.000 20.000 10.000 30.063 30.000 10.000 30.056 40.000 30.000 30.500 20.000 20.143 50.017 40.125 30.097 20.164 30.000 10.582 40.400 10.000 20.000 10.000 40.083 40.000 10.000 30.000 30.000 10.025 30.156 30.533 30.250 20.200 20.500 10.000 11.000 10.333 41.000 10.000 10.000 30.000 30.000 30.000 20.000 30.333 20.000 30.000 30.000 30.000 30.000 10.400 30.000 10.364 20.000 10.000 30.500 30.000 10.511 40.000 10.000 10.286 30.333 20.000 50.000 10.000 30.000 10.000 20.000 10.034 30.111 50.000 30.333 40.031 50.000 40.750 10.125 20.000 20.000 10.151 20.000 10.000 10.000 20.500 20.000 10.000 20.000 20.000 50.000 20.000 20.000 20.000 1
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.


ScanNet Benchmark

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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mix3Dpermissive0.781 10.964 10.855 10.843 100.781 10.858 70.575 30.831 190.685 70.714 10.979 10.594 30.310 170.801 10.892 80.841 20.819 30.723 30.940 80.887 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)
OccuSeg+Semantic0.764 20.758 460.796 200.839 120.746 110.907 10.562 50.850 130.680 90.672 50.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 10
CU-Hybrid Net0.764 20.924 20.819 80.840 110.757 60.853 90.580 10.848 140.709 20.643 120.958 100.587 70.295 230.753 140.884 120.758 110.815 60.725 20.927 170.867 100.743 5
O-CNNpermissive0.762 40.924 20.823 50.844 90.770 20.852 100.577 20.847 150.711 10.640 160.958 100.592 40.217 580.762 100.888 90.758 110.813 70.726 10.932 150.868 90.744 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
PointTransformerV20.752 50.742 530.809 140.872 10.758 50.860 60.552 70.891 50.610 300.687 20.960 80.559 140.304 200.766 80.926 20.767 80.797 140.644 220.942 60.876 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
DMF-Net0.752 50.906 60.793 230.802 290.689 270.825 310.556 60.867 90.681 80.602 300.960 80.555 160.365 30.779 30.859 170.747 140.795 180.717 40.917 200.856 180.764 2
PointConvFormer0.749 70.793 320.790 240.807 240.750 100.856 80.524 160.881 70.588 410.642 150.977 40.591 50.274 340.781 20.929 10.804 30.796 150.642 230.947 30.885 20.715 17
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 70.909 40.818 100.811 210.752 80.839 190.485 320.842 160.673 100.644 110.957 130.528 250.305 190.773 60.859 170.788 40.818 50.693 70.916 210.856 180.723 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 90.623 790.804 160.859 30.745 120.824 330.501 230.912 20.690 60.685 30.956 140.567 110.320 130.768 70.918 30.720 220.802 100.676 120.921 180.881 40.779 1
StratifiedFormerpermissive0.747 100.901 70.803 170.845 80.757 60.846 140.512 190.825 220.696 50.645 100.956 140.576 90.262 440.744 180.861 160.742 150.770 300.705 50.899 330.860 150.734 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
VMNetpermissive0.746 110.870 120.838 20.858 40.729 170.850 120.501 230.874 80.587 420.658 80.956 140.564 120.299 210.765 90.900 50.716 250.812 80.631 280.939 90.858 160.709 18
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Virtual MVFusion0.746 110.771 400.819 80.848 60.702 250.865 50.397 700.899 30.699 30.664 70.948 410.588 60.330 90.746 170.851 240.764 90.796 150.704 60.935 110.866 110.728 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
Retro-FPN0.744 130.842 190.800 180.767 410.740 130.836 220.541 100.914 10.672 110.626 190.958 100.552 170.272 360.777 40.886 110.696 320.801 110.674 140.941 70.858 160.717 15
EQ-Net0.743 140.620 800.799 190.849 50.730 160.822 350.493 300.897 40.664 120.681 40.955 180.562 130.378 10.760 110.903 40.738 160.801 110.673 150.907 260.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
LRPNet0.742 150.816 270.806 150.807 240.752 80.828 290.575 30.839 180.699 30.637 170.954 230.520 270.320 130.755 130.834 280.760 100.772 270.676 120.915 220.862 130.717 15
SAT0.742 150.860 140.765 350.819 160.769 30.848 130.533 120.829 200.663 130.631 180.955 180.586 80.274 340.753 140.896 60.729 170.760 370.666 170.921 180.855 200.733 7
TXC0.740 170.842 190.832 40.805 280.715 210.846 140.473 340.885 60.615 260.671 60.971 60.547 180.320 130.697 220.799 370.777 60.819 30.682 100.946 40.871 80.696 23
LargeKernel3D0.739 180.909 40.820 70.806 260.740 130.852 100.545 90.826 210.594 400.643 120.955 180.541 200.263 430.723 200.858 190.775 70.767 310.678 110.933 130.848 240.694 24
MinkowskiNetpermissive0.736 190.859 150.818 100.832 130.709 220.840 180.521 180.853 120.660 150.643 120.951 310.544 190.286 280.731 190.893 70.675 390.772 270.683 90.874 510.852 220.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 200.890 80.837 30.864 20.726 180.873 20.530 150.824 230.489 730.647 90.978 20.609 20.336 70.624 370.733 460.758 110.776 250.570 530.949 20.877 50.728 8
SparseConvNet0.725 210.647 760.821 60.846 70.721 190.869 30.533 120.754 430.603 360.614 230.955 180.572 100.325 110.710 210.870 130.724 200.823 20.628 290.934 120.865 120.683 27
PointTransformer++0.725 210.727 600.811 130.819 160.765 40.841 170.502 220.814 290.621 250.623 200.955 180.556 150.284 290.620 380.866 140.781 50.757 400.648 200.932 150.862 130.709 18
MatchingNet0.724 230.812 290.812 120.810 220.735 150.834 240.495 290.860 110.572 480.602 300.954 230.512 290.280 310.757 120.845 260.725 190.780 230.606 390.937 100.851 230.700 21
INS-Conv-semantic0.717 240.751 490.759 380.812 200.704 240.868 40.537 110.842 160.609 320.608 260.953 260.534 210.293 240.616 390.864 150.719 240.793 190.640 240.933 130.845 290.663 32
PointMetaBase0.714 250.835 210.785 260.821 140.684 290.846 140.531 140.865 100.614 270.596 340.953 260.500 320.246 500.674 230.888 90.692 330.764 330.624 300.849 660.844 300.675 29
contrastBoundarypermissive0.705 260.769 430.775 310.809 230.687 280.820 380.439 580.812 300.661 140.591 360.945 490.515 280.171 760.633 340.856 200.720 220.796 150.668 160.889 400.847 260.689 25
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 270.889 90.745 470.813 190.672 310.818 420.493 300.815 270.623 230.610 240.947 430.470 420.249 490.594 420.848 250.705 290.779 240.646 210.892 380.823 360.611 46
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 Click0.701 280.825 250.796 200.723 480.716 200.832 250.433 600.816 250.634 210.609 250.969 70.418 670.344 50.559 540.833 290.715 260.808 90.560 570.902 300.847 260.680 28
JSENetpermissive0.699 290.881 110.762 360.821 140.667 320.800 550.522 170.792 350.613 280.607 270.935 690.492 340.205 630.576 470.853 220.691 340.758 390.652 190.872 540.828 330.649 36
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-IIpermissive0.696 300.704 650.790 240.787 330.709 220.837 200.459 420.815 270.543 570.615 220.956 140.529 230.250 470.551 590.790 380.703 300.799 130.619 340.908 250.848 240.700 21
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 310.743 520.794 220.655 720.684 290.822 350.497 280.719 530.622 240.617 210.977 40.447 540.339 60.750 160.664 610.703 300.790 210.596 430.946 40.855 200.647 37
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 320.884 100.754 420.795 320.647 380.818 420.422 620.802 330.612 290.604 280.945 490.462 450.189 710.563 530.853 220.726 180.765 320.632 270.904 280.821 390.606 50
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 330.704 650.741 510.754 450.656 340.829 270.501 230.741 480.609 320.548 430.950 350.522 260.371 20.633 340.756 410.715 260.771 290.623 310.861 620.814 410.658 33
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 340.866 130.748 440.819 160.645 400.794 580.450 470.802 330.587 420.604 280.945 490.464 440.201 660.554 560.840 270.723 210.732 490.602 410.907 260.822 380.603 53
KP-FCNN0.684 350.847 180.758 400.784 350.647 380.814 450.473 340.772 380.605 340.594 350.935 690.450 520.181 740.587 430.805 350.690 350.785 220.614 350.882 440.819 400.632 42
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet0.684 350.712 640.784 270.782 370.658 330.835 230.499 270.823 240.641 180.597 330.950 350.487 350.281 300.575 480.619 640.647 520.764 330.620 330.871 570.846 280.688 26
VACNN++0.684 350.728 590.757 410.776 380.690 260.804 520.464 400.816 250.577 470.587 370.945 490.508 310.276 330.671 240.710 510.663 440.750 430.589 480.881 450.832 320.653 35
Superpoint Network0.683 380.851 170.728 550.800 310.653 360.806 500.468 370.804 310.572 480.602 300.946 460.453 510.239 530.519 650.822 300.689 370.762 360.595 450.895 360.827 340.630 43
PointContrast_LA_SEM0.683 380.757 470.784 270.786 340.639 420.824 330.408 650.775 370.604 350.541 450.934 730.532 220.269 390.552 570.777 390.645 550.793 190.640 240.913 230.824 350.671 30
VI-PointConv0.676 400.770 420.754 420.783 360.621 460.814 450.552 70.758 410.571 500.557 410.954 230.529 230.268 410.530 630.682 560.675 390.719 520.603 400.888 410.833 310.665 31
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 410.789 330.748 440.763 430.635 440.814 450.407 670.747 450.581 460.573 380.950 350.484 360.271 380.607 400.754 420.649 490.774 260.596 430.883 430.823 360.606 50
SALANet0.670 420.816 270.770 330.768 400.652 370.807 490.451 440.747 450.659 160.545 440.924 790.473 410.149 860.571 500.811 340.635 580.746 440.623 310.892 380.794 530.570 63
PointASNLpermissive0.666 430.703 670.781 290.751 470.655 350.830 260.471 360.769 390.474 760.537 470.951 310.475 400.279 320.635 320.698 550.675 390.751 420.553 620.816 730.806 450.703 20
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PointConvpermissive0.666 430.781 350.759 380.699 570.644 410.822 350.475 330.779 360.564 530.504 610.953 260.428 610.203 650.586 450.754 420.661 450.753 410.588 490.902 300.813 430.642 38
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PPCNN++permissive0.663 450.746 500.708 590.722 490.638 430.820 380.451 440.566 800.599 380.541 450.950 350.510 300.313 160.648 290.819 320.616 630.682 680.590 470.869 580.810 440.656 34
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 460.778 360.702 620.806 260.619 470.813 480.468 370.693 610.494 680.524 530.941 600.449 530.298 220.510 670.821 310.675 390.727 510.568 550.826 710.803 470.637 40
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 470.698 680.743 490.650 730.564 640.820 380.505 210.758 410.631 220.479 660.945 490.480 380.226 540.572 490.774 400.690 350.735 470.614 350.853 650.776 680.597 56
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 480.752 480.734 530.664 700.583 590.815 440.399 690.754 430.639 190.535 490.942 580.470 420.309 180.665 250.539 700.650 480.708 570.635 260.857 640.793 550.642 38
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 490.778 360.731 540.699 570.577 600.829 270.446 490.736 490.477 750.523 550.945 490.454 490.269 390.484 740.749 450.618 610.738 450.599 420.827 700.792 580.621 45
MVPNetpermissive0.641 500.831 220.715 570.671 670.590 550.781 640.394 710.679 630.642 170.553 420.937 660.462 450.256 450.649 280.406 830.626 590.691 650.666 170.877 470.792 580.608 49
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 500.776 380.703 610.721 500.557 670.826 300.451 440.672 650.563 540.483 650.943 570.425 640.162 810.644 300.726 470.659 460.709 560.572 520.875 490.786 630.559 68
PointMRNet0.640 520.717 630.701 630.692 600.576 610.801 540.467 390.716 540.563 540.459 710.953 260.429 600.169 780.581 460.854 210.605 640.710 540.550 630.894 370.793 550.575 61
FPConvpermissive0.639 530.785 340.760 370.713 550.603 500.798 560.392 720.534 850.603 360.524 530.948 410.457 470.250 470.538 610.723 490.598 680.696 630.614 350.872 540.799 480.567 65
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 540.797 310.769 340.641 780.590 550.820 380.461 410.537 840.637 200.536 480.947 430.388 750.206 620.656 260.668 590.647 520.732 490.585 500.868 590.793 550.473 87
PointSPNet0.637 550.734 560.692 700.714 540.576 610.797 570.446 490.743 470.598 390.437 760.942 580.403 710.150 850.626 360.800 360.649 490.697 620.557 600.846 670.777 670.563 66
SConv0.636 560.830 230.697 660.752 460.572 630.780 660.445 510.716 540.529 600.530 500.951 310.446 550.170 770.507 690.666 600.636 570.682 680.541 680.886 420.799 480.594 57
Supervoxel-CNN0.635 570.656 740.711 580.719 510.613 480.757 750.444 540.765 400.534 590.566 390.928 770.478 390.272 360.636 310.531 720.664 430.645 780.508 760.864 610.792 580.611 46
joint point-basedpermissive0.634 580.614 810.778 300.667 690.633 450.825 310.420 630.804 310.467 780.561 400.951 310.494 330.291 250.566 510.458 780.579 750.764 330.559 590.838 680.814 410.598 55
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 590.731 570.688 730.675 640.591 540.784 630.444 540.565 810.610 300.492 630.949 390.456 480.254 460.587 430.706 520.599 670.665 740.612 380.868 590.791 620.579 60
3DSM_DMMF0.631 600.626 780.745 470.801 300.607 490.751 760.506 200.729 520.565 520.491 640.866 930.434 560.197 690.595 410.630 630.709 280.705 590.560 570.875 490.740 780.491 82
PointNet2-SFPN0.631 600.771 400.692 700.672 650.524 720.837 200.440 570.706 590.538 580.446 730.944 550.421 660.219 570.552 570.751 440.591 710.737 460.543 670.901 320.768 700.557 69
APCF-Net0.631 600.742 530.687 750.672 650.557 670.792 610.408 650.665 660.545 560.508 580.952 300.428 610.186 720.634 330.702 530.620 600.706 580.555 610.873 520.798 500.581 59
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 630.604 830.741 510.766 420.590 550.747 770.501 230.734 500.503 670.527 510.919 830.454 490.323 120.550 600.420 820.678 380.688 660.544 650.896 350.795 520.627 44
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 640.800 300.625 850.719 510.545 700.806 500.445 510.597 750.448 820.519 560.938 650.481 370.328 100.489 730.499 770.657 470.759 380.592 460.881 450.797 510.634 41
SegGroup_sempermissive0.627 650.818 260.747 460.701 560.602 510.764 720.385 760.629 720.490 710.508 580.931 760.409 690.201 660.564 520.725 480.618 610.692 640.539 690.873 520.794 530.548 72
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 660.830 230.694 680.757 440.563 650.772 700.448 480.647 690.520 620.509 570.949 390.431 590.191 700.496 710.614 650.647 520.672 720.535 710.876 480.783 640.571 62
HPEIN0.618 670.729 580.668 760.647 750.597 530.766 710.414 640.680 620.520 620.525 520.946 460.432 570.215 590.493 720.599 660.638 560.617 830.570 530.897 340.806 450.605 52
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-GCNpermissive0.610 680.858 160.772 320.489 900.532 710.792 610.404 680.643 710.570 510.507 600.935 690.414 680.046 950.510 670.702 530.602 660.705 590.549 640.859 630.773 690.534 75
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 690.760 450.667 770.649 740.521 730.793 590.457 430.648 680.528 610.434 780.947 430.401 720.153 840.454 760.721 500.648 510.717 530.536 700.904 280.765 710.485 83
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 700.634 770.743 490.697 590.601 520.781 640.437 590.585 780.493 690.446 730.933 740.394 730.011 970.654 270.661 620.603 650.733 480.526 720.832 690.761 730.480 84
dtc_net0.596 710.683 690.725 560.715 530.549 690.803 530.444 540.647 690.493 690.495 620.941 600.409 690.000 990.424 810.544 690.598 680.703 610.522 730.912 240.792 580.520 78
LAP-D0.594 720.720 610.692 700.637 790.456 820.773 690.391 740.730 510.587 420.445 750.940 630.381 760.288 260.434 790.453 800.591 710.649 760.581 510.777 770.749 770.610 48
DPC0.592 730.720 610.700 640.602 830.480 780.762 740.380 770.713 570.585 450.437 760.940 630.369 780.288 260.434 790.509 760.590 730.639 810.567 560.772 780.755 750.592 58
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 740.766 440.659 800.683 620.470 810.740 790.387 750.620 740.490 710.476 670.922 810.355 810.245 510.511 660.511 750.571 760.643 790.493 800.872 540.762 720.600 54
ROSMRF0.580 750.772 390.707 600.681 630.563 650.764 720.362 790.515 860.465 790.465 700.936 680.427 630.207 610.438 770.577 670.536 790.675 710.486 810.723 840.779 650.524 77
SD-DETR0.576 760.746 500.609 890.445 940.517 740.643 900.366 780.714 560.456 800.468 690.870 920.432 570.264 420.558 550.674 570.586 740.688 660.482 820.739 820.733 800.537 74
SQN_0.1%0.569 770.676 710.696 670.657 710.497 750.779 670.424 610.548 820.515 640.376 830.902 900.422 650.357 40.379 840.456 790.596 700.659 750.544 650.685 870.665 910.556 70
TextureNetpermissive0.566 780.672 730.664 780.671 670.494 760.719 800.445 510.678 640.411 880.396 810.935 690.356 800.225 550.412 820.535 710.565 770.636 820.464 840.794 760.680 880.568 64
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
DVVNet0.562 790.648 750.700 640.770 390.586 580.687 840.333 830.650 670.514 650.475 680.906 870.359 790.223 560.340 860.442 810.422 900.668 730.501 770.708 850.779 650.534 75
Pointnet++ & Featurepermissive0.557 800.735 550.661 790.686 610.491 770.744 780.392 720.539 830.451 810.375 840.946 460.376 770.205 630.403 830.356 860.553 780.643 790.497 780.824 720.756 740.515 79
GMLPs0.538 810.495 910.693 690.647 750.471 800.793 590.300 860.477 870.505 660.358 850.903 890.327 840.081 920.472 750.529 730.448 880.710 540.509 740.746 800.737 790.554 71
PanopticFusion-label0.529 820.491 920.688 730.604 820.386 870.632 910.225 960.705 600.434 850.293 910.815 940.348 820.241 520.499 700.669 580.507 810.649 760.442 900.796 750.602 940.561 67
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_weak0.516 830.676 710.591 920.609 800.442 830.774 680.335 820.597 750.422 870.357 860.932 750.341 830.094 910.298 880.528 740.473 860.676 700.495 790.602 930.721 830.349 94
Online SegFusion0.515 840.607 820.644 830.579 850.434 840.630 920.353 800.628 730.440 830.410 790.762 970.307 860.167 790.520 640.403 840.516 800.565 860.447 880.678 880.701 850.514 80
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, FTSDF0.501 850.558 870.608 900.424 960.478 790.690 830.246 920.586 770.468 770.450 720.911 850.394 730.160 820.438 770.212 930.432 890.541 910.475 830.742 810.727 810.477 85
PCNN0.498 860.559 860.644 830.560 870.420 860.711 820.229 940.414 880.436 840.352 870.941 600.324 850.155 830.238 930.387 850.493 820.529 920.509 740.813 740.751 760.504 81
3DMV0.484 870.484 930.538 940.643 770.424 850.606 950.310 840.574 790.433 860.378 820.796 950.301 870.214 600.537 620.208 940.472 870.507 950.413 930.693 860.602 940.539 73
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 880.577 850.611 880.356 980.321 950.715 810.299 880.376 920.328 950.319 890.944 550.285 890.164 800.216 960.229 910.484 840.545 900.456 860.755 790.709 840.475 86
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 890.679 700.604 910.578 860.380 880.682 850.291 890.106 980.483 740.258 960.920 820.258 930.025 960.231 950.325 870.480 850.560 880.463 850.725 830.666 900.231 98
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 900.474 940.623 860.463 920.366 900.651 880.310 840.389 910.349 930.330 880.937 660.271 910.126 880.285 890.224 920.350 950.577 850.445 890.625 910.723 820.394 90
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
PNET20.442 910.548 880.548 930.597 840.363 910.628 930.300 860.292 930.374 900.307 900.881 910.268 920.186 720.238 930.204 950.407 910.506 960.449 870.667 890.620 930.462 88
SurfaceConvPF0.442 910.505 900.622 870.380 970.342 930.654 870.227 950.397 900.367 910.276 930.924 790.240 940.198 680.359 850.262 890.366 920.581 840.435 910.640 900.668 890.398 89
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 930.437 960.646 820.474 910.369 890.645 890.353 800.258 950.282 970.279 920.918 840.298 880.147 870.283 900.294 880.487 830.562 870.427 920.619 920.633 920.352 93
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 940.525 890.647 810.522 880.324 940.488 980.077 990.712 580.353 920.401 800.636 990.281 900.176 750.340 860.565 680.175 990.551 890.398 940.370 990.602 940.361 92
SPLAT Netcopyleft0.393 950.472 950.511 950.606 810.311 960.656 860.245 930.405 890.328 950.197 970.927 780.227 960.000 990.001 1000.249 900.271 980.510 930.383 960.593 940.699 860.267 96
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+FTSDF0.383 960.297 980.491 960.432 950.358 920.612 940.274 900.116 970.411 880.265 940.904 880.229 950.079 930.250 910.185 960.320 960.510 930.385 950.548 950.597 970.394 90
PointNet++permissive0.339 970.584 840.478 970.458 930.256 980.360 990.250 910.247 960.278 980.261 950.677 980.183 970.117 890.212 970.145 980.364 930.346 990.232 990.548 950.523 980.252 97
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 980.353 970.290 990.278 990.166 990.553 960.169 980.286 940.147 990.148 990.908 860.182 980.064 940.023 990.018 1000.354 940.363 970.345 970.546 970.685 870.278 95
ScanNetpermissive0.306 990.203 990.366 980.501 890.311 960.524 970.211 970.002 1000.342 940.189 980.786 960.145 990.102 900.245 920.152 970.318 970.348 980.300 980.460 980.437 990.182 99
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 1000.000 1000.041 1000.172 1000.030 1000.062 1000.001 1000.035 990.004 1000.051 1000.143 1000.019 1000.003 980.041 980.050 990.003 1000.054 1000.018 1000.005 1000.264 1000.082 100


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mask3D0.780 11.000 10.786 260.716 240.696 50.885 50.500 30.714 180.810 20.672 30.715 40.679 60.809 21.000 10.831 20.833 80.787 41.000 10.602 6
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023
Queryformer0.775 21.000 10.933 10.601 330.754 10.886 40.564 20.535 360.666 70.664 40.716 30.639 100.820 11.000 10.844 10.897 20.804 21.000 10.622 2
SPFormerpermissive0.770 30.903 380.903 20.806 130.609 160.886 30.568 10.815 60.705 50.711 10.655 50.652 90.685 101.000 10.789 40.809 140.776 61.000 10.583 11
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral]
SoftGroup++0.769 41.000 10.803 190.937 10.684 60.865 70.213 190.870 20.664 80.571 90.758 10.702 40.807 31.000 10.653 150.902 10.792 31.000 10.626 1
SoftGrouppermissive0.761 51.000 10.808 160.845 80.716 20.862 90.243 160.824 40.655 100.620 50.734 20.699 50.791 50.981 240.716 70.844 50.769 71.000 10.594 9
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
TD3D0.751 61.000 10.774 270.867 70.621 120.934 10.404 60.706 190.812 10.605 70.633 100.626 110.690 91.000 10.640 170.820 110.777 51.000 10.612 4
PBNetpermissive0.747 71.000 10.818 120.837 100.713 30.844 110.457 50.647 260.711 40.614 60.617 120.657 80.650 121.000 10.692 90.822 100.765 91.000 10.595 8
GraphCut0.732 81.000 10.788 240.724 230.642 100.859 100.248 150.787 110.618 130.596 80.653 70.722 20.583 301.000 10.766 50.861 30.825 11.000 10.504 22
IPCA-Inst0.731 91.000 10.788 250.884 60.698 40.788 260.252 140.760 130.646 110.511 170.637 90.665 70.804 41.000 10.644 160.778 170.747 111.000 10.561 15
TopoSeg0.725 101.000 10.806 180.933 20.668 80.758 290.272 130.734 170.630 120.549 130.654 60.606 120.697 80.966 260.612 210.839 60.754 101.000 10.573 12
DKNet0.718 111.000 10.814 130.782 160.619 130.872 60.224 170.751 150.569 170.677 20.585 150.724 10.633 220.981 240.515 310.819 120.736 121.000 10.617 3
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022
SSEC0.707 121.000 10.850 60.924 30.648 90.747 320.162 210.862 30.572 160.520 150.624 110.549 150.649 201.000 10.560 260.706 320.768 81.000 10.591 10
HAISpermissive0.699 131.000 10.849 70.820 110.675 70.808 200.279 110.757 140.465 220.517 160.596 130.559 140.600 241.000 10.654 140.767 190.676 160.994 340.560 16
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 141.000 10.697 430.888 50.556 220.803 210.387 70.626 280.417 260.556 120.585 160.702 30.600 241.000 10.824 30.720 310.692 141.000 10.509 21
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
DualGroup0.694 151.000 10.799 210.811 120.622 110.817 150.376 80.805 90.590 150.487 200.568 190.525 190.650 120.835 370.600 220.829 90.655 181.000 10.526 18
SphereSeg0.680 161.000 10.856 50.744 220.618 140.893 20.151 220.651 250.713 30.537 140.579 180.430 280.651 111.000 10.389 400.744 260.697 130.991 360.601 7
Box2Mask0.677 171.000 10.847 80.771 180.509 300.816 160.277 120.558 350.482 190.562 110.640 80.448 240.700 61.000 10.666 100.852 40.578 300.997 290.488 26
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
OccuSeg+instance0.672 181.000 10.758 350.682 270.576 200.842 120.477 40.504 400.524 180.567 100.585 170.451 230.557 311.000 10.751 60.797 150.563 331.000 10.467 30
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 191.000 10.822 110.764 210.616 150.815 170.139 260.694 210.597 140.459 240.566 200.599 130.600 240.516 470.715 80.819 130.635 221.000 10.603 5
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 201.000 10.760 330.667 290.581 180.863 80.323 90.655 240.477 200.473 220.549 220.432 270.650 121.000 10.655 130.738 270.585 290.944 400.472 29
CSC-Pretrained0.648 211.000 10.810 140.768 190.523 280.813 180.143 250.819 50.389 290.422 320.511 260.443 250.650 121.000 10.624 190.732 280.634 231.000 10.375 37
PE0.645 221.000 10.773 290.798 150.538 240.786 270.088 330.799 100.350 330.435 310.547 230.545 160.646 210.933 270.562 250.761 220.556 380.997 290.501 24
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 231.000 10.758 340.582 390.539 230.826 140.046 370.765 120.372 310.436 300.588 140.539 180.650 121.000 10.577 230.750 240.653 200.997 290.495 25
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022
Dyco3Dcopyleft0.641 241.000 10.841 90.893 40.531 260.802 220.115 300.588 330.448 230.438 280.537 250.430 290.550 320.857 290.534 290.764 210.657 170.987 370.568 13
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 251.000 10.895 40.800 140.480 340.676 360.144 240.737 160.354 320.447 250.400 380.365 340.700 61.000 10.569 240.836 70.599 251.000 10.473 28
PointGroup0.636 261.000 10.765 300.624 310.505 320.797 230.116 290.696 200.384 300.441 260.559 210.476 210.596 271.000 10.666 100.756 230.556 370.997 290.513 20
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]
DD-UNet+Group0.635 270.667 390.797 230.714 250.562 210.774 280.146 230.810 80.429 250.476 210.546 240.399 310.633 221.000 10.632 180.722 300.609 241.000 10.514 19
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
DENet0.629 281.000 10.797 220.608 320.589 170.627 400.219 180.882 10.310 350.402 370.383 400.396 320.650 121.000 10.663 120.543 480.691 151.000 10.568 14
3D-MPA0.611 291.000 10.833 100.765 200.526 270.756 300.136 280.588 330.470 210.438 290.432 350.358 350.650 120.857 290.429 360.765 200.557 361.000 10.430 32
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
OSIS0.605 301.000 10.801 200.599 340.535 250.728 340.286 100.436 440.679 60.491 180.433 330.256 370.404 440.857 290.620 200.724 290.510 421.000 10.539 17
AOIA0.601 311.000 10.761 320.687 260.485 330.828 130.008 430.663 230.405 280.405 360.425 360.490 200.596 270.714 400.553 280.779 160.597 260.992 350.424 34
PCJC0.578 321.000 10.810 150.583 380.449 370.813 190.042 380.603 310.341 340.490 190.465 300.410 300.650 120.835 370.264 460.694 360.561 340.889 440.504 23
SSEN0.575 331.000 10.761 310.473 410.477 350.795 240.066 340.529 370.658 90.460 230.461 310.380 330.331 460.859 280.401 390.692 380.653 191.000 10.348 39
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 340.528 490.708 420.626 300.580 190.745 330.063 350.627 270.240 390.400 380.497 270.464 220.515 331.000 10.475 330.745 250.571 311.000 10.429 33
NeuralBF0.555 350.667 390.896 30.843 90.517 290.751 310.029 390.519 380.414 270.439 270.465 290.000 550.484 350.857 290.287 440.693 370.651 211.000 10.485 27
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
MTML0.549 361.000 10.807 170.588 370.327 420.647 380.004 450.815 70.180 410.418 330.364 420.182 400.445 381.000 10.442 350.688 390.571 321.000 10.396 35
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
One_Thing_One_Clickpermissive0.529 370.667 390.718 380.777 170.399 380.683 350.000 480.669 220.138 440.391 390.374 410.539 170.360 450.641 440.556 270.774 180.593 270.997 290.251 44
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Sparse R-CNN0.515 381.000 10.538 500.282 440.468 360.790 250.173 200.345 460.429 240.413 350.484 280.176 410.595 290.591 450.522 300.668 400.476 430.986 380.327 40
Occipital-SCS0.512 391.000 10.716 390.509 400.506 310.611 410.092 320.602 320.177 420.346 420.383 390.165 420.442 390.850 360.386 410.618 440.543 390.889 440.389 36
3D-BoNet0.488 401.000 10.672 450.590 360.301 440.484 510.098 310.620 290.306 360.341 430.259 460.125 440.434 410.796 390.402 380.499 500.513 410.909 430.439 31
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
PanopticFusion-inst0.478 410.667 390.712 410.595 350.259 470.550 470.000 480.613 300.175 430.250 480.434 320.437 260.411 430.857 290.485 320.591 470.267 530.944 400.359 38
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS0.470 420.667 390.685 440.677 280.372 400.562 450.000 480.482 410.244 380.316 450.298 430.052 500.442 400.857 290.267 450.702 330.559 351.000 10.287 42
SALoss-ResNet0.459 431.000 10.737 370.159 540.259 460.587 430.138 270.475 420.217 400.416 340.408 370.128 430.315 470.714 400.411 370.536 490.590 280.873 470.304 41
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.447 440.528 490.555 480.381 420.382 390.633 390.002 460.509 390.260 370.361 410.432 340.327 360.451 370.571 460.367 420.639 420.386 440.980 390.276 43
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 450.667 390.773 280.185 510.317 430.656 370.000 480.407 450.134 450.381 400.267 450.217 390.476 360.714 400.452 340.629 430.514 401.000 10.222 47
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
3D-SISpermissive0.382 461.000 10.432 520.245 460.190 480.577 440.013 420.263 480.033 510.320 440.240 470.075 460.422 420.857 290.117 500.699 340.271 520.883 460.235 46
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 470.667 390.542 490.264 450.157 510.550 460.000 480.205 510.009 520.270 470.218 480.075 460.500 340.688 430.007 560.698 350.301 490.459 530.200 48
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 480.667 390.715 400.233 470.189 490.479 520.008 430.218 490.067 500.201 500.173 490.107 450.123 520.438 480.150 480.615 450.355 450.916 420.093 55
R-PointNet0.306 490.500 510.405 530.311 430.348 410.589 420.054 360.068 540.126 460.283 460.290 440.028 510.219 500.214 510.331 430.396 540.275 500.821 490.245 45
Region-18class0.284 500.250 550.751 360.228 490.270 450.521 480.000 480.468 430.008 540.205 490.127 500.000 550.068 540.070 540.262 470.652 410.323 470.740 500.173 49
SemRegionNet-20cls0.250 510.333 520.613 460.229 480.163 500.493 490.000 480.304 470.107 470.147 520.100 510.052 490.231 480.119 520.039 520.445 520.325 460.654 510.141 51
tmp0.248 520.667 390.437 510.188 500.153 520.491 500.000 480.208 500.094 490.153 510.099 520.057 480.217 510.119 520.039 520.466 510.302 480.640 520.140 52
3D-BEVIS0.248 520.667 390.566 470.076 550.035 560.394 540.027 410.035 550.098 480.099 540.030 550.025 520.098 530.375 500.126 490.604 460.181 540.854 480.171 50
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
ASIS0.199 540.333 520.253 550.167 530.140 530.438 530.000 480.177 520.008 530.121 530.069 530.004 540.231 490.429 490.036 540.445 530.273 510.333 550.119 54
Sgpn_scannet0.143 550.208 560.390 540.169 520.065 540.275 550.029 400.069 530.000 550.087 550.043 540.014 530.027 560.000 550.112 510.351 550.168 550.438 540.138 53
MaskRCNN 2d->3d Proj0.058 560.333 520.002 560.000 560.053 550.002 560.002 470.021 560.000 550.045 560.024 560.238 380.065 550.000 550.014 550.107 560.020 560.110 560.006 56


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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 10.512 10.422 150.898 10.723 10.714 10.954 20.454 10.509 10.773 10.895 10.756 10.820 10.653 10.935 10.891 10.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2Dcopyleft0.670 20.822 30.795 30.836 20.659 20.481 20.451 110.769 30.656 30.567 30.931 30.395 40.390 40.700 30.534 30.689 90.770 20.574 30.865 60.831 30.675 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 200.648 30.463 30.549 20.742 60.676 20.628 20.961 10.420 20.379 50.684 60.381 150.732 20.723 30.599 20.827 130.851 20.634 6
CMX0.613 40.681 70.725 90.502 120.634 50.297 150.478 90.830 20.651 40.537 60.924 40.375 50.315 120.686 50.451 120.714 40.543 180.504 50.894 40.823 40.688 3
DMMF_3d0.605 50.651 80.744 70.782 30.637 40.387 40.536 30.732 70.590 60.540 50.856 180.359 90.306 130.596 110.539 20.627 180.706 40.497 70.785 180.757 160.476 19
MCA-Net0.595 60.533 170.756 60.746 40.590 80.334 70.506 60.670 120.587 70.500 100.905 80.366 80.352 80.601 100.506 60.669 150.648 70.501 60.839 120.769 120.516 18
RFBNet0.592 70.616 90.758 50.659 50.581 90.330 80.469 100.655 150.543 120.524 70.924 40.355 100.336 100.572 140.479 80.671 130.648 70.480 90.814 160.814 50.614 9
FAN_NV_RVC0.586 80.510 180.764 40.079 230.620 70.330 80.494 70.753 40.573 80.556 40.884 130.405 30.303 140.718 20.452 110.672 120.658 50.509 40.898 30.813 60.727 2
DCRedNet0.583 90.682 60.723 100.542 110.510 170.310 120.451 110.668 130.549 110.520 80.920 60.375 50.446 20.528 170.417 130.670 140.577 150.478 100.862 70.806 70.628 8
MIX6D_RVC0.582 100.695 40.687 140.225 180.632 60.328 100.550 10.748 50.623 50.494 130.890 110.350 120.254 200.688 40.454 100.716 30.597 140.489 80.881 50.768 130.575 12
SSMAcopyleft0.577 110.695 40.716 120.439 140.563 110.314 110.444 130.719 80.551 100.503 90.887 120.346 130.348 90.603 90.353 170.709 50.600 120.457 120.901 20.786 80.599 11
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE0.566 120.569 160.686 160.435 150.524 140.294 160.421 160.712 90.543 120.463 150.872 140.320 140.363 70.611 80.477 90.686 100.627 90.443 150.862 70.775 110.639 5
EMSAFormer0.564 130.581 130.736 80.564 100.546 130.219 200.517 40.675 110.486 170.427 190.904 90.352 110.320 110.589 120.528 40.708 60.464 210.413 190.847 110.786 80.611 10
SN_RN152pyrx8_RVCcopyleft0.546 140.572 140.663 180.638 70.518 150.298 140.366 210.633 180.510 150.446 170.864 160.296 170.267 170.542 160.346 180.704 70.575 160.431 160.853 100.766 140.630 7
UDSSEG_RVC0.545 150.610 110.661 190.588 80.556 120.268 180.482 80.642 170.572 90.475 140.836 200.312 150.367 60.630 70.189 200.639 170.495 200.452 130.826 140.756 170.541 14
segfomer with 6d0.542 160.594 120.687 140.146 210.579 100.308 130.515 50.703 100.472 180.498 110.868 150.369 70.282 150.589 120.390 140.701 80.556 170.416 180.860 90.759 150.539 16
FuseNetpermissive0.535 170.570 150.681 170.182 190.512 160.290 170.431 140.659 140.504 160.495 120.903 100.308 160.428 30.523 180.365 160.676 110.621 110.470 110.762 190.779 100.541 14
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 180.613 100.722 110.418 160.358 230.337 60.370 200.479 210.443 190.368 210.907 70.207 200.213 220.464 210.525 50.618 190.657 60.450 140.788 170.721 200.408 22
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 190.481 210.612 200.579 90.456 190.343 50.384 180.623 190.525 140.381 200.845 190.254 190.264 190.557 150.182 210.581 210.598 130.429 170.760 200.661 220.446 21
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 200.505 190.709 130.092 220.427 200.241 190.411 170.654 160.385 230.457 160.861 170.053 230.279 160.503 190.481 70.645 160.626 100.365 210.748 210.725 190.529 17
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 210.490 200.581 210.289 170.507 180.067 230.379 190.610 200.417 210.435 180.822 220.278 180.267 170.503 190.228 190.616 200.533 190.375 200.820 150.729 180.560 13
Enet (reimpl)0.376 220.264 230.452 230.452 130.365 210.181 210.143 230.456 220.409 220.346 220.769 230.164 210.218 210.359 220.123 230.403 230.381 230.313 230.571 220.685 210.472 20
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
ScanNet (2d proj)permissive0.330 230.293 220.521 220.657 60.361 220.161 220.250 220.004 230.440 200.183 230.836 200.125 220.060 230.319 230.132 220.417 220.412 220.344 220.541 230.427 230.109 23
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
DMMF0.003 240.000 240.005 240.000 240.000 240.037 240.001 240.000 240.001 240.005 240.003 240.000 240.000 240.000 240.000 240.000 240.002 240.001 240.000 240.006 240.000 24


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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
UniDet_RVC0.205 10.381 10.323 10.037 10.226 10.177 10.063 10.277 10.120 10.067 10.131 10.074 20.317 10.080 10.235 10.289 10.141 10.678 10.080 1
MaskRCNN_ScanNetpermissive0.119 20.129 20.212 20.002 20.112 20.148 20.014 20.205 20.044 20.066 20.078 20.095 10.142 20.030 20.128 20.139 20.080 20.459 20.057 2
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17


This table lists the benchmark results for the scene type classification scenario.




Method Infoavg recallapartmentbathroombedroom / hotelbookstore / libraryconference roomcopy/mail roomhallwaykitchenlaundry roomliving room / loungemiscofficestorage / basement / garage
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
multi-taskpermissive0.700 10.500 11.000 10.882 20.500 21.000 11.000 10.500 21.000 11.000 10.778 10.000 20.938 10.000 2
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE0.691 20.500 10.938 20.824 31.000 11.000 10.500 21.000 10.857 20.500 20.556 30.000 20.812 20.500 1
SE-ResNeXt-SSMA0.498 30.000 40.812 30.941 10.500 20.500 30.500 20.500 20.429 40.500 20.667 20.500 10.625 30.000 2
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 40.250 30.812 30.529 40.500 20.500 30.000 40.500 20.571 30.000 40.556 30.000 20.375 40.000 2