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
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 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 by
AWCS0.305 100.508 100.225 100.142 70.463 100.063 110.195 70.000 10.000 30.000 10.467 20.551 10.504 70.773 60.764 100.142 100.029 130.000 30.626 90.100 90.000 70.360 100.179 110.507 110.137 110.006 50.300 100.000 10.000 30.172 60.364 110.512 50.000 10.056 100.000 20.865 100.093 40.634 130.000 60.071 90.396 110.296 120.876 70.000 10.000 20.373 100.436 120.063 80.749 20.877 60.721 80.131 30.124 100.804 110.000 10.000 90.515 80.010 60.452 90.252 80.578 100.417 70.179 130.484 80.171 50.337 100.606 100.000 50.115 60.937 100.142 80.000 10.008 80.000 110.157 120.484 100.402 130.501 110.339 70.553 40.529 20.478 90.000 30.000 10.404 80.001 70.022 90.077 70.000 30.894 90.219 40.628 60.093 110.305 100.886 10.233 50.000 10.603 70.112 50.023 70.000 40.000 10.000 70.741 30.664 50.097 110.253 100.782 90.264 70.523 90.154 10.707 120.000 40.411 60.000 10.000 50.000 30.332 120.000 20.000 50.000 10.602 50.595 80.185 110.656 120.159 50.000 10.355 90.424 110.154 110.729 110.516 80.220 80.620 30.084 90.000 10.707 100.651 90.173 30.014 70.381 130.582 110.000 10.619 20.049 100.000 50.000 10.702 30.000 20.000 10.302 120.489 110.317 90.334 70.392 40.922 100.254 90.533 100.394 90.129 130.613 110.000 10.000 90.820 30.649 90.749 100.000 10.782 100.282 60.863 40.000 10.288 120.006 80.220 90.633 100.542 3
LGroundpermissive0.272 110.485 110.184 110.106 110.476 80.077 80.218 60.000 10.000 30.000 10.547 10.295 90.540 40.746 90.745 110.058 120.112 120.005 10.658 70.077 130.000 70.322 110.178 120.512 100.190 90.199 10.277 110.000 10.000 30.173 50.399 80.000 70.000 10.039 120.000 20.858 110.085 60.676 80.002 40.103 40.498 70.323 100.703 100.000 10.000 20.296 110.549 80.216 10.702 50.768 100.718 100.028 90.092 120.786 120.000 10.000 90.453 120.022 50.251 130.252 80.572 110.348 110.321 70.514 60.063 110.279 120.552 110.000 50.019 120.932 110.132 120.000 10.000 90.000 110.156 130.457 110.623 90.518 100.265 120.358 80.381 110.395 110.000 30.000 10.127 130.012 50.051 10.000 80.000 30.886 100.014 100.437 130.179 40.244 110.826 110.000 80.000 10.599 80.136 10.085 30.000 40.000 10.000 70.565 90.612 100.143 40.207 110.566 110.232 110.446 110.127 20.708 110.000 40.384 70.000 10.000 50.000 30.402 100.000 20.059 30.000 10.525 130.566 90.229 100.659 110.000 80.000 10.265 110.446 100.147 120.720 130.597 60.066 100.000 80.187 50.000 10.726 90.467 130.134 90.000 80.413 110.629 90.000 10.363 120.055 80.022 20.000 10.626 90.000 20.000 10.323 110.479 130.154 120.117 110.028 120.901 110.243 110.415 120.295 130.143 60.610 120.000 10.000 90.777 80.397 130.324 120.000 10.778 110.179 80.702 120.000 10.274 130.404 10.233 80.622 110.398 7
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
Minkowski 34Dpermissive0.253 120.463 120.154 130.102 120.381 130.084 60.134 110.000 10.000 30.000 10.386 50.141 130.279 130.737 110.703 120.014 130.164 110.000 30.663 60.092 120.000 70.224 120.291 70.531 70.056 130.000 60.242 120.000 10.000 30.013 110.331 120.000 70.000 10.035 130.001 10.858 110.059 120.650 120.000 60.056 100.353 120.299 110.670 110.000 10.000 20.284 120.484 110.071 70.594 120.720 120.710 110.027 100.068 130.813 100.000 10.005 80.492 100.164 10.274 120.111 120.571 120.307 130.293 90.307 130.150 60.163 130.531 120.002 40.545 30.932 110.093 130.000 10.000 90.002 100.159 110.368 130.581 110.440 130.228 130.406 60.282 130.294 120.000 30.000 10.189 120.060 20.036 40.000 80.000 30.897 80.000 130.525 100.025 130.205 130.771 130.000 80.000 10.593 90.108 80.044 40.000 40.000 10.000 70.282 130.589 110.094 120.169 120.466 130.227 120.419 130.125 30.757 100.002 20.334 120.000 10.000 50.000 30.357 110.000 20.000 50.000 10.582 80.513 120.337 90.612 130.000 80.000 10.250 120.352 130.136 130.724 120.655 40.280 70.000 80.046 120.000 10.606 130.559 110.159 50.102 10.445 60.655 60.000 10.310 130.117 40.000 50.000 10.581 130.026 10.000 10.265 130.483 120.084 130.097 130.044 110.865 130.142 130.588 80.351 110.272 20.596 130.000 10.003 70.622 120.720 80.096 130.000 10.771 120.016 120.772 110.000 10.302 110.194 50.214 100.621 120.197 13
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
ALS-MinkowskiNetcopyleft0.414 10.610 10.322 20.271 10.542 10.153 10.159 90.000 10.000 30.000 10.404 30.503 30.532 50.672 130.804 40.285 10.888 10.000 30.900 10.226 10.087 20.598 30.342 30.671 10.217 80.087 20.449 20.000 10.000 30.253 10.477 41.000 10.000 10.118 30.000 20.905 10.071 110.710 10.076 10.047 120.665 10.376 70.981 10.000 10.000 20.466 60.632 50.113 30.769 10.956 20.795 10.031 80.314 10.936 10.000 10.390 20.601 10.000 70.458 70.366 10.719 20.440 40.564 10.699 30.314 10.464 50.784 10.200 10.283 40.973 10.142 80.000 10.250 40.285 40.220 50.718 10.752 40.723 10.460 10.248 120.475 60.463 100.000 30.000 10.446 60.021 40.025 70.285 10.000 30.972 10.149 50.769 10.230 10.535 10.879 20.252 40.000 10.693 10.129 20.000 100.000 40.000 10.447 10.958 10.662 60.159 20.598 20.780 100.344 20.646 20.106 40.893 10.135 10.455 30.000 10.194 30.259 10.726 20.475 10.000 50.000 10.741 10.865 10.571 20.817 30.445 30.000 10.506 10.630 20.230 80.916 10.728 10.635 11.000 10.252 40.000 10.804 10.697 30.137 80.043 50.717 10.807 20.000 10.510 100.245 10.000 50.000 10.709 20.000 20.000 10.703 10.572 20.646 10.223 90.531 20.984 10.397 20.813 10.798 10.135 110.800 10.000 10.097 10.832 10.752 70.842 70.000 10.852 10.149 90.846 80.000 10.666 40.359 20.252 60.777 10.690 2
PTv3 ScanNet2000.393 20.592 20.330 10.216 20.520 20.109 30.108 120.000 10.337 10.000 10.310 100.394 70.494 100.753 80.848 10.256 30.717 40.000 30.842 20.192 30.065 30.449 70.346 20.546 50.190 90.000 60.384 50.000 10.000 30.218 20.505 10.791 20.000 10.136 20.000 20.903 20.073 100.687 50.000 60.168 10.551 40.387 60.941 20.000 10.000 20.397 90.654 30.000 90.714 40.759 110.752 60.118 40.264 30.926 20.000 10.048 40.575 30.000 70.597 10.366 10.755 10.469 10.474 20.798 10.140 70.617 10.692 50.000 50.592 20.971 20.188 30.000 10.133 60.593 10.349 10.650 30.717 60.699 20.455 20.790 10.523 30.636 10.301 10.000 10.622 20.000 80.017 110.259 30.000 30.921 30.337 10.733 20.210 20.514 20.860 70.407 10.000 10.688 20.109 70.000 100.000 40.000 10.151 30.671 50.782 10.115 90.641 10.903 10.349 10.616 30.088 50.832 40.000 40.480 20.000 10.428 10.000 30.497 80.000 20.000 50.000 10.662 30.690 20.612 10.828 10.575 10.000 10.404 50.644 10.325 40.887 30.728 10.009 120.134 60.026 130.000 10.761 20.731 10.172 40.077 20.528 40.727 40.000 10.603 40.220 30.022 20.000 10.740 10.000 20.000 10.661 30.586 10.566 20.436 40.531 20.978 20.457 10.708 30.583 40.141 70.748 20.000 10.026 20.822 20.871 30.879 50.000 10.851 20.405 20.914 10.000 10.682 20.000 110.281 20.738 20.463 6
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
PonderV2 ScanNet2000.346 40.552 60.270 60.175 50.497 60.070 100.239 50.000 10.000 30.000 10.232 130.412 60.584 20.842 30.804 40.212 60.540 60.000 30.433 120.106 80.000 70.590 40.290 80.548 40.243 50.000 60.356 90.000 10.000 30.062 80.398 90.441 60.000 10.104 80.000 20.888 40.076 90.682 60.030 20.094 50.491 80.351 90.869 80.000 10.063 10.403 80.700 20.000 90.660 100.881 50.761 20.050 70.186 60.852 90.000 10.007 70.570 60.100 20.565 20.326 50.641 80.431 50.290 100.621 50.259 30.408 70.622 80.125 20.082 80.950 40.179 40.000 10.263 30.424 30.193 70.558 50.880 20.545 90.375 50.727 20.445 80.499 70.000 30.000 10.475 50.002 60.034 50.083 60.000 30.924 20.290 30.636 50.115 100.400 40.874 40.186 60.000 10.611 60.128 30.113 20.000 40.000 10.000 70.584 80.636 70.103 100.385 70.843 50.283 30.603 50.080 60.825 60.000 40.377 80.000 10.000 50.000 30.457 90.000 20.000 50.000 10.574 100.608 70.481 30.792 40.394 40.000 10.357 80.503 90.261 70.817 90.504 100.304 60.472 40.115 70.000 10.750 50.677 50.202 20.000 80.509 50.729 30.000 10.519 90.000 120.000 50.000 10.620 100.000 20.000 10.660 50.560 50.486 40.384 60.346 60.952 40.247 100.667 40.436 80.269 30.691 50.000 10.010 40.787 60.889 20.880 40.000 10.810 60.336 40.860 60.000 10.606 50.009 70.248 70.681 50.392 8
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
CeCo0.340 50.551 70.247 90.181 40.475 90.057 130.142 100.000 10.000 30.000 10.387 40.463 40.499 80.924 20.774 80.213 50.257 90.000 30.546 110.100 90.006 60.615 10.177 130.534 60.246 40.000 60.400 30.000 10.338 10.006 120.484 30.609 30.000 10.083 90.000 20.873 80.089 50.661 100.000 60.048 110.560 30.408 50.892 60.000 10.000 20.586 10.616 60.000 90.692 70.900 40.721 80.162 10.228 40.860 70.000 10.000 90.575 30.083 30.550 30.347 30.624 90.410 90.360 50.740 20.109 90.321 110.660 60.000 50.121 50.939 90.143 70.000 10.400 10.003 90.190 80.564 40.652 80.615 70.421 30.304 100.579 10.547 40.000 30.000 10.296 100.000 80.030 60.096 50.000 30.916 40.037 90.551 80.171 50.376 60.865 60.286 30.000 10.633 30.102 100.027 60.011 30.000 10.000 70.474 100.742 20.133 60.311 90.824 70.242 90.503 100.068 70.828 50.000 40.429 50.000 10.063 40.000 30.781 10.000 20.000 50.000 10.665 20.633 50.450 50.818 20.000 80.000 10.429 30.532 60.226 90.825 70.510 90.377 40.709 20.079 100.000 10.753 40.683 40.102 120.063 30.401 120.620 100.000 10.619 20.000 120.000 50.000 10.595 110.000 20.000 10.345 100.564 40.411 60.603 10.384 50.945 60.266 70.643 50.367 100.304 10.663 80.000 10.010 40.726 110.767 60.898 30.000 10.784 90.435 10.861 50.000 10.447 80.000 110.257 50.656 90.377 9
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
L3DETR-ScanNet_2000.336 60.533 90.279 40.155 60.508 40.073 90.101 130.000 10.058 20.000 10.294 120.233 120.548 30.927 10.788 70.264 20.463 70.000 30.638 80.098 110.014 50.411 90.226 90.525 90.225 70.010 40.397 40.000 10.000 30.192 40.380 100.598 40.000 10.117 40.000 20.883 50.082 70.689 30.000 60.032 130.549 50.417 40.910 40.000 10.000 20.448 70.613 70.000 90.697 60.960 10.759 30.158 20.293 20.883 50.000 10.312 30.583 20.079 40.422 100.068 130.660 60.418 60.298 80.430 100.114 80.526 30.776 20.051 30.679 10.946 50.152 60.000 10.183 50.000 110.211 60.511 80.409 120.565 80.355 60.448 50.512 40.557 20.000 30.000 10.420 70.000 80.007 130.104 40.000 30.125 130.330 20.514 110.146 80.321 90.860 70.174 70.000 10.629 40.075 120.000 100.000 40.000 10.002 60.671 50.712 40.141 50.339 80.856 30.261 80.529 80.067 80.835 20.000 40.369 100.000 10.259 20.000 30.629 40.000 20.487 10.000 10.579 90.646 30.107 130.720 90.122 60.000 10.333 100.505 80.303 60.908 20.503 110.565 20.074 70.324 10.000 10.740 60.661 70.109 100.000 80.427 90.563 130.000 10.579 80.108 60.000 50.000 10.664 40.000 20.000 10.641 60.539 70.416 50.515 20.256 70.940 90.312 40.209 130.620 20.138 100.636 90.000 10.000 90.775 90.861 40.765 90.000 10.801 80.119 110.860 60.000 10.687 10.001 100.192 120.679 70.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
CSC-Pretrainpermissive0.249 130.455 130.171 120.079 130.418 120.059 120.186 80.000 10.000 30.000 10.335 80.250 110.316 120.766 70.697 130.142 100.170 100.003 20.553 100.112 70.097 10.201 130.186 100.476 130.081 120.000 60.216 130.000 10.000 30.001 130.314 130.000 70.000 10.055 110.000 20.832 130.094 30.659 110.002 40.076 70.310 130.293 130.664 120.000 10.000 20.175 130.634 40.130 20.552 130.686 130.700 130.076 60.110 110.770 130.000 10.000 90.430 130.000 70.319 110.166 110.542 130.327 120.205 120.332 120.052 120.375 90.444 130.000 50.012 130.930 130.203 20.000 10.000 90.046 80.175 100.413 120.592 100.471 120.299 110.152 130.340 120.247 130.000 30.000 10.225 110.058 30.037 30.000 80.207 10.862 120.014 100.548 90.033 120.233 120.816 120.000 80.000 10.542 110.123 40.121 10.019 20.000 10.000 70.463 120.454 130.045 130.128 130.557 120.235 100.441 120.063 90.484 130.000 40.308 130.000 10.000 50.000 30.318 130.000 20.000 50.000 10.545 120.543 100.164 120.734 70.000 80.000 10.215 130.371 120.198 100.743 100.205 120.062 110.000 80.079 100.000 10.683 120.547 120.142 70.000 80.441 70.579 120.000 10.464 110.098 70.041 10.000 10.590 120.000 20.000 10.373 90.494 100.174 110.105 120.001 130.895 120.222 120.537 90.307 120.180 50.625 100.000 10.000 90.591 130.609 110.398 110.000 10.766 130.014 130.638 130.000 10.377 100.004 90.206 110.609 130.465 5
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
BFANet ScanNet200permissive0.360 30.553 50.293 30.193 30.483 70.096 40.266 40.000 10.000 30.000 10.298 110.255 100.661 10.810 50.810 20.194 70.785 30.000 30.000 130.161 40.000 70.494 60.382 10.574 30.258 30.000 60.372 70.000 10.000 30.043 100.436 60.000 70.000 10.239 10.000 20.901 30.105 10.689 30.025 30.128 20.614 20.436 10.493 130.000 10.000 20.526 40.546 90.109 40.651 110.953 30.753 50.101 50.143 90.897 30.000 10.431 10.469 110.000 70.522 50.337 40.661 50.459 20.409 30.666 40.102 100.508 40.757 30.000 50.060 100.970 30.497 10.000 10.376 20.511 20.262 40.688 20.921 10.617 60.321 100.590 30.491 50.556 30.000 30.000 10.481 30.093 10.043 20.284 20.000 30.875 110.135 60.669 40.124 90.394 50.849 100.298 20.000 10.476 130.088 110.042 50.000 40.000 10.254 20.653 70.741 30.215 10.573 40.852 40.266 60.654 10.056 100.835 20.000 40.492 10.000 10.000 50.000 30.612 70.000 20.000 50.000 10.616 40.469 130.460 40.698 100.516 20.000 10.378 60.563 30.476 20.863 40.574 70.330 50.000 80.282 30.000 10.760 30.710 20.233 10.000 80.641 30.814 10.000 10.585 70.053 90.000 50.000 10.629 80.000 20.000 10.678 20.528 90.534 30.129 100.596 10.973 30.264 80.772 20.526 60.139 90.707 30.000 10.000 90.764 100.591 120.848 60.000 10.827 30.338 30.806 100.000 10.568 60.151 60.358 10.659 80.510 4
OA-CNN-L_ScanNet2000.333 70.558 30.269 70.124 90.448 110.080 70.272 30.000 10.000 30.000 10.342 60.515 20.524 60.713 120.789 60.158 90.384 80.000 30.806 40.125 50.000 70.496 50.332 50.498 120.227 60.024 30.474 10.000 10.003 20.071 70.487 20.000 70.000 10.110 60.000 20.876 60.013 130.703 20.000 60.076 70.473 90.355 80.906 50.000 10.000 20.476 50.706 10.000 90.672 90.835 90.748 70.015 120.223 50.860 70.000 10.000 90.572 50.000 70.509 60.313 60.662 30.398 100.396 40.411 110.276 20.527 20.711 40.000 50.076 90.946 50.166 50.000 10.022 70.160 50.183 90.493 90.699 70.637 40.403 40.330 90.406 90.526 50.024 20.000 10.392 90.000 80.016 120.000 80.196 20.915 50.112 80.557 70.197 30.352 80.877 30.000 80.000 10.592 100.103 90.000 100.067 10.000 10.089 40.735 40.625 80.130 80.568 50.836 60.271 40.534 70.043 110.799 70.001 30.445 40.000 10.000 50.024 20.661 30.000 20.262 20.000 10.591 60.517 110.373 70.788 60.021 70.000 10.455 20.517 70.320 50.823 80.200 130.001 130.150 50.100 80.000 10.736 70.668 60.103 110.052 40.662 20.720 50.000 10.602 50.112 50.002 40.000 10.637 70.000 20.000 10.621 80.569 30.398 70.412 50.234 80.949 50.363 30.492 110.495 70.251 40.665 70.000 10.001 80.805 40.833 50.794 80.000 10.821 40.314 50.843 90.000 10.560 70.245 30.262 40.713 30.370 10
PPT-SpUNet-F.T.0.332 80.556 40.270 50.123 100.519 30.091 50.349 20.000 10.000 30.000 10.339 70.383 80.498 90.833 40.807 30.241 40.584 50.000 30.755 50.124 60.000 70.608 20.330 60.530 80.314 10.000 60.374 60.000 10.000 30.197 30.459 50.000 70.000 10.117 40.000 20.876 60.095 20.682 60.000 60.086 60.518 60.433 20.930 30.000 10.000 20.563 30.542 100.077 60.715 30.858 70.756 40.008 130.171 80.874 60.000 10.039 50.550 70.000 70.545 40.256 70.657 70.453 30.351 60.449 90.213 40.392 80.611 90.000 50.037 110.946 50.138 100.000 10.000 90.063 70.308 20.537 60.796 30.673 30.323 90.392 70.400 100.509 60.000 30.000 10.649 10.000 80.023 80.000 80.000 30.914 60.002 120.506 120.163 70.359 70.872 50.000 80.000 10.623 50.112 50.001 90.000 40.000 10.021 50.753 20.565 120.150 30.579 30.806 80.267 50.616 30.042 120.783 90.000 40.374 90.000 10.000 50.000 30.620 60.000 20.000 50.000 10.572 110.634 40.350 80.792 40.000 80.000 10.376 70.535 50.378 30.855 50.672 30.074 90.000 80.185 60.000 10.727 80.660 80.076 130.000 80.432 80.646 70.000 10.594 60.006 110.000 50.000 10.658 50.000 20.000 10.661 30.549 60.300 100.291 80.045 100.942 80.304 50.600 70.572 50.135 110.695 40.000 10.008 60.793 50.942 10.899 20.000 10.816 50.181 70.897 20.000 10.679 30.223 40.264 30.691 40.345 11
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024
OctFormer ScanNet200permissive0.326 90.539 80.265 80.131 80.499 50.110 20.522 10.000 10.000 30.000 10.318 90.427 50.455 110.743 100.765 90.175 80.842 20.000 30.828 30.204 20.033 40.429 80.335 40.601 20.312 20.000 60.357 80.000 10.000 30.047 90.423 70.000 70.000 10.105 70.000 20.873 80.079 80.670 90.000 60.117 30.471 100.432 30.829 90.000 10.000 20.584 20.417 130.089 50.684 80.837 80.705 120.021 110.178 70.892 40.000 10.028 60.505 90.000 70.457 80.200 100.662 30.412 80.244 110.496 70.000 130.451 60.626 70.000 50.102 70.943 80.138 100.000 10.000 90.149 60.291 30.534 70.722 50.632 50.331 80.253 110.453 70.487 80.000 30.000 10.479 40.000 80.022 90.000 80.000 30.900 70.128 70.684 30.164 60.413 30.854 90.000 80.000 10.512 120.074 130.003 80.000 40.000 10.000 70.469 110.613 90.132 70.529 60.871 20.227 120.582 60.026 130.787 80.000 40.339 110.000 10.000 50.000 30.626 50.000 20.029 40.000 10.587 70.612 60.411 60.724 80.000 80.000 10.407 40.552 40.513 10.849 60.655 40.408 30.000 80.296 20.000 10.686 110.645 100.145 60.022 60.414 100.633 80.000 10.637 10.224 20.000 50.000 10.650 60.000 20.000 10.622 70.535 80.343 80.483 30.230 90.943 70.289 60.618 60.596 30.140 80.679 60.000 10.022 30.783 70.620 100.906 10.000 10.806 70.137 100.865 30.000 10.378 90.000 110.168 130.680 60.227 12
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023