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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted by
MinkowskiNetpermissive0.736 20.859 30.818 20.832 30.709 40.840 30.521 40.853 20.660 20.643 20.951 60.544 30.286 90.731 30.893 10.675 90.772 60.683 20.874 140.852 30.727 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
OccuSeg+Semantic0.764 10.758 160.796 40.839 20.746 10.907 10.562 10.850 30.680 10.672 10.978 10.610 10.335 10.777 10.819 70.847 10.830 10.691 10.972 10.885 10.727 1
PointASNLpermissive0.666 80.703 210.781 60.751 150.655 70.830 50.471 100.769 90.474 260.537 100.951 60.475 80.279 110.635 60.698 130.675 90.751 100.553 190.816 240.806 100.703 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
MatchingNet0.724 40.812 100.812 30.810 50.735 20.834 40.495 70.860 10.572 120.602 50.954 30.512 40.280 100.757 20.845 50.725 30.780 50.606 80.937 20.851 40.700 4
SparseConvNet0.725 30.647 250.821 10.846 10.721 30.869 20.533 20.754 100.603 60.614 30.955 20.572 20.325 20.710 40.870 20.724 40.823 20.628 50.934 30.865 20.683 5
CU-Hybrid Net0.693 60.596 290.789 50.803 70.677 50.800 110.469 110.846 40.554 160.591 70.948 120.500 50.316 40.609 70.847 40.732 20.808 30.593 90.894 80.839 50.652 6
JSENet0.699 50.881 10.762 90.821 40.667 60.800 110.522 30.792 60.613 40.607 40.935 230.492 70.205 200.576 120.853 30.691 60.758 80.652 40.872 160.828 60.649 7
PointConvpermissive0.666 80.781 120.759 110.699 170.644 90.822 70.475 80.779 70.564 150.504 180.953 40.428 190.203 220.586 100.754 90.661 120.753 90.588 100.902 40.813 90.642 8
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
DCM-Net0.658 100.778 130.702 180.806 60.619 110.813 90.468 120.693 180.494 230.524 140.941 170.449 140.298 50.510 180.821 60.675 90.727 110.568 130.826 210.803 120.637 9
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
KP-FCNN0.684 70.847 50.758 120.784 90.647 80.814 80.473 90.772 80.605 50.594 60.935 230.450 130.181 270.587 90.805 80.690 70.785 40.614 60.882 100.819 70.632 10
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
FusionAwareConv0.630 170.604 280.741 140.766 120.590 150.747 240.501 60.734 110.503 210.527 120.919 310.454 120.323 30.550 140.420 280.678 80.688 180.544 210.896 70.795 150.627 11
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
MCCNNpermissive0.633 150.866 20.731 150.771 100.576 180.809 100.410 200.684 190.497 220.491 190.949 100.466 90.105 360.581 110.646 160.620 180.680 200.542 220.817 230.795 150.618 12
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
LAP-D0.594 220.720 190.692 240.637 250.456 290.773 190.391 250.730 120.587 80.445 260.940 190.381 240.288 70.434 270.453 250.591 220.649 230.581 110.777 280.749 270.610 13
MVPNetpermissive0.641 110.831 70.715 170.671 200.590 150.781 170.394 220.679 210.642 30.553 90.937 210.462 100.256 120.649 50.406 290.626 170.691 170.666 30.877 110.792 170.608 14
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
HPEIN0.618 200.729 180.668 260.647 230.597 140.766 210.414 190.680 200.520 180.525 130.946 140.432 170.215 180.493 250.599 190.638 150.617 290.570 120.897 60.806 100.605 15
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
CCRFNet0.589 240.766 140.659 290.683 190.470 280.740 260.387 260.620 270.490 240.476 220.922 290.355 290.245 140.511 170.511 210.571 250.643 250.493 280.872 160.762 230.600 16
joint point-basedpermissive0.634 140.614 270.778 70.667 220.633 100.825 60.420 180.804 50.467 280.561 80.951 60.494 60.291 60.566 130.458 240.579 240.764 70.559 180.838 200.814 80.598 17
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
SConv0.636 130.830 80.697 210.752 140.572 190.780 180.445 150.716 150.529 170.530 110.951 60.446 150.170 280.507 200.666 150.636 160.682 190.541 230.886 90.799 130.594 18
DPC0.592 230.720 190.700 190.602 280.480 260.762 220.380 270.713 160.585 90.437 270.940 190.369 260.288 70.434 270.509 220.590 230.639 270.567 150.772 290.755 250.592 19
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
SIConv0.625 180.830 80.694 230.757 130.563 200.772 200.448 130.647 250.520 180.509 160.949 100.431 180.191 250.496 230.614 180.647 130.672 210.535 240.876 120.783 180.571 20
TextureNetpermissive0.566 260.672 230.664 270.671 200.494 240.719 270.445 150.678 220.411 330.396 280.935 230.356 280.225 160.412 290.535 200.565 260.636 280.464 300.794 270.680 330.568 21
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
FPConvpermissive0.639 120.785 110.760 100.713 160.603 130.798 130.392 230.534 310.603 60.524 140.948 120.457 110.250 130.538 150.723 110.598 200.696 160.614 60.872 160.799 130.567 22
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PanopticFusion-label0.529 290.491 360.688 250.604 270.386 320.632 360.225 400.705 170.434 310.293 350.815 390.348 300.241 150.499 220.669 140.507 280.649 230.442 340.796 260.602 390.561 23
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
3DMV0.484 320.484 370.538 380.643 240.424 300.606 390.310 300.574 290.433 320.378 300.796 400.301 320.214 190.537 160.208 380.472 330.507 390.413 370.693 340.602 390.539 24
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SPH3D-GCNpermissive0.610 210.858 40.772 80.489 350.532 220.792 140.404 210.643 260.570 130.507 170.935 230.414 210.046 410.510 180.702 120.602 190.705 130.549 200.859 190.773 210.534 25
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
DVVNet0.562 270.648 240.700 190.770 110.586 170.687 310.333 290.650 230.514 200.475 230.906 350.359 270.223 170.340 320.442 270.422 350.668 220.501 260.708 330.779 200.534 25
Pointnet++ & Featurepermissive0.557 280.735 170.661 280.686 180.491 250.744 250.392 230.539 300.451 290.375 310.946 140.376 250.205 200.403 300.356 310.553 270.643 250.497 270.824 220.756 240.515 27
FRPointConv0.619 190.762 150.697 210.602 280.547 210.782 160.432 170.650 230.581 100.458 240.952 50.428 190.130 330.496 230.747 100.641 140.710 120.568 130.902 40.782 190.511 28
PCNN0.498 310.559 320.644 320.560 320.420 310.711 290.229 380.414 330.436 300.352 320.941 170.324 310.155 310.238 370.387 300.493 290.529 360.509 250.813 250.751 260.504 29
SegGCNpermissive0.589 240.833 60.731 150.539 330.514 230.789 150.448 130.467 320.573 110.484 210.936 220.396 220.061 400.501 210.507 230.594 210.700 150.563 160.874 140.771 220.493 30
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
3DSM_DMMF0.631 160.626 260.745 130.801 80.607 120.751 230.506 50.729 130.565 140.491 190.866 380.434 160.197 240.595 80.630 170.709 50.705 130.560 170.875 130.740 280.491 31
3DMV, FTSDF0.501 300.558 330.608 350.424 400.478 270.690 300.246 360.586 280.468 270.450 250.911 330.394 230.160 300.438 260.212 370.432 340.541 350.475 290.742 310.727 290.477 32
PointCNN with RGBpermissive0.458 330.577 310.611 340.356 420.321 380.715 280.299 320.376 360.328 390.319 330.944 160.285 340.164 290.216 400.229 360.484 310.545 340.456 320.755 300.709 300.475 33
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PNET20.442 350.548 340.548 370.597 300.363 350.628 370.300 310.292 370.374 360.307 340.881 370.268 350.186 260.238 370.204 390.407 360.506 400.449 330.667 350.620 370.462 34
SurfaceConvPF0.442 350.505 350.622 330.380 410.342 370.654 340.227 390.397 350.367 370.276 370.924 280.240 380.198 230.359 310.262 340.366 370.581 310.435 350.640 360.668 340.398 35
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
ScanNet+FTSDF0.383 400.297 420.491 400.432 390.358 360.612 380.274 340.116 410.411 330.265 380.904 360.229 390.079 380.250 350.185 400.320 400.510 370.385 380.548 390.597 410.394 36
Tangent Convolutionspermissive0.438 370.437 400.646 310.474 370.369 340.645 350.353 280.258 390.282 410.279 360.918 320.298 330.147 320.283 340.294 330.487 300.562 320.427 360.619 370.633 360.352 37
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak0.411 380.479 380.650 300.475 360.285 410.519 420.087 430.725 140.396 350.386 290.621 430.250 370.117 340.338 330.443 260.188 430.594 300.369 400.377 430.616 380.306 38
SSC-UNetpermissive0.308 420.353 410.290 430.278 430.166 430.553 400.169 420.286 380.147 430.148 430.908 340.182 420.064 390.023 430.018 440.354 390.363 410.345 410.546 410.685 320.278 39
SPLAT Netcopyleft0.393 390.472 390.511 390.606 260.311 390.656 330.245 370.405 340.328 390.197 410.927 270.227 400.000 440.001 440.249 350.271 420.510 370.383 390.593 380.699 310.267 40
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
PointNet++permissive0.339 410.584 300.478 410.458 380.256 420.360 430.250 350.247 400.278 420.261 390.677 420.183 410.117 340.212 410.145 420.364 380.346 430.232 430.548 390.523 420.252 41
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
FCPNpermissive0.447 340.679 220.604 360.578 310.380 330.682 320.291 330.106 420.483 250.258 400.920 300.258 360.025 420.231 390.325 320.480 320.560 330.463 310.725 320.666 350.231 42
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ScanNetpermissive0.306 430.203 430.366 420.501 340.311 390.524 410.211 410.002 440.342 380.189 420.786 410.145 430.102 370.245 360.152 410.318 410.348 420.300 420.460 420.437 430.182 43
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 440.000 440.041 440.172 440.030 440.062 440.001 440.035 430.004 440.051 440.143 440.019 440.003 430.041 420.050 430.003 440.054 440.018 440.005 440.264 440.082 44

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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted by
PointGroup0.636 31.000 10.765 50.624 40.505 40.797 30.116 60.696 30.384 50.441 50.559 20.476 10.596 41.000 10.666 20.756 40.556 80.997 60.513 1
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral]
PCJC0.578 51.000 10.810 30.583 80.449 80.813 20.042 110.603 60.341 70.490 20.465 40.410 40.650 20.835 100.264 140.694 60.561 60.889 130.504 2
GICN0.638 21.000 10.895 10.800 10.480 50.676 70.144 30.737 20.354 60.447 40.400 100.365 60.700 11.000 10.569 30.836 10.599 21.000 10.473 3
OccuSeg+instance0.672 11.000 10.758 70.682 30.576 10.842 10.477 10.504 110.524 20.567 10.585 10.451 20.557 61.000 10.751 10.797 20.563 51.000 10.467 4
3D-BoNet0.488 101.000 10.672 120.590 60.301 120.484 170.098 70.620 40.306 80.341 120.259 140.125 140.434 100.796 110.402 90.499 160.513 100.909 120.439 5
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
3D-MPA0.611 41.000 10.833 20.765 20.526 20.756 60.136 50.588 80.470 30.438 60.432 80.358 70.650 20.857 60.429 70.765 30.557 71.000 10.430 6
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
MTML0.549 71.000 10.807 40.588 70.327 110.647 80.004 170.815 10.180 110.418 70.364 120.182 100.445 81.000 10.442 60.688 80.571 41.000 10.396 7
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Occipital-SCS0.512 91.000 10.716 90.509 90.506 30.611 100.092 80.602 70.177 120.346 110.383 110.165 120.442 90.850 90.386 110.618 110.543 90.889 130.389 8
PanopticFusion-inst0.478 110.667 130.712 110.595 50.259 140.550 150.000 200.613 50.175 130.250 150.434 60.437 30.411 120.857 60.485 50.591 140.267 180.944 90.359 9
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SSEN0.575 61.000 10.761 60.473 100.477 60.795 40.066 90.529 90.658 10.460 30.461 50.380 50.331 130.859 50.401 100.692 70.653 11.000 10.348 10
Sparse R-CNN0.515 81.000 10.538 150.282 140.468 70.790 50.173 20.345 130.429 40.413 90.484 30.176 110.595 50.591 130.522 40.668 90.476 110.986 70.327 11
ResNet-backbone0.459 121.000 10.737 80.159 190.259 130.587 130.138 40.475 120.217 100.416 80.408 90.128 130.315 140.714 120.411 80.536 150.590 30.873 160.304 12
MASCpermissive0.447 130.528 170.555 140.381 110.382 90.633 90.002 180.509 100.260 90.361 100.432 70.327 80.451 70.571 140.367 120.639 100.386 120.980 80.276 13
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
R-PointNet0.306 160.500 190.405 180.311 120.348 100.589 120.054 100.068 190.126 140.283 140.290 130.028 180.219 150.214 190.331 130.396 180.275 160.821 180.245 14
3D-SISpermissive0.382 141.000 10.432 170.245 150.190 150.577 140.013 150.263 150.033 190.320 130.240 150.075 160.422 110.857 60.117 170.699 50.271 170.883 150.235 15
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
3D-BEVIS0.248 170.667 130.566 130.076 200.035 220.394 190.027 130.035 200.098 150.099 200.030 210.025 190.098 180.375 170.126 160.604 130.181 200.854 170.171 16
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Region0.248 170.667 130.437 160.188 170.153 170.491 160.000 200.208 170.094 160.153 180.099 180.057 170.217 160.119 200.039 210.466 170.302 150.640 190.140 17
Sgpn_scannet0.143 210.208 220.390 190.169 180.065 190.275 210.029 120.069 180.000 210.087 210.043 190.014 200.027 210.000 210.112 190.351 190.168 210.438 210.138 18
Seg-Clusterpermissive0.215 190.370 200.337 200.285 130.105 180.325 200.025 140.282 140.085 170.105 190.107 170.007 220.079 190.317 180.114 180.309 200.304 140.587 200.123 19
UNet-backbone0.319 150.667 130.715 100.233 160.189 160.479 180.008 160.218 160.067 180.201 160.173 160.107 150.123 170.438 160.150 150.615 120.355 130.916 110.093 20
PE0.188 200.528 170.046 210.017 210.062 200.600 110.000 200.003 220.030 200.177 170.032 200.013 210.000 220.487 150.090 200.093 220.261 190.916 100.029 21
MaskRCNN 2d->3d Proj0.058 220.333 210.002 220.000 220.053 210.002 220.002 190.021 210.000 210.045 220.024 220.238 90.065 200.000 210.014 220.107 210.020 220.110 220.006 22

This table lists the benchmark results for the 2D semantic label scenario.


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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
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
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
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
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
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
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
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
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.
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
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
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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 bysorted bysort bysort bysort 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