The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).



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




Method Infoavg iouhead ioucommon ioutail ioualarm 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 extinguisherfireplacefloorfolded 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 cleanerventwallwardrobewashing machinewater bottlewater coolerwater pitcherwhiteboardwindowwindowsill
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LGroundpermissive0.272 10.485 10.184 10.106 10.476 10.077 20.218 10.000 10.000 10.000 10.547 10.295 10.540 10.746 20.745 10.058 20.112 30.005 10.658 20.077 30.000 20.322 10.178 30.512 20.190 10.199 10.277 10.000 10.000 10.173 10.399 10.000 10.000 10.039 20.000 20.858 10.085 20.676 10.002 10.103 10.498 10.323 10.703 10.000 10.000 10.296 10.549 20.216 10.702 10.768 10.718 10.028 20.092 20.786 20.000 10.000 20.453 20.022 20.251 30.252 10.572 10.348 10.321 10.514 10.063 20.279 20.552 10.000 20.019 20.932 10.132 20.000 10.000 10.000 30.156 30.457 10.623 10.518 10.265 20.358 20.381 10.395 10.000 10.000 10.127 30.012 30.051 10.000 10.000 20.886 20.014 10.437 30.179 10.244 10.826 10.000 10.000 10.599 10.136 10.085 20.000 20.000 10.000 10.565 10.612 10.143 10.207 10.566 10.232 20.446 10.127 10.708 20.000 20.384 10.000 10.000 10.000 10.402 10.000 10.059 10.000 10.525 30.566 10.229 20.659 20.000 10.000 10.265 10.446 10.147 20.720 30.597 20.066 20.000 10.187 10.000 10.726 10.467 30.134 30.000 20.413 30.629 20.000 10.363 20.055 30.022 20.000 10.626 10.000 20.000 10.323 20.479 30.154 20.117 10.028 20.901 10.243 10.415 30.295 30.143 30.610 20.000 10.000 20.777 10.397 30.324 20.000 10.778 10.179 10.702 20.000 10.274 30.404 10.233 10.622 10.398 2
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
CSC-Pretrainpermissive0.249 30.455 30.171 20.079 30.418 20.059 30.186 20.000 10.000 10.000 10.335 30.250 20.316 20.766 10.697 30.142 10.170 10.003 20.553 30.112 10.097 10.201 30.186 20.476 30.081 20.000 20.216 30.000 10.000 10.001 30.314 30.000 10.000 10.055 10.000 20.832 30.094 10.659 20.002 10.076 20.310 30.293 30.664 30.000 10.000 10.175 30.634 10.130 20.552 30.686 30.700 30.076 10.110 10.770 30.000 10.000 20.430 30.000 30.319 10.166 20.542 30.327 20.205 30.332 20.052 30.375 10.444 30.000 20.012 30.930 30.203 10.000 10.000 10.046 10.175 10.413 20.592 20.471 20.299 10.152 30.340 20.247 30.000 10.000 10.225 10.058 20.037 20.000 10.207 10.862 30.014 10.548 10.033 20.233 20.816 20.000 10.000 10.542 30.123 20.121 10.019 10.000 10.000 10.463 20.454 30.045 30.128 30.557 20.235 10.441 20.063 30.484 30.000 20.308 30.000 10.000 10.000 10.318 30.000 10.000 20.000 10.545 20.543 20.164 30.734 10.000 10.000 10.215 30.371 20.198 10.743 10.205 30.062 30.000 10.079 20.000 10.683 20.547 20.142 20.000 20.441 20.579 30.000 10.464 10.098 20.041 10.000 10.590 20.000 20.000 10.373 10.494 10.174 10.105 20.001 30.895 20.222 20.537 20.307 20.180 20.625 10.000 10.000 20.591 30.609 20.398 10.000 10.766 30.014 30.638 30.000 10.377 10.004 30.206 30.609 30.465 1
Ji Hou, Benjamin Graham, Matthias Nie├čner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 20.463 20.154 30.102 20.381 30.084 10.134 30.000 10.000 10.000 10.386 20.141 30.279 30.737 30.703 20.014 30.164 20.000 30.663 10.092 20.000 20.224 20.291 10.531 10.056 30.000 20.242 20.000 10.000 10.013 20.331 20.000 10.000 10.035 30.001 10.858 10.059 30.650 30.000 30.056 30.353 20.299 20.670 20.000 10.000 10.284 20.484 30.071 30.594 20.720 20.710 20.027 30.068 30.813 10.000 10.005 10.492 10.164 10.274 20.111 30.571 20.307 30.293 20.307 30.150 10.163 30.531 20.002 10.545 10.932 10.093 30.000 10.000 10.002 20.159 20.368 30.581 30.440 30.228 30.406 10.282 30.294 20.000 10.000 10.189 20.060 10.036 30.000 10.000 20.897 10.000 30.525 20.025 30.205 30.771 30.000 10.000 10.593 20.108 30.044 30.000 20.000 10.000 10.282 30.589 20.094 20.169 20.466 30.227 30.419 30.125 20.757 10.002 10.334 20.000 10.000 10.000 10.357 20.000 10.000 20.000 10.582 10.513 30.337 10.612 30.000 10.000 10.250 20.352 30.136 30.724 20.655 10.280 10.000 10.046 30.000 10.606 30.559 10.159 10.102 10.445 10.655 10.000 10.310 30.117 10.000 30.000 10.581 30.026 10.000 10.265 30.483 20.084 30.097 30.044 10.865 30.142 30.588 10.351 10.272 10.596 30.000 10.003 10.622 20.720 10.096 30.000 10.771 20.016 20.772 10.000 10.302 20.194 20.214 20.621 20.197 3
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019