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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
OccuSeg+Semantic0.764 10.758 150.796 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
SparseConvNet0.725 30.647 240.821 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
JSENet0.699 50.881 10.762 90.821 40.667 60.800 110.522 30.792 60.613 40.607 40.935 220.492 70.205 200.576 120.853 30.691 60.758 80.652 40.872 150.828 60.649 7
MinkowskiNetpermissive0.736 20.859 30.818 20.832 30.709 40.840 30.521 40.853 20.660 20.643 20.951 50.544 30.286 90.731 30.893 10.675 90.772 60.683 20.874 130.852 30.727 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
3DSM_DMMF0.631 160.626 250.745 130.801 80.607 120.751 220.506 50.729 130.565 130.491 190.866 370.434 160.197 240.595 80.630 160.709 50.705 120.560 160.875 120.740 270.491 30
FusionAwareConv0.630 170.604 270.741 140.766 120.590 150.747 230.501 60.734 110.503 200.527 120.919 300.454 120.323 30.550 140.420 270.678 80.688 170.544 200.896 60.795 150.627 11
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
MatchingNet0.724 40.812 100.812 30.810 50.735 20.834 40.495 70.860 10.572 110.602 50.954 30.512 40.280 100.757 20.845 50.725 30.780 50.606 80.937 20.851 40.700 4
PointConvpermissive0.666 80.781 120.759 110.699 170.644 90.822 70.475 80.779 70.564 140.504 180.953 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
KP-FCNN0.684 70.847 50.758 120.784 90.647 80.814 80.473 90.772 80.605 50.594 60.935 220.450 130.181 270.587 90.805 80.690 70.785 40.614 60.882 90.819 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
PointASNLpermissive0.666 80.703 200.781 60.751 150.655 70.830 50.471 100.769 90.474 250.537 100.951 50.475 80.279 110.635 60.698 120.675 90.751 100.553 180.816 230.806 100.703 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
CU-Hybrid Net0.693 60.596 280.789 50.803 70.677 50.800 110.469 110.846 40.554 150.591 70.948 110.500 50.316 40.609 70.847 40.732 20.808 30.593 90.894 70.839 50.652 6
DCM-Net0.658 100.778 130.702 180.806 60.619 110.813 90.468 120.693 180.494 220.524 140.941 160.449 140.298 50.510 180.821 60.675 90.727 110.568 130.826 200.803 120.637 9
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
SIConv0.625 180.830 80.694 220.757 130.563 200.772 190.448 130.647 240.520 170.509 160.949 90.431 180.191 250.496 230.614 170.647 130.672 200.535 230.876 110.783 180.571 20
SegGCNpermissive0.589 230.833 60.731 150.539 320.514 220.789 150.448 130.467 310.573 100.484 210.936 210.396 210.061 390.501 210.507 220.594 200.700 140.563 150.874 130.771 210.493 29
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
TextureNetpermissive0.566 250.672 220.664 260.671 200.494 230.719 260.445 150.678 220.411 320.396 270.935 220.356 270.225 160.412 280.535 190.565 250.636 270.464 290.794 260.680 320.568 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
SConv0.636 130.830 80.697 210.752 140.572 190.780 170.445 150.716 150.529 160.530 110.951 50.446 150.170 280.507 200.666 140.636 150.682 180.541 220.886 80.799 130.594 18
joint point-basedpermissive0.634 140.614 260.778 70.667 220.633 100.825 60.420 170.804 50.467 270.561 80.951 50.494 60.291 60.566 130.458 230.579 230.764 70.559 170.838 190.814 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
HPEIN0.618 190.729 170.668 250.647 230.597 140.766 200.414 180.680 200.520 170.525 130.946 130.432 170.215 180.493 240.599 180.638 140.617 280.570 120.897 50.806 100.605 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
MCCNNpermissive0.633 150.866 20.731 150.771 100.576 180.809 100.410 190.684 190.497 210.491 190.949 90.466 90.105 350.581 110.646 150.620 170.680 190.542 210.817 220.795 150.618 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
SPH3D-GCNpermissive0.610 200.858 40.772 80.489 340.532 210.792 140.404 200.643 250.570 120.507 170.935 220.414 200.046 400.510 180.702 110.602 180.705 120.549 190.859 180.773 200.534 25
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
MVPNetpermissive0.641 110.831 70.715 170.671 200.590 150.781 160.394 210.679 210.642 30.553 90.937 200.462 100.256 120.649 50.406 280.626 160.691 160.666 30.877 100.792 170.608 14
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
Pointnet++ & Featurepermissive0.557 270.735 160.661 270.686 180.491 240.744 240.392 220.539 290.451 280.375 300.946 130.376 240.205 200.403 290.356 300.553 260.643 240.497 260.824 210.756 230.515 27
FPConvpermissive0.639 120.785 110.760 100.713 160.603 130.798 130.392 220.534 300.603 60.524 140.948 110.457 110.250 130.538 150.723 100.598 190.696 150.614 60.872 150.799 130.567 22
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
LAP-D0.594 210.720 180.692 230.637 250.456 280.773 180.391 240.730 120.587 80.445 250.940 180.381 230.288 70.434 260.453 240.591 210.649 220.581 110.777 270.749 260.610 13
CCRFNet0.589 230.766 140.659 280.683 190.470 270.740 250.387 250.620 260.490 230.476 220.922 280.355 280.245 140.511 170.511 200.571 240.643 240.493 270.872 150.762 220.600 16
DPC0.592 220.720 180.700 190.602 280.480 250.762 210.380 260.713 160.585 90.437 260.940 180.369 250.288 70.434 260.509 210.590 220.639 260.567 140.772 280.755 240.592 19
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
Tangent Convolutionspermissive0.438 360.437 390.646 300.474 360.369 330.645 340.353 270.258 380.282 400.279 350.918 310.298 320.147 320.283 330.294 320.487 290.562 310.427 350.619 360.633 350.352 36
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
DVVNet0.562 260.648 230.700 190.770 110.586 170.687 300.333 280.650 230.514 190.475 230.906 340.359 260.223 170.340 310.442 260.422 340.668 210.501 250.708 320.779 190.534 25
3DMV0.484 310.484 360.538 370.643 240.424 290.606 380.310 290.574 280.433 310.378 290.796 390.301 310.214 190.537 160.208 370.472 320.507 380.413 360.693 330.602 380.539 24
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PNET20.442 340.548 330.548 360.597 290.363 340.628 360.300 300.292 360.374 350.307 330.881 360.268 340.186 260.238 360.204 380.407 350.506 390.449 320.667 340.620 360.462 33
PointCNN with RGBpermissive0.458 320.577 300.611 330.356 410.321 370.715 270.299 310.376 350.328 380.319 320.944 150.285 330.164 290.216 390.229 350.484 300.545 330.456 310.755 290.709 290.475 32
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 330.679 210.604 350.578 300.380 320.682 310.291 320.106 410.483 240.258 390.920 290.258 350.025 410.231 380.325 310.480 310.560 320.463 300.725 310.666 340.231 41
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ScanNet+FTSDF0.383 390.297 410.491 390.432 380.358 350.612 370.274 330.116 400.411 320.265 370.904 350.229 380.079 370.250 340.185 390.320 390.510 360.385 370.548 380.597 400.394 35
PointNet++permissive0.339 400.584 290.478 400.458 370.256 410.360 420.250 340.247 390.278 410.261 380.677 410.183 400.117 330.212 400.145 410.364 370.346 420.232 420.548 380.523 410.252 40
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
3DMV, FTSDF0.501 290.558 320.608 340.424 390.478 260.690 290.246 350.586 270.468 260.450 240.911 320.394 220.160 300.438 250.212 360.432 330.541 340.475 280.742 300.727 280.477 31
SPLAT Netcopyleft0.393 380.472 380.511 380.606 260.311 380.656 320.245 360.405 330.328 380.197 400.927 260.227 390.000 430.001 430.249 340.271 410.510 360.383 380.593 370.699 300.267 39
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
PCNN0.498 300.559 310.644 310.560 310.420 300.711 280.229 370.414 320.436 290.352 310.941 160.324 300.155 310.238 360.387 290.493 280.529 350.509 240.813 240.751 250.504 28
SurfaceConvPF0.442 340.505 340.622 320.380 400.342 360.654 330.227 380.397 340.367 360.276 360.924 270.240 370.198 230.359 300.262 330.366 360.581 300.435 340.640 350.668 330.398 34
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PanopticFusion-label0.529 280.491 350.688 240.604 270.386 310.632 350.225 390.705 170.434 300.293 340.815 380.348 290.241 150.499 220.669 130.507 270.649 220.442 330.796 250.602 380.561 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)
ScanNetpermissive0.306 420.203 420.366 410.501 330.311 380.524 400.211 400.002 430.342 370.189 410.786 400.145 420.102 360.245 350.152 400.318 400.348 410.300 410.460 410.437 420.182 42
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SSC-UNetpermissive0.308 410.353 400.290 420.278 420.166 420.553 390.169 410.286 370.147 420.148 420.908 330.182 410.064 380.023 420.018 430.354 380.363 400.345 400.546 400.685 310.278 38
subcloud_weak0.411 370.479 370.650 290.475 350.285 400.519 410.087 420.725 140.396 340.386 280.621 420.250 360.117 330.338 320.443 250.188 420.594 290.369 390.377 420.616 370.306 37
ERROR0.054 430.000 430.041 430.172 430.030 430.062 430.001 430.035 420.004 430.051 430.143 430.019 430.003 420.041 410.050 420.003 430.054 430.018 430.005 430.264 430.082 43

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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Sparse R-CNN0.714 71.000 10.926 50.694 100.699 70.890 10.636 10.516 120.693 90.743 10.588 50.369 100.601 40.594 140.800 60.886 20.676 80.986 70.546 10
SSEN0.724 61.000 10.926 40.781 60.661 90.845 40.596 20.529 110.764 50.653 30.489 130.461 80.500 120.859 90.765 80.872 40.761 11.000 10.577 7
OccuSeg+instance0.742 31.000 10.923 60.785 40.745 40.867 20.557 30.578 90.729 60.670 20.644 20.488 60.577 61.000 10.794 70.830 80.620 111.000 10.550 9
3D-BoNet0.687 111.000 10.887 100.836 20.587 140.643 170.550 40.620 60.724 70.522 120.501 110.243 150.512 101.000 10.751 100.807 110.661 90.909 150.612 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
GICN0.788 11.000 10.978 20.867 10.781 20.833 50.527 50.824 30.806 30.549 90.596 40.551 40.700 11.000 10.853 20.935 10.733 21.000 10.651 2
PointGroup0.778 21.000 10.900 90.798 30.715 60.863 30.493 60.706 50.895 10.569 80.701 10.576 20.639 31.000 10.880 10.851 60.719 40.997 60.709 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]
ResNet-backbone0.695 81.000 10.855 110.579 140.589 130.735 130.484 70.588 70.856 20.634 40.571 60.298 110.500 121.000 10.824 30.818 90.702 60.935 130.545 11
Occipital-SCS0.688 101.000 10.913 70.730 90.737 50.743 120.442 80.855 20.655 100.546 100.546 90.263 140.508 110.889 80.568 140.771 130.705 50.889 160.625 3
R-PointNet0.544 150.500 210.655 170.661 110.663 80.765 90.432 90.214 180.612 110.584 70.499 120.204 160.286 170.429 170.655 110.650 180.539 120.950 100.499 13
UNet-backbone0.605 131.000 10.909 80.764 80.603 120.704 140.415 100.301 160.548 140.461 140.394 140.267 130.386 140.857 100.649 120.817 100.504 140.959 90.356 16
Seg-Clusterpermissive0.380 190.625 180.420 200.456 170.296 190.473 200.390 110.433 130.293 200.322 190.247 170.066 210.264 180.325 190.388 170.486 190.401 190.614 200.341 17
PanopticFusion-inst0.693 91.000 10.852 120.655 120.616 100.788 70.334 120.763 40.771 40.457 150.555 80.652 10.518 90.857 100.765 80.732 150.631 100.944 110.577 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)
MTML0.731 51.000 10.992 10.779 70.609 110.746 110.308 130.867 10.601 120.607 60.539 100.519 50.550 71.000 10.824 30.869 50.729 31.000 10.616 4
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
MaskRCNN 2d->3d Proj0.261 210.903 150.081 210.008 210.233 200.175 210.280 140.106 200.150 210.203 210.175 190.480 70.218 200.143 200.542 150.404 210.153 210.393 210.049 21
3D-MPA0.737 41.000 10.933 30.785 40.794 10.831 60.279 150.588 70.695 80.616 50.559 70.556 30.650 21.000 10.809 50.875 30.696 71.000 10.608 6
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
Sgpn_scannet0.390 180.556 190.636 180.493 160.353 170.539 190.271 160.160 190.450 170.359 180.178 180.146 170.250 190.143 200.347 200.698 170.436 170.667 190.331 18
3D-SISpermissive0.558 141.000 10.773 140.614 130.503 150.691 150.200 170.412 140.498 160.546 110.311 160.103 190.600 50.857 100.382 180.799 120.445 160.938 120.371 14
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
3D-BEVIS0.401 170.667 170.687 160.419 190.137 210.587 180.188 180.235 170.359 180.211 200.093 210.080 200.311 160.571 150.382 180.754 140.300 200.874 170.357 15
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
PE0.374 200.528 200.446 190.190 200.347 180.747 100.187 190.099 210.319 190.411 170.120 200.278 120.000 210.563 160.413 160.466 200.435 180.920 140.268 20
MASCpermissive0.615 120.711 160.802 130.540 150.757 30.777 80.029 200.577 100.588 130.521 130.600 30.436 90.534 80.697 130.616 130.838 70.526 130.980 80.534 12
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
Region0.474 161.000 10.727 150.433 180.481 160.673 160.022 210.380 150.517 150.436 160.338 150.128 180.343 150.429 170.291 210.728 160.473 150.833 180.300 19

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


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

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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysorted 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
sorted bysort bysort bysort bysort bysort bysort 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