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 bysort bysort bysort bysort bysort bysort 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 by
PTv3 ScanNet2000.393 10.592 10.330 10.216 10.520 10.109 20.108 100.000 10.337 10.000 10.310 90.394 60.494 80.753 70.848 10.256 20.717 20.000 30.842 10.192 20.065 20.449 50.346 10.546 30.190 70.000 50.384 40.000 10.000 30.218 10.505 10.791 10.000 10.136 10.000 20.903 10.073 90.687 30.000 40.168 10.551 20.387 50.941 10.000 10.000 20.397 70.654 30.000 70.714 30.759 90.752 40.118 40.264 20.926 10.000 10.048 20.575 20.000 70.597 10.366 10.755 10.469 10.474 10.798 10.140 60.617 10.692 30.000 40.592 20.971 10.188 20.000 10.133 40.593 10.349 10.650 10.717 40.699 10.455 10.790 10.523 30.636 10.301 10.000 10.622 20.000 60.017 90.259 10.000 30.921 20.337 10.733 10.210 10.514 10.860 60.407 10.000 10.688 10.109 60.000 90.000 40.000 10.151 10.671 40.782 10.115 70.641 10.903 10.349 10.616 10.088 40.832 20.000 30.480 10.000 10.428 10.000 20.497 60.000 10.000 50.000 10.662 20.690 10.612 10.828 10.575 10.000 10.404 40.644 10.325 30.887 20.728 10.009 100.134 50.026 110.000 10.761 10.731 10.172 30.077 20.528 20.727 20.000 10.603 40.220 20.022 20.000 10.740 10.000 20.000 10.661 10.586 10.566 10.436 40.531 10.978 10.457 10.708 10.583 30.141 70.748 10.000 10.026 10.822 10.871 30.879 50.000 10.851 10.405 20.914 10.000 10.682 20.000 90.281 10.738 10.463 4
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.
CeCo0.340 30.551 50.247 70.181 20.475 70.057 110.142 80.000 10.000 30.000 10.387 30.463 30.499 60.924 20.774 60.213 40.257 70.000 30.546 100.100 70.006 50.615 10.177 110.534 40.246 30.000 50.400 20.000 10.338 10.006 100.484 30.609 20.000 10.083 70.000 20.873 60.089 40.661 80.000 40.048 100.560 10.408 40.892 50.000 10.000 20.586 10.616 50.000 70.692 60.900 20.721 60.162 10.228 30.860 50.000 10.000 70.575 20.083 30.550 30.347 20.624 70.410 70.360 30.740 20.109 80.321 90.660 40.000 40.121 40.939 70.143 60.000 10.400 10.003 70.190 60.564 20.652 60.615 50.421 20.304 90.579 10.547 30.000 30.000 10.296 80.000 60.030 50.096 30.000 30.916 30.037 70.551 60.171 40.376 40.865 50.286 20.000 10.633 20.102 90.027 50.011 30.000 10.000 50.474 80.742 20.133 40.311 70.824 60.242 70.503 80.068 60.828 30.000 30.429 30.000 10.063 30.000 20.781 10.000 10.000 50.000 10.665 10.633 40.450 30.818 20.000 60.000 10.429 20.532 40.226 70.825 50.510 70.377 30.709 10.079 80.000 10.753 20.683 20.102 100.063 30.401 100.620 80.000 10.619 20.000 100.000 50.000 10.595 90.000 20.000 10.345 80.564 30.411 40.603 10.384 30.945 40.266 60.643 30.367 80.304 10.663 60.000 10.010 30.726 90.767 60.898 30.000 10.784 70.435 10.861 50.000 10.447 60.000 90.257 40.656 70.377 7
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023
PonderV2 ScanNet2000.346 20.552 40.270 40.175 30.497 50.070 80.239 40.000 10.000 30.000 10.232 110.412 50.584 10.842 30.804 30.212 50.540 40.000 30.433 110.106 60.000 60.590 30.290 60.548 20.243 40.000 50.356 70.000 10.000 30.062 70.398 70.441 50.000 10.104 60.000 20.888 20.076 80.682 40.030 10.094 40.491 60.351 70.869 70.000 10.063 10.403 60.700 20.000 70.660 90.881 30.761 10.050 60.186 50.852 70.000 10.007 50.570 50.100 20.565 20.326 30.641 60.431 30.290 80.621 30.259 20.408 50.622 60.125 10.082 70.950 20.179 30.000 10.263 20.424 20.193 50.558 30.880 10.545 70.375 40.727 20.445 60.499 60.000 30.000 10.475 40.002 40.034 40.083 40.000 30.924 10.290 30.636 30.115 80.400 30.874 30.186 40.000 10.611 50.128 20.113 20.000 40.000 10.000 50.584 60.636 50.103 80.385 50.843 40.283 20.603 30.080 50.825 40.000 30.377 60.000 10.000 40.000 20.457 70.000 10.000 50.000 10.574 80.608 60.481 20.792 30.394 20.000 10.357 60.503 70.261 60.817 70.504 80.304 40.472 30.115 50.000 10.750 30.677 30.202 10.000 70.509 30.729 10.000 10.519 80.000 100.000 50.000 10.620 80.000 20.000 10.660 30.560 40.486 20.384 60.346 40.952 20.247 80.667 20.436 60.269 30.691 30.000 10.010 30.787 50.889 20.880 40.000 10.810 40.336 30.860 60.000 10.606 40.009 50.248 50.681 40.392 6
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.
L3DETR-ScanNet_2000.336 40.533 70.279 20.155 40.508 30.073 70.101 110.000 10.058 20.000 10.294 100.233 100.548 20.927 10.788 50.264 10.463 50.000 30.638 70.098 90.014 40.411 70.226 70.525 70.225 60.010 30.397 30.000 10.000 30.192 30.380 80.598 30.000 10.117 20.000 20.883 30.082 60.689 20.000 40.032 110.549 30.417 30.910 30.000 10.000 20.448 50.613 60.000 70.697 50.960 10.759 20.158 20.293 10.883 30.000 10.312 10.583 10.079 40.422 80.068 110.660 40.418 40.298 60.430 80.114 70.526 30.776 10.051 20.679 10.946 30.152 50.000 10.183 30.000 90.211 40.511 60.409 100.565 60.355 50.448 40.512 40.557 20.000 30.000 10.420 50.000 60.007 110.104 20.000 30.125 110.330 20.514 90.146 70.321 70.860 60.174 50.000 10.629 30.075 100.000 90.000 40.000 10.002 40.671 40.712 30.141 30.339 60.856 30.261 60.529 60.067 70.835 10.000 30.369 80.000 10.259 20.000 20.629 30.000 10.487 10.000 10.579 70.646 20.107 110.720 80.122 40.000 10.333 80.505 60.303 50.908 10.503 90.565 10.074 60.324 10.000 10.740 40.661 50.109 80.000 70.427 70.563 110.000 10.579 70.108 50.000 50.000 10.664 30.000 20.000 10.641 40.539 60.416 30.515 20.256 50.940 70.312 30.209 110.620 10.138 90.636 70.000 10.000 80.775 80.861 40.765 70.000 10.801 60.119 90.860 60.000 10.687 10.001 80.192 100.679 60.699 1
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12
OA-CNN-L_ScanNet2000.333 50.558 20.269 50.124 70.448 90.080 50.272 30.000 10.000 30.000 10.342 50.515 20.524 40.713 110.789 40.158 70.384 60.000 30.806 30.125 30.000 60.496 40.332 30.498 100.227 50.024 20.474 10.000 10.003 20.071 60.487 20.000 60.000 10.110 40.000 20.876 40.013 110.703 10.000 40.076 60.473 70.355 60.906 40.000 10.000 20.476 40.706 10.000 70.672 80.835 70.748 50.015 100.223 40.860 50.000 10.000 70.572 40.000 70.509 50.313 40.662 20.398 80.396 20.411 90.276 10.527 20.711 20.000 40.076 80.946 30.166 40.000 10.022 50.160 30.183 70.493 70.699 50.637 30.403 30.330 80.406 70.526 40.024 20.000 10.392 70.000 60.016 100.000 60.196 20.915 40.112 60.557 50.197 20.352 60.877 20.000 60.000 10.592 90.103 80.000 90.067 10.000 10.089 20.735 30.625 60.130 60.568 30.836 50.271 30.534 50.043 90.799 50.001 20.445 20.000 10.000 40.024 10.661 20.000 10.262 20.000 10.591 40.517 100.373 50.788 50.021 50.000 10.455 10.517 50.320 40.823 60.200 110.001 110.150 40.100 60.000 10.736 50.668 40.103 90.052 40.662 10.720 30.000 10.602 50.112 40.002 40.000 10.637 60.000 20.000 10.621 60.569 20.398 50.412 50.234 60.949 30.363 20.492 90.495 50.251 40.665 50.000 10.001 70.805 30.833 50.794 60.000 10.821 20.314 40.843 80.000 10.560 50.245 20.262 30.713 20.370 8
PPT-SpUNet-F.T.0.332 60.556 30.270 30.123 80.519 20.091 30.349 20.000 10.000 30.000 10.339 60.383 70.498 70.833 40.807 20.241 30.584 30.000 30.755 40.124 40.000 60.608 20.330 40.530 60.314 10.000 50.374 50.000 10.000 30.197 20.459 40.000 60.000 10.117 20.000 20.876 40.095 10.682 40.000 40.086 50.518 40.433 10.930 20.000 10.000 20.563 30.542 80.077 40.715 20.858 50.756 30.008 110.171 70.874 40.000 10.039 30.550 60.000 70.545 40.256 50.657 50.453 20.351 40.449 70.213 30.392 60.611 70.000 40.037 90.946 30.138 80.000 10.000 70.063 50.308 20.537 40.796 20.673 20.323 80.392 60.400 80.509 50.000 30.000 10.649 10.000 60.023 60.000 60.000 30.914 50.002 100.506 100.163 60.359 50.872 40.000 60.000 10.623 40.112 40.001 80.000 40.000 10.021 30.753 10.565 100.150 10.579 20.806 70.267 40.616 10.042 100.783 70.000 30.374 70.000 10.000 40.000 20.620 50.000 10.000 50.000 10.572 90.634 30.350 60.792 30.000 60.000 10.376 50.535 30.378 20.855 30.672 20.074 70.000 70.185 40.000 10.727 60.660 60.076 110.000 70.432 60.646 50.000 10.594 60.006 90.000 50.000 10.658 40.000 20.000 10.661 10.549 50.300 80.291 80.045 80.942 60.304 40.600 50.572 40.135 100.695 20.000 10.008 50.793 40.942 10.899 20.000 10.816 30.181 60.897 20.000 10.679 30.223 30.264 20.691 30.345 9
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.
LGroundpermissive0.272 90.485 90.184 90.106 90.476 60.077 60.218 50.000 10.000 30.000 10.547 10.295 80.540 30.746 80.745 90.058 100.112 100.005 10.658 60.077 110.000 60.322 90.178 100.512 80.190 70.199 10.277 90.000 10.000 30.173 40.399 60.000 60.000 10.039 100.000 20.858 90.085 50.676 60.002 20.103 30.498 50.323 80.703 90.000 10.000 20.296 90.549 70.216 10.702 40.768 80.718 80.028 70.092 100.786 100.000 10.000 70.453 100.022 50.251 110.252 60.572 90.348 90.321 50.514 40.063 90.279 100.552 90.000 40.019 100.932 90.132 100.000 10.000 70.000 90.156 110.457 90.623 70.518 80.265 100.358 70.381 90.395 90.000 30.000 10.127 110.012 30.051 10.000 60.000 30.886 90.014 80.437 110.179 30.244 90.826 90.000 60.000 10.599 70.136 10.085 30.000 40.000 10.000 50.565 70.612 80.143 20.207 90.566 90.232 90.446 90.127 20.708 90.000 30.384 50.000 10.000 40.000 20.402 80.000 10.059 30.000 10.525 110.566 80.229 80.659 90.000 60.000 10.265 90.446 80.147 100.720 110.597 50.066 80.000 70.187 30.000 10.726 70.467 110.134 70.000 70.413 90.629 70.000 10.363 100.055 70.022 20.000 10.626 70.000 20.000 10.323 90.479 110.154 100.117 90.028 100.901 90.243 90.415 100.295 110.143 60.610 100.000 10.000 80.777 70.397 110.324 100.000 10.778 90.179 70.702 100.000 10.274 110.404 10.233 60.622 90.398 5
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv
AWCS0.305 80.508 80.225 80.142 50.463 80.063 90.195 60.000 10.000 30.000 10.467 20.551 10.504 50.773 50.764 80.142 80.029 110.000 30.626 80.100 70.000 60.360 80.179 90.507 90.137 90.006 40.300 80.000 10.000 30.172 50.364 90.512 40.000 10.056 80.000 20.865 80.093 30.634 110.000 40.071 80.396 90.296 100.876 60.000 10.000 20.373 80.436 100.063 60.749 10.877 40.721 60.131 30.124 80.804 90.000 10.000 70.515 70.010 60.452 70.252 60.578 80.417 50.179 110.484 60.171 40.337 80.606 80.000 40.115 50.937 80.142 70.000 10.008 60.000 90.157 100.484 80.402 110.501 90.339 60.553 30.529 20.478 80.000 30.000 10.404 60.001 50.022 70.077 50.000 30.894 80.219 40.628 40.093 90.305 80.886 10.233 30.000 10.603 60.112 40.023 60.000 40.000 10.000 50.741 20.664 40.097 90.253 80.782 80.264 50.523 70.154 10.707 100.000 30.411 40.000 10.000 40.000 20.332 100.000 10.000 50.000 10.602 30.595 70.185 90.656 100.159 30.000 10.355 70.424 90.154 90.729 90.516 60.220 60.620 20.084 70.000 10.707 80.651 70.173 20.014 60.381 110.582 90.000 10.619 20.049 80.000 50.000 10.702 20.000 20.000 10.302 100.489 90.317 70.334 70.392 20.922 80.254 70.533 80.394 70.129 110.613 90.000 10.000 80.820 20.649 80.749 80.000 10.782 80.282 50.863 40.000 10.288 100.006 60.220 70.633 80.542 2
OctFormer ScanNet200permissive0.326 70.539 60.265 60.131 60.499 40.110 10.522 10.000 10.000 30.000 10.318 80.427 40.455 90.743 90.765 70.175 60.842 10.000 30.828 20.204 10.033 30.429 60.335 20.601 10.312 20.000 50.357 60.000 10.000 30.047 80.423 50.000 60.000 10.105 50.000 20.873 60.079 70.670 70.000 40.117 20.471 80.432 20.829 80.000 10.000 20.584 20.417 110.089 30.684 70.837 60.705 100.021 90.178 60.892 20.000 10.028 40.505 80.000 70.457 60.200 80.662 20.412 60.244 90.496 50.000 110.451 40.626 50.000 40.102 60.943 60.138 80.000 10.000 70.149 40.291 30.534 50.722 30.632 40.331 70.253 100.453 50.487 70.000 30.000 10.479 30.000 60.022 70.000 60.000 30.900 60.128 50.684 20.164 50.413 20.854 80.000 60.000 10.512 110.074 110.003 70.000 40.000 10.000 50.469 90.613 70.132 50.529 40.871 20.227 100.582 40.026 110.787 60.000 30.339 90.000 10.000 40.000 20.626 40.000 10.029 40.000 10.587 50.612 50.411 40.724 70.000 60.000 10.407 30.552 20.513 10.849 40.655 30.408 20.000 70.296 20.000 10.686 90.645 80.145 50.022 50.414 80.633 60.000 10.637 10.224 10.000 50.000 10.650 50.000 20.000 10.622 50.535 70.343 60.483 30.230 70.943 50.289 50.618 40.596 20.140 80.679 40.000 10.022 20.783 60.620 90.906 10.000 10.806 50.137 80.865 30.000 10.378 70.000 90.168 110.680 50.227 10
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
CSC-Pretrainpermissive0.249 110.455 110.171 100.079 110.418 100.059 100.186 70.000 10.000 30.000 10.335 70.250 90.316 100.766 60.697 110.142 80.170 80.003 20.553 90.112 50.097 10.201 110.186 80.476 110.081 100.000 50.216 110.000 10.000 30.001 110.314 110.000 60.000 10.055 90.000 20.832 110.094 20.659 90.002 20.076 60.310 110.293 110.664 110.000 10.000 20.175 110.634 40.130 20.552 110.686 110.700 110.076 50.110 90.770 110.000 10.000 70.430 110.000 70.319 90.166 90.542 110.327 100.205 100.332 100.052 100.375 70.444 110.000 40.012 110.930 110.203 10.000 10.000 70.046 60.175 80.413 100.592 80.471 100.299 90.152 110.340 100.247 110.000 30.000 10.225 90.058 20.037 20.000 60.207 10.862 100.014 80.548 70.033 100.233 100.816 100.000 60.000 10.542 100.123 30.121 10.019 20.000 10.000 50.463 100.454 110.045 110.128 110.557 100.235 80.441 100.063 80.484 110.000 30.308 110.000 10.000 40.000 20.318 110.000 10.000 50.000 10.545 100.543 90.164 100.734 60.000 60.000 10.215 110.371 100.198 80.743 80.205 100.062 90.000 70.079 80.000 10.683 100.547 100.142 60.000 70.441 50.579 100.000 10.464 90.098 60.041 10.000 10.590 100.000 20.000 10.373 70.494 80.174 90.105 100.001 110.895 100.222 100.537 70.307 100.180 50.625 80.000 10.000 80.591 110.609 100.398 90.000 10.766 110.014 110.638 110.000 10.377 80.004 70.206 90.609 110.465 3
Ji Hou, Benjamin Graham, Matthias Nie├čner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021
Minkowski 34Dpermissive0.253 100.463 100.154 110.102 100.381 110.084 40.134 90.000 10.000 30.000 10.386 40.141 110.279 110.737 100.703 100.014 110.164 90.000 30.663 50.092 100.000 60.224 100.291 50.531 50.056 110.000 50.242 100.000 10.000 30.013 90.331 100.000 60.000 10.035 110.001 10.858 90.059 100.650 100.000 40.056 90.353 100.299 90.670 100.000 10.000 20.284 100.484 90.071 50.594 100.720 100.710 90.027 80.068 110.813 80.000 10.005 60.492 90.164 10.274 100.111 100.571 100.307 110.293 70.307 110.150 50.163 110.531 100.002 30.545 30.932 90.093 110.000 10.000 70.002 80.159 90.368 110.581 90.440 110.228 110.406 50.282 110.294 100.000 30.000 10.189 100.060 10.036 30.000 60.000 30.897 70.000 110.525 80.025 110.205 110.771 110.000 60.000 10.593 80.108 70.044 40.000 40.000 10.000 50.282 110.589 90.094 100.169 100.466 110.227 100.419 110.125 30.757 80.002 10.334 100.000 10.000 40.000 20.357 90.000 10.000 50.000 10.582 60.513 110.337 70.612 110.000 60.000 10.250 100.352 110.136 110.724 100.655 30.280 50.000 70.046 100.000 10.606 110.559 90.159 40.102 10.445 40.655 40.000 10.310 110.117 30.000 50.000 10.581 110.026 10.000 10.265 110.483 100.084 110.097 110.044 90.865 110.142 110.588 60.351 90.272 20.596 110.000 10.003 60.622 100.720 70.096 110.000 10.771 100.016 100.772 90.000 10.302 90.194 40.214 80.621 100.197 11
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019