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
OccuSeg+Semantic0.764 10.758 130.796 30.839 20.746 10.907 10.562 10.850 20.680 10.672 10.978 10.610 10.335 10.777 10.819 60.847 10.830 10.691 10.972 10.885 10.727 1
MinkowskiNetpermissive0.736 20.859 30.818 20.832 30.709 30.840 30.521 40.853 10.660 20.643 20.951 40.544 30.286 90.731 20.893 10.675 80.772 50.683 20.874 120.852 30.727 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 30.647 220.821 10.846 10.721 20.869 20.533 20.754 90.603 60.614 30.955 20.572 20.325 20.710 30.870 20.724 30.823 20.628 50.934 20.865 20.683 4
JSENet0.699 40.881 10.762 80.821 40.667 50.800 100.522 30.792 50.613 40.607 40.935 200.492 60.205 190.576 110.853 30.691 50.758 70.652 40.872 130.828 50.649 6
CU-Hybrid Net0.693 50.596 260.789 40.803 60.677 40.800 100.469 100.846 30.554 130.591 60.948 100.500 40.316 40.609 60.847 40.732 20.808 30.593 80.894 60.839 40.652 5
KP-FCNN0.684 60.847 50.758 110.784 80.647 70.814 70.473 80.772 70.605 50.594 50.935 200.450 120.181 260.587 80.805 70.690 60.785 40.614 60.882 80.819 60.632 9
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointConvpermissive0.666 70.781 100.759 100.699 160.644 80.822 60.475 70.779 60.564 120.504 170.953 30.428 180.203 210.586 90.754 80.661 110.753 80.588 90.902 30.813 80.642 7
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 70.703 180.781 50.751 140.655 60.830 40.471 90.769 80.474 230.537 90.951 40.475 70.279 100.635 50.698 110.675 80.751 90.553 160.816 210.806 90.703 3
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
DualMeshNet0.658 90.778 110.702 160.806 50.619 100.813 80.468 110.693 160.494 200.524 130.941 150.449 130.298 50.510 170.821 50.675 80.727 100.568 120.826 180.803 110.637 8
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualMeshNet: Joint Geodesic and Euclidean Convolutions for 3D Semantic Segmentation. CVPR 2020
MVPNetpermissive0.641 100.831 60.715 150.671 190.590 140.781 140.394 190.679 190.642 30.553 80.937 190.462 90.256 110.649 40.406 250.626 150.691 140.666 30.877 90.792 160.608 13
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
FPConvpermissive0.639 110.785 90.760 90.713 150.603 120.798 120.392 200.534 280.603 60.524 130.948 100.457 100.250 120.538 140.723 90.598 180.696 130.614 60.872 130.799 120.567 21
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
SConv0.636 120.830 70.697 190.752 130.572 180.780 150.445 130.716 130.529 140.530 100.951 40.446 140.170 270.507 190.666 130.636 140.682 160.541 200.886 70.799 120.594 17
joint point-basedpermissive0.634 130.614 240.778 60.667 210.633 90.825 50.420 150.804 40.467 250.561 70.951 40.494 50.291 60.566 120.458 210.579 210.764 60.559 150.838 170.814 70.598 16
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 140.866 20.731 140.771 90.576 170.809 90.410 170.684 170.497 190.491 180.949 80.466 80.105 330.581 100.646 140.620 160.680 170.542 190.817 200.795 140.618 11
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
3DSM_DMMF0.631 150.626 230.745 120.801 70.607 110.751 200.506 50.729 120.565 110.491 180.866 350.434 150.197 230.595 70.630 150.709 40.705 110.560 140.875 110.740 250.491 28
FusionAwareConv0.630 160.604 250.741 130.766 110.590 140.747 210.501 60.734 100.503 180.527 110.919 280.454 110.323 30.550 130.420 240.678 70.688 150.544 180.896 50.795 140.627 10
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR2020
SIConv0.625 170.830 70.694 200.757 120.563 190.772 170.448 120.647 220.520 150.509 150.949 80.431 170.191 240.496 210.614 160.647 120.672 180.535 210.876 100.783 170.571 19
HPEIN0.618 180.729 150.668 230.647 220.597 130.766 180.414 160.680 180.520 150.525 120.946 120.432 160.215 170.493 220.599 170.638 130.617 260.570 110.897 40.806 90.605 14
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-GCNpermissive0.610 190.858 40.772 70.489 320.532 200.792 130.404 180.643 230.570 100.507 160.935 200.414 190.046 370.510 170.702 100.602 170.705 110.549 170.859 160.773 190.534 24
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds.
LAP-D0.594 200.720 160.692 210.637 240.456 260.773 160.391 220.730 110.587 80.445 230.940 170.381 210.288 70.434 240.453 220.591 190.649 200.581 100.777 250.749 240.610 12
DPC0.592 210.720 160.700 170.602 270.480 230.762 190.380 240.713 140.585 90.437 240.940 170.369 230.288 70.434 240.509 200.590 200.639 240.567 130.772 260.755 220.592 18
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions. arXiv
CCRFNet0.589 220.766 120.659 260.683 180.470 250.740 230.387 230.620 240.490 210.476 200.922 260.355 260.245 130.511 160.511 190.571 220.643 220.493 250.872 130.762 200.600 15
TextureNetpermissive0.566 230.672 200.664 240.671 190.494 210.719 240.445 130.678 200.411 300.396 250.935 200.356 250.225 150.412 260.535 180.565 230.636 250.464 270.794 240.680 300.568 20
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
DVVNet0.562 240.648 210.700 170.770 100.586 160.687 280.333 260.650 210.514 170.475 210.906 320.359 240.223 160.340 290.442 230.422 320.668 190.501 230.708 300.779 180.534 24
Pointnet++ & Featurepermissive0.557 250.735 140.661 250.686 170.491 220.744 220.392 200.539 270.451 260.375 270.946 120.376 220.205 190.403 270.356 270.553 240.643 220.497 240.824 190.756 210.515 26
PanopticFusion-label0.529 260.491 330.688 220.604 260.386 290.632 330.225 370.705 150.434 280.293 310.815 360.348 270.241 140.499 200.669 120.507 250.649 200.442 310.796 230.602 350.561 22
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
3DMV, FTSDF0.501 270.558 300.608 310.424 360.478 240.690 270.246 330.586 250.468 240.450 220.911 300.394 200.160 290.438 230.212 330.432 310.541 310.475 260.742 280.727 260.477 29
PCNN0.498 280.559 290.644 280.560 300.420 280.711 260.229 350.414 290.436 270.352 280.941 150.324 280.155 300.238 330.387 260.493 260.529 320.509 220.813 220.751 230.504 27
3DMV0.484 290.484 340.538 340.643 230.424 270.606 360.310 270.574 260.433 290.378 260.796 370.301 290.214 180.537 150.208 340.472 300.507 350.413 340.693 310.602 350.539 23
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 300.577 280.611 300.356 380.321 350.715 250.299 290.376 320.328 350.319 290.944 140.285 310.164 280.216 360.229 320.484 280.545 300.456 290.755 270.709 270.475 30
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 310.679 190.604 320.578 290.380 300.682 290.291 300.106 380.483 220.258 360.920 270.258 330.025 380.231 350.325 280.480 290.560 290.463 280.725 290.666 320.231 38
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PNET20.442 320.548 310.548 330.597 280.363 320.628 340.300 280.292 330.374 320.307 300.881 340.268 320.186 250.238 330.204 350.407 330.506 360.449 300.667 320.620 340.462 31
SurfaceConvPF0.442 320.505 320.622 290.380 370.342 340.654 310.227 360.397 310.367 330.276 330.924 250.240 340.198 220.359 280.262 300.366 340.581 270.435 320.640 330.668 310.398 32
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 340.437 360.646 270.474 330.369 310.645 320.353 250.258 350.282 370.279 320.918 290.298 300.147 310.283 300.294 290.487 270.562 280.427 330.619 340.633 330.352 34
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
SPLAT Netcopyleft0.393 350.472 350.511 350.606 250.311 360.656 300.245 340.405 300.328 350.197 370.927 240.227 360.000 400.001 400.249 310.271 390.510 330.383 360.593 350.699 280.267 36
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+FTSDF0.383 360.297 380.491 360.432 350.358 330.612 350.274 310.116 370.411 300.265 340.904 330.229 350.079 350.250 310.185 360.320 370.510 330.385 350.548 360.597 370.394 33
PointNet++permissive0.339 370.584 270.478 370.458 340.256 380.360 390.250 320.247 360.278 380.261 350.677 390.183 370.117 320.212 370.145 380.364 350.346 390.232 390.548 360.523 380.252 37
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 380.353 370.290 390.278 390.166 390.553 370.169 390.286 340.147 390.148 390.908 310.182 380.064 360.023 390.018 400.354 360.363 370.345 370.546 380.685 290.278 35
ScanNetpermissive0.306 390.203 390.366 380.501 310.311 360.524 380.211 380.002 400.342 340.189 380.786 380.145 390.102 340.245 320.152 370.318 380.348 380.300 380.460 390.437 390.182 39
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 400.000 400.041 400.172 400.030 400.062 400.001 400.035 390.004 400.051 400.143 400.019 400.003 390.041 380.050 390.003 400.054 400.018 400.005 400.264 400.082 40

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
OccuSeg+instance0.672 11.000 10.758 60.682 30.576 10.842 10.477 10.504 100.524 20.567 10.585 10.451 20.557 41.000 10.751 10.797 20.563 51.000 10.467 3
GICN0.638 21.000 10.895 10.800 10.480 50.676 50.144 20.737 20.354 50.447 30.400 80.365 50.700 11.000 10.569 30.836 10.599 21.000 10.473 2
PointGroup0.636 31.000 10.765 40.624 40.505 40.797 20.116 50.696 30.384 40.441 40.559 20.476 10.596 31.000 10.666 20.756 40.556 70.997 60.513 1
MPA0.611 41.000 10.833 20.765 20.526 20.756 40.136 40.588 70.470 30.438 50.432 60.358 60.650 20.857 60.429 60.765 30.557 61.000 10.430 5
SSEN0.575 51.000 10.761 50.473 90.477 60.795 30.066 80.529 80.658 10.460 20.461 30.380 40.331 110.859 50.401 90.692 60.653 11.000 10.348 9
MTML0.549 61.000 10.807 30.588 70.327 90.647 60.004 150.815 10.180 90.418 60.364 100.182 90.445 61.000 10.442 50.688 70.571 41.000 10.396 6
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Occipital-SCS0.512 71.000 10.716 80.509 80.506 30.611 80.092 70.602 60.177 100.346 90.383 90.165 100.442 70.850 90.386 100.618 90.543 80.889 110.389 7
3D-BoNet0.488 81.000 10.672 110.590 60.301 100.484 140.098 60.620 40.306 60.341 100.259 120.125 120.434 80.796 100.402 80.499 140.513 90.909 100.439 4
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 90.667 110.712 100.595 50.259 120.550 120.000 180.613 50.175 110.250 130.434 40.437 30.411 100.857 60.485 40.591 120.267 160.944 80.359 8
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
ResNet-backbone0.459 101.000 10.737 70.159 170.259 110.587 100.138 30.475 110.217 80.416 70.408 70.128 110.315 120.714 110.411 70.536 130.590 30.873 130.304 10
MASCpermissive0.447 110.528 150.555 130.381 100.382 70.633 70.002 160.509 90.260 70.361 80.432 50.327 70.451 50.571 120.367 110.639 80.386 100.980 70.276 11
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 121.000 10.432 150.245 130.190 130.577 110.013 130.263 130.033 170.320 110.240 130.075 140.422 90.857 60.117 150.699 50.271 150.883 120.235 13
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
UNet-backbone0.319 130.667 110.715 90.233 140.189 140.479 150.008 140.218 140.067 160.201 140.173 140.107 130.123 150.438 130.150 130.615 100.355 110.916 90.093 18
R-PointNet0.306 140.500 160.405 160.311 110.348 80.589 90.054 90.068 170.126 120.283 120.290 110.028 160.219 130.214 160.331 120.396 160.275 140.821 150.245 12
RegionNet0.248 150.667 110.437 140.188 150.153 150.491 130.000 180.208 150.094 140.153 150.099 160.057 150.217 140.119 170.039 180.466 150.302 130.640 160.140 15
3D-BEVIS0.248 150.667 110.566 120.076 180.035 190.394 160.027 110.035 180.098 130.099 170.030 180.025 170.098 160.375 140.126 140.604 110.181 170.854 140.171 14
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Seg-Clusterpermissive0.215 170.370 170.337 180.285 120.105 160.325 170.025 120.282 120.085 150.105 160.107 150.007 190.079 170.317 150.114 160.309 180.304 120.587 170.123 17
Sgpn_scannet0.143 180.208 190.390 170.169 160.065 170.275 180.029 100.069 160.000 180.087 180.043 170.014 180.027 190.000 180.112 170.351 170.168 180.438 180.138 16
MaskRCNN 2d->3d Proj0.058 190.333 180.002 190.000 190.053 180.002 190.002 170.021 190.000 180.045 190.024 190.238 80.065 180.000 180.014 190.107 190.020 190.110 190.006 19

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
CU-Hybrid-2D Net0.636 10.825 10.820 10.179 130.648 10.463 10.549 10.742 10.676 10.628 10.961 10.420 10.379 30.684 10.381 70.732 10.723 10.599 10.827 50.851 10.634 1
DMMF_3d0.605 20.651 40.744 50.782 10.637 20.387 20.536 20.732 20.590 30.540 20.856 90.359 40.306 80.596 40.539 10.627 80.706 20.497 40.785 90.757 80.476 9
DMMF0.597 30.543 80.755 40.749 20.585 40.338 40.494 40.704 40.598 20.494 80.911 40.347 60.327 70.593 50.527 20.675 40.646 60.513 20.842 30.774 60.527 7
MCA-Net0.595 40.533 90.756 30.746 30.590 30.334 60.506 30.670 50.587 40.500 60.905 60.366 30.352 40.601 30.506 40.669 70.648 40.501 30.839 40.769 70.516 8
RFBNet0.592 50.616 50.758 20.659 40.581 50.330 70.469 50.655 80.543 70.524 30.924 20.355 50.336 60.572 60.479 50.671 50.648 40.480 50.814 70.814 20.614 3
DCRedNet0.583 60.682 30.723 60.542 70.510 80.310 90.451 60.668 60.549 60.520 40.920 30.375 20.446 10.528 80.417 60.670 60.577 100.478 60.862 20.806 30.628 2
SSMAcopyleft0.577 70.695 20.716 80.439 90.563 60.314 80.444 70.719 30.551 50.503 50.887 80.346 70.348 50.603 20.353 90.709 20.600 80.457 80.901 10.786 40.599 4
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
FuseNetpermissive0.535 80.570 70.681 90.182 120.512 70.290 100.431 80.659 70.504 90.495 70.903 70.308 80.428 20.523 90.365 80.676 30.621 70.470 70.762 100.779 50.541 6
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 90.613 60.722 70.418 100.358 130.337 50.370 110.479 110.443 100.368 110.907 50.207 110.213 120.464 110.525 30.618 90.657 30.450 90.788 80.721 100.408 12
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 100.481 110.612 100.579 60.456 100.343 30.384 90.623 90.525 80.381 100.845 100.254 100.264 100.557 70.182 110.581 110.598 90.429 100.760 110.661 120.446 11
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ILC-PSPNet0.475 110.490 100.581 110.289 110.507 90.067 130.379 100.610 100.417 120.435 90.822 120.278 90.267 90.503 100.228 100.616 100.533 110.375 110.820 60.729 90.560 5
Enet (reimpl)0.376 120.264 130.452 130.452 80.365 110.181 110.143 130.456 120.409 130.346 120.769 130.164 120.218 110.359 120.123 130.403 130.381 130.313 130.571 120.685 110.472 10
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)permissive0.330 130.293 120.521 120.657 50.361 120.161 120.250 120.004 130.440 110.183 130.836 110.125 130.060 130.319 130.132 120.417 120.412 120.344 120.541 130.427 130.109 13
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

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
MaskRCNN_ScanNetpermissive0.119 10.129 10.212 10.002 10.112 10.148 10.014 10.205 10.044 10.066 10.078 10.095 10.142 10.030 10.128 10.139 10.080 10.459 10.057 1
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort by
multi-task learnerpermissive0.700 10.500 11.000 10.882 20.500 11.000 11.000 10.500 11.000 11.000 10.778 10.000 20.938 10.000 1
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D.
SE-ResNeXt-SSMA0.498 20.000 30.812 20.941 10.500 10.500 20.500 20.500 10.429 30.500 20.667 20.500 10.625 20.000 1
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 30.250 20.812 20.529 30.500 10.500 20.000 30.500 10.571 20.000 30.556 30.000 20.375 30.000 1