The 3D semantic instance prediction task involves detecting and segmenting the object in an 3D scan mesh.

Evaluation and metrics

Similarly to the ScanNet benchmark in ScanNet200 our evaluation ranks all methods according to the average precision for each class. We report the mean average precision AP at overlap 0.25 (AP 25%), overlap 0.5 (AP 50%), and over overlaps in the range [0.5:0.95:0.05] (AP) for all 200 categories. Note that multiple predictions of the same ground truth instance are penalized as false positives.



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%alarm clockarmchairbackpackbagballbarbasketbathroom cabinetbathroom counterbathroom stallbathroom stall doorbathroom vanitybathtubbedbenchbicyclebinblackboardblanketblindsboardbookbookshelfbottlebowlboxbroombucketbulletin boardcabinetcalendarcandlecartcase of water bottlescd caseceilingceiling lightchairclockclosetcloset doorcloset rodcloset wallclothesclothes dryercoat rackcoffee kettlecoffee makercoffee tablecolumncomputer towercontainercopiercouchcountercratecupcurtaincushiondecorationdeskdining tabledish rackdishwasherdividerdoordoorframedresserdumbbelldustpanend tablefanfile cabinetfire alarmfire extinguisherfireplacefolded chairfurnitureguitarguitar casehair dryerhandicap barhatheadphonesironing boardjacketkeyboardkeyboard pianokitchen cabinetkitchen counterladderlamplaptoplaundry basketlaundry detergentlaundry hamperledgelightlight switchluggagemachinemailboxmatmattressmicrowavemini fridgemirrormonitormousemusic standnightstandobjectoffice chairottomanovenpaperpaper bagpaper cutterpaper towel dispenserpaper towel rollpersonpianopicturepillarpillowpipeplantplateplungerposterpotted plantpower outletpower stripprinterprojectorprojector screenpurserackradiatorrailrange hoodrecycling binrefrigeratorscaleseatshelfshoeshowershower curtainshower curtain rodshower doorshower floorshower headshower wallsignsinksoap dishsoap dispensersofa chairspeakerstair railstairsstandstoolstorage binstorage containerstorage organizerstovestructurestuffed animalsuitcasetabletelephonetissue boxtoastertoaster oventoilettoilet papertoilet paper dispensertoilet paper holdertoilet seat cover dispensertoweltrash bintrash cantraytubetvtv standvacuum cleanerventwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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Mask3D Scannet2000.388 10.542 10.357 10.237 10.610 10.091 10.125 40.000 10.000 10.000 10.065 20.668 10.451 11.000 10.955 10.640 10.500 10.039 10.125 20.063 10.409 10.311 10.291 10.609 20.266 10.000 10.163 10.000 10.008 10.044 10.496 11.000 10.000 10.018 10.000 10.756 10.573 10.808 10.000 10.010 10.042 20.130 20.552 10.042 10.000 11.000 10.725 30.750 10.883 11.000 10.832 30.024 10.107 10.614 20.226 10.250 10.628 10.792 10.677 20.400 10.741 10.278 10.511 10.077 40.111 10.313 10.715 10.302 10.017 20.200 10.000 10.188 10.000 10.178 10.736 11.000 10.615 10.514 10.409 10.380 40.600 10.000 10.000 10.400 10.013 10.254 10.381 10.000 10.123 30.400 10.839 10.258 10.463 10.926 10.265 10.000 10.857 10.099 10.021 10.500 10.027 10.028 11.000 10.502 40.016 10.076 30.500 10.612 10.578 10.005 10.597 10.194 10.497 10.000 10.500 10.000 10.323 30.000 11.000 10.000 10.748 10.708 20.050 30.890 11.000 10.008 10.151 20.301 11.000 11.000 10.792 20.945 11.000 10.511 10.004 10.753 10.776 10.287 10.020 10.003 30.974 20.033 10.412 40.000 10.000 10.000 10.667 10.000 10.000 10.491 10.676 10.352 10.335 10.060 10.822 40.527 11.000 10.517 10.606 10.853 10.000 10.004 10.806 11.000 10.727 10.000 10.042 10.739 10.000 10.399 20.391 10.504 10.591 10.571 1
LGround Inst.permissive0.246 20.413 20.170 20.130 20.455 40.003 40.500 10.000 10.000 10.000 10.017 30.333 30.111 41.000 10.681 30.400 20.000 20.000 21.000 10.003 40.000 20.167 20.190 20.637 10.067 20.000 10.081 20.000 10.000 20.000 20.264 30.000 20.000 10.000 20.000 10.387 30.031 40.754 20.000 10.000 20.151 10.135 10.056 30.000 20.000 10.582 30.589 40.500 20.815 21.000 10.903 10.000 20.097 20.588 30.000 20.000 20.234 20.000 20.500 30.400 10.682 30.156 20.159 30.750 10.046 20.125 30.660 20.000 20.200 10.000 40.000 10.000 20.000 10.164 20.402 20.500 20.373 20.025 20.143 40.426 20.317 20.000 10.000 10.000 20.000 20.063 20.000 20.000 10.000 40.000 30.575 30.250 20.241 20.772 20.000 20.000 10.653 30.034 20.000 20.000 20.000 20.000 21.000 10.561 30.000 20.100 20.500 10.541 40.452 20.000 20.581 20.000 20.364 20.000 10.000 20.000 10.571 10.000 10.000 20.000 10.568 40.511 30.167 20.857 20.000 20.000 20.164 10.112 20.000 40.530 41.000 10.286 20.000 20.125 20.000 20.464 40.706 20.208 30.000 20.125 10.744 30.000 20.500 10.000 10.000 10.000 10.511 20.000 10.000 10.344 20.541 20.068 20.333 20.000 21.000 10.196 30.533 30.318 20.000 30.748 30.000 10.000 20.690 21.000 10.400 30.000 10.000 20.667 20.000 10.333 30.333 20.270 30.399 20.083 3
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild.
Minkowski 34D Inst.permissive0.203 40.369 30.134 40.078 40.479 30.003 30.500 10.000 10.000 10.000 10.100 10.371 20.300 20.667 30.746 20.400 20.000 20.000 20.000 30.031 20.000 20.074 30.165 30.413 40.000 30.000 10.070 30.000 10.000 20.000 20.221 40.000 20.000 10.000 20.000 10.372 40.070 20.706 30.000 10.000 20.000 40.123 30.033 40.000 20.000 10.422 40.732 20.000 30.778 41.000 10.845 20.000 20.090 30.636 10.000 20.000 20.158 30.000 20.250 40.050 40.693 20.123 30.051 40.385 30.009 30.118 40.406 40.000 20.000 30.200 10.000 10.000 20.000 10.133 30.307 40.500 20.251 30.000 30.281 20.402 30.317 20.000 10.000 10.000 20.000 20.060 30.000 20.000 10.396 10.200 20.669 20.021 30.218 40.720 40.000 20.000 10.696 20.025 30.000 20.000 20.000 20.000 20.125 40.596 10.000 20.191 10.500 10.595 20.369 30.000 20.500 30.000 20.143 40.000 10.000 20.000 10.226 40.000 10.000 20.000 10.701 20.511 30.000 40.851 30.000 20.000 20.150 30.052 40.100 30.981 20.500 30.286 20.000 20.000 40.000 20.545 30.522 40.250 20.000 20.000 40.522 40.000 20.500 10.000 10.000 10.000 10.282 40.000 10.000 10.178 40.382 30.018 40.056 30.000 20.997 20.107 40.677 20.313 30.000 30.726 40.000 10.000 20.583 40.903 30.200 40.000 10.000 20.333 30.000 10.442 10.083 30.109 40.387 30.000 4
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
CSC-Pretrain Inst.permissive0.209 30.361 40.157 30.085 30.506 20.007 20.500 10.000 10.000 10.000 10.000 40.093 40.221 30.667 30.524 40.400 20.000 20.000 20.000 30.004 30.000 20.000 40.109 40.589 30.000 30.000 10.059 40.000 10.000 20.000 20.322 20.000 20.000 10.000 20.000 10.405 20.055 30.700 40.000 10.000 20.028 30.091 40.083 20.000 20.000 10.667 20.768 10.000 30.807 31.000 10.776 40.000 20.000 40.340 40.000 20.000 20.103 40.000 20.750 10.200 30.634 40.053 40.246 20.677 20.006 40.198 20.432 30.000 20.000 30.050 30.000 10.000 20.000 10.111 40.356 30.500 20.188 40.000 30.220 30.448 10.050 40.000 10.000 10.000 20.000 20.032 40.000 20.000 10.396 10.000 30.573 40.000 40.228 30.747 30.000 20.000 10.573 40.021 40.000 20.000 20.000 20.000 20.500 30.573 20.000 20.000 40.125 40.592 30.364 40.000 20.450 40.000 20.364 20.000 10.000 20.000 10.340 20.000 10.000 20.000 10.610 30.833 10.221 10.702 40.000 20.000 20.135 40.094 30.125 20.571 30.500 30.143 40.000 20.125 20.000 20.618 20.667 30.115 40.000 20.125 11.000 10.000 20.500 10.000 10.000 10.000 10.502 30.000 10.000 10.312 30.248 40.050 30.000 40.000 20.997 20.420 20.500 40.149 40.451 20.748 20.000 10.000 20.636 30.667 40.600 20.000 10.000 20.278 40.000 10.333 30.000 40.294 20.381 40.110 2
Ji Hou, Benjamin Graham, Matthias Nie├čner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021