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 10.758 290.796 70.839 40.746 20.907 10.562 10.850 50.680 20.672 10.978 10.610 10.335 30.777 10.819 140.847 10.830 10.691 30.972 10.885 10.727 2
BPNet0.749 20.909 10.818 40.811 80.752 10.839 60.485 120.842 70.673 30.644 40.957 30.528 70.305 90.773 20.859 40.788 20.818 30.693 20.916 60.856 50.723 4
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021
VMNet0.746 30.870 50.838 10.858 10.729 40.850 40.501 70.874 20.587 200.658 30.956 40.564 40.299 100.765 30.900 10.716 70.812 40.631 100.939 20.858 40.709 5
Virtual MVFusion0.746 30.771 250.819 30.848 20.702 80.865 30.397 420.899 10.699 10.664 20.948 200.588 20.330 40.746 50.851 70.764 30.796 70.704 10.935 40.866 20.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
MinkowskiNetpermissive0.736 50.859 70.818 40.832 50.709 70.840 50.521 40.853 40.660 40.643 50.951 100.544 50.286 150.731 60.893 20.675 170.772 130.683 40.874 270.852 60.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 60.647 470.821 20.846 30.721 50.869 20.533 20.754 190.603 170.614 60.955 50.572 30.325 50.710 70.870 30.724 60.823 20.628 110.934 50.865 30.683 8
MatchingNet0.724 70.812 170.812 60.810 90.735 30.834 80.495 100.860 30.572 250.602 100.954 60.512 90.280 160.757 40.845 110.725 50.780 100.606 190.937 30.851 70.700 7
RFCR0.702 80.889 30.745 200.813 70.672 100.818 180.493 110.815 90.623 110.610 70.947 230.470 190.249 250.594 200.848 80.705 110.779 110.646 70.892 170.823 120.611 23
One Thing One Click0.701 90.825 150.796 70.723 250.716 60.832 90.433 320.816 80.634 80.609 80.969 20.418 400.344 20.559 310.833 120.715 80.808 50.560 310.902 100.847 80.680 9
JSENet0.699 100.881 40.762 150.821 60.667 120.800 280.522 30.792 120.613 120.607 90.935 430.492 130.205 360.576 270.853 60.691 120.758 160.652 60.872 300.828 100.649 14
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
CU-Hybrid Net0.693 110.596 520.789 90.803 110.677 90.800 280.469 160.846 60.554 310.591 130.948 200.500 100.316 70.609 170.847 90.732 40.808 50.593 220.894 150.839 90.652 13
FusionNet0.688 120.704 400.741 230.754 220.656 130.829 110.501 70.741 240.609 140.548 180.950 140.522 80.371 10.633 140.756 210.715 80.771 140.623 120.861 350.814 170.658 12
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
KP-FCNN0.684 130.847 90.758 180.784 150.647 160.814 200.473 140.772 150.605 150.594 120.935 430.450 270.181 440.587 210.805 160.690 130.785 90.614 140.882 210.819 150.632 19
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointContrast_LA_SEM0.683 140.757 300.784 100.786 140.639 180.824 150.408 370.775 140.604 160.541 200.934 470.532 60.269 200.552 320.777 190.645 290.793 80.640 80.913 80.824 110.671 10
Feature_GeometricNetpermissive0.682 150.844 100.739 250.802 120.668 110.805 260.420 330.807 100.631 90.601 110.947 230.440 300.106 580.586 230.846 100.683 150.739 200.607 180.916 60.823 120.633 18
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
ROSMRF3D0.673 160.789 180.748 190.763 200.635 200.814 200.407 390.747 210.581 230.573 140.950 140.484 140.271 190.607 180.754 220.649 250.774 120.596 210.883 200.823 120.606 27
SALANet0.670 170.816 160.770 140.768 180.652 150.807 250.451 200.747 210.659 50.545 190.924 500.473 180.149 530.571 290.811 150.635 320.746 190.623 120.892 170.794 280.570 38
PointConvpermissive0.666 180.781 200.759 170.699 300.644 170.822 160.475 130.779 130.564 280.504 330.953 70.428 350.203 380.586 230.754 220.661 220.753 170.588 230.902 100.813 190.642 15
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 180.703 410.781 110.751 240.655 140.830 100.471 150.769 160.474 470.537 210.951 100.475 170.279 170.635 120.698 320.675 170.751 180.553 360.816 460.806 200.703 6
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
VACNN++0.664 200.891 20.724 300.680 360.636 190.814 200.438 310.629 460.553 320.537 210.950 140.499 110.247 260.626 150.786 180.666 200.700 330.566 290.860 360.816 160.665 11
DCM-Net0.658 210.778 210.702 350.806 100.619 220.813 230.468 170.693 350.494 430.524 270.941 350.449 280.298 110.510 380.821 130.675 170.727 240.568 270.826 430.803 220.637 17
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 220.698 420.743 220.650 450.564 370.820 170.505 60.758 180.631 90.479 390.945 280.480 150.226 290.572 280.774 200.690 130.735 230.614 140.853 390.776 400.597 31
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segcopyleft0.654 230.752 310.734 260.664 430.583 310.815 190.399 410.754 190.639 70.535 230.942 330.470 190.309 80.665 80.539 400.650 240.708 290.635 90.857 380.793 290.642 15
RandLA-Netpermissive0.645 240.778 210.731 270.699 300.577 320.829 110.446 240.736 250.477 460.523 290.945 280.454 250.269 200.484 450.749 250.618 360.738 210.599 200.827 420.792 310.621 21
MVPNetpermissive0.641 250.831 120.715 310.671 400.590 280.781 380.394 430.679 380.642 60.553 170.937 400.462 220.256 220.649 90.406 520.626 330.691 370.666 50.877 230.792 310.608 26
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 250.776 230.703 340.721 260.557 400.826 130.451 200.672 400.563 290.483 380.943 320.425 380.162 480.644 100.726 260.659 230.709 280.572 250.875 250.786 350.559 43
PointMRNet0.640 270.717 390.701 360.692 320.576 330.801 270.467 180.716 300.563 290.459 430.953 70.429 340.169 460.581 250.854 50.605 370.710 270.550 370.894 150.793 290.575 36
FPConvpermissive0.639 280.785 190.760 160.713 290.603 250.798 310.392 440.534 530.603 170.524 270.948 200.457 230.250 240.538 350.723 270.598 400.696 360.614 140.872 300.799 230.567 40
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointSPNet0.637 290.734 340.692 410.714 280.576 330.797 320.446 240.743 230.598 190.437 470.942 330.403 420.150 520.626 150.800 170.649 250.697 350.557 340.846 400.777 390.563 41
SConv0.636 300.830 130.697 390.752 230.572 360.780 390.445 260.716 300.529 360.530 240.951 100.446 290.170 450.507 400.666 340.636 310.682 390.541 420.886 190.799 230.594 32
Supervoxel-CNN0.635 310.656 450.711 320.719 270.613 230.757 450.444 280.765 170.534 350.566 150.928 480.478 160.272 180.636 110.531 420.664 210.645 470.508 460.864 340.792 310.611 23
joint point-basedpermissive0.634 320.614 500.778 120.667 420.633 210.825 140.420 330.804 110.467 490.561 160.951 100.494 120.291 120.566 300.458 460.579 450.764 150.559 330.838 410.814 170.598 30
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 330.866 60.731 270.771 160.576 330.809 240.410 360.684 360.497 420.491 350.949 170.466 210.105 590.581 250.646 350.620 340.680 400.542 410.817 450.795 260.618 22
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
PointMTL0.632 340.731 350.688 440.675 370.591 270.784 370.444 280.565 510.610 130.492 340.949 170.456 240.254 230.587 210.706 290.599 390.665 440.612 170.868 330.791 340.579 35
3DSM_DMMF0.631 350.626 490.745 200.801 130.607 240.751 460.506 50.729 280.565 270.491 350.866 610.434 310.197 400.595 190.630 360.709 100.705 310.560 310.875 250.740 500.491 52
PointNet2-SFPN0.631 350.771 250.692 410.672 380.524 430.837 70.440 300.706 330.538 340.446 450.944 300.421 390.219 320.552 320.751 240.591 420.737 220.543 400.901 120.768 430.557 44
APCF-Net0.631 350.742 320.687 460.672 380.557 400.792 340.408 370.665 410.545 330.508 310.952 90.428 350.186 420.634 130.702 300.620 340.706 300.555 350.873 290.798 250.581 34
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 380.604 510.741 230.766 190.590 280.747 470.501 70.734 260.503 410.527 250.919 540.454 250.323 60.550 340.420 510.678 160.688 380.544 390.896 140.795 260.627 20
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
SIConv0.625 390.830 130.694 400.757 210.563 380.772 410.448 220.647 440.520 380.509 300.949 170.431 330.191 410.496 430.614 370.647 280.672 420.535 440.876 240.783 360.571 37
HPEIN0.618 400.729 360.668 470.647 470.597 260.766 420.414 350.680 370.520 380.525 260.946 260.432 320.215 330.493 440.599 380.638 300.617 520.570 260.897 130.806 200.605 28
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-GCNpermissive0.610 410.858 80.772 130.489 580.532 420.792 340.404 400.643 450.570 260.507 320.935 430.414 410.046 640.510 380.702 300.602 380.705 310.549 380.859 370.773 410.534 46
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 420.760 280.667 480.649 460.521 440.793 330.457 190.648 430.528 370.434 490.947 230.401 430.153 510.454 460.721 280.648 270.717 250.536 430.904 90.765 440.485 53
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
LAP-D0.594 430.720 370.692 410.637 490.456 510.773 400.391 460.730 270.587 200.445 460.940 370.381 460.288 130.434 490.453 470.591 420.649 450.581 240.777 500.749 490.610 25
DPC0.592 440.720 370.700 370.602 520.480 480.762 440.380 490.713 320.585 220.437 470.940 370.369 480.288 130.434 490.509 440.590 440.639 500.567 280.772 510.755 470.592 33
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 450.766 270.659 510.683 340.470 500.740 490.387 480.620 470.490 440.476 400.922 520.355 520.245 270.511 370.511 430.571 460.643 480.493 490.872 300.762 450.600 29
SegGCNpermissive0.589 450.833 110.731 270.539 560.514 450.789 360.448 220.467 550.573 240.484 370.936 410.396 440.061 630.501 410.507 450.594 410.700 330.563 300.874 270.771 420.493 51
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
ROSMRF0.580 470.772 240.707 330.681 350.563 380.764 430.362 500.515 540.465 500.465 420.936 410.427 370.207 350.438 470.577 390.536 500.675 410.486 500.723 550.779 370.524 48
TextureNetpermissive0.566 480.672 440.664 490.671 400.494 460.719 500.445 260.678 390.411 560.396 500.935 430.356 510.225 300.412 510.535 410.565 470.636 510.464 530.794 490.680 560.568 39
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
DVVNet0.562 490.648 460.700 370.770 170.586 300.687 540.333 520.650 420.514 400.475 410.906 580.359 500.223 310.340 550.442 500.422 580.668 430.501 470.708 560.779 370.534 46
Pointnet++ & Featurepermissive0.557 500.735 330.661 500.686 330.491 470.744 480.392 440.539 520.451 520.375 530.946 260.376 470.205 360.403 520.356 540.553 490.643 480.497 480.824 440.756 460.515 49
PointMRNet-lite0.553 510.633 480.648 530.659 440.430 520.800 280.390 470.592 480.454 510.371 540.939 390.368 490.136 550.368 530.448 480.560 480.715 260.486 500.882 210.720 520.462 56
PanopticFusion-label0.529 520.491 590.688 440.604 510.386 550.632 590.225 630.705 340.434 540.293 580.815 620.348 530.241 280.499 420.669 330.507 510.649 450.442 570.796 480.602 620.561 42
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, FTSDF0.501 530.558 560.608 580.424 630.478 490.690 530.246 590.586 490.468 480.450 440.911 560.394 450.160 490.438 470.212 600.432 570.541 580.475 520.742 530.727 510.477 54
PCNN0.498 540.559 550.644 550.560 550.420 540.711 520.229 610.414 560.436 530.352 550.941 350.324 540.155 500.238 600.387 530.493 520.529 590.509 450.813 470.751 480.504 50
3DMV0.484 550.484 600.538 610.643 480.424 530.606 620.310 530.574 500.433 550.378 520.796 630.301 550.214 340.537 360.208 610.472 560.507 620.413 600.693 570.602 620.539 45
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 560.577 540.611 570.356 650.321 610.715 510.299 550.376 590.328 620.319 560.944 300.285 570.164 470.216 630.229 590.484 540.545 570.456 550.755 520.709 530.475 55
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 570.679 430.604 590.578 540.380 560.682 550.291 560.106 650.483 450.258 630.920 530.258 590.025 650.231 620.325 550.480 550.560 560.463 540.725 540.666 580.231 65
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PNET20.442 580.548 570.548 600.597 530.363 580.628 600.300 540.292 600.374 590.307 570.881 600.268 580.186 420.238 600.204 620.407 590.506 630.449 560.667 580.620 600.462 56
SurfaceConvPF0.442 580.505 580.622 560.380 640.342 600.654 570.227 620.397 580.367 600.276 600.924 500.240 610.198 390.359 540.262 570.366 600.581 540.435 580.640 590.668 570.398 58
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 600.437 630.646 540.474 600.369 570.645 580.353 510.258 620.282 640.279 590.918 550.298 560.147 540.283 570.294 560.487 530.562 550.427 590.619 600.633 590.352 60
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
subcloud_weak0.411 610.479 610.650 520.475 590.285 640.519 650.087 660.725 290.396 580.386 510.621 660.250 600.117 560.338 560.443 490.188 660.594 530.369 630.377 660.616 610.306 61
SPLAT Netcopyleft0.393 620.472 620.511 620.606 500.311 620.656 560.245 600.405 570.328 620.197 640.927 490.227 630.000 670.001 670.249 580.271 650.510 600.383 620.593 610.699 540.267 63
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+FTSDF0.383 630.297 650.491 630.432 620.358 590.612 610.274 570.116 640.411 560.265 610.904 590.229 620.079 610.250 580.185 630.320 630.510 600.385 610.548 620.597 640.394 59
PointNet++permissive0.339 640.584 530.478 640.458 610.256 650.360 660.250 580.247 630.278 650.261 620.677 650.183 640.117 560.212 640.145 650.364 610.346 660.232 660.548 620.523 650.252 64
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 650.353 640.290 660.278 660.166 660.553 630.169 650.286 610.147 660.148 660.908 570.182 650.064 620.023 660.018 670.354 620.363 640.345 640.546 640.685 550.278 62
ScanNetpermissive0.306 660.203 660.366 650.501 570.311 620.524 640.211 640.002 670.342 610.189 650.786 640.145 660.102 600.245 590.152 640.318 640.348 650.300 650.460 650.437 660.182 66
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 670.000 670.041 670.172 670.030 670.062 670.001 670.035 660.004 670.051 670.143 670.019 670.003 660.041 650.050 660.003 670.054 670.018 670.005 670.264 670.082 67

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
HAIS0.699 11.000 10.849 20.820 30.675 10.808 70.279 30.757 50.465 60.517 30.596 10.559 30.600 71.000 10.654 50.767 40.676 20.994 130.560 3
SSTNet0.698 21.000 10.697 190.888 20.556 40.803 80.387 20.626 90.417 90.556 20.585 30.702 10.600 71.000 10.824 10.720 110.692 11.000 10.509 5
OccuSeg+instance0.672 31.000 10.758 140.682 100.576 30.842 10.477 10.504 190.524 30.567 10.585 40.451 70.557 131.000 10.751 20.797 30.563 121.000 10.467 10
Mask-Group0.664 41.000 10.822 50.764 80.616 20.815 40.139 70.694 80.597 20.459 60.566 50.599 20.600 70.516 230.715 30.819 20.635 61.000 10.603 1
CSC-Pretrained0.648 51.000 10.810 60.768 60.523 90.813 50.143 60.819 10.389 100.422 130.511 100.443 80.650 21.000 10.624 60.732 100.634 71.000 10.375 16
PE0.645 61.000 10.773 100.798 50.538 60.786 130.088 140.799 30.350 140.435 120.547 70.545 40.646 60.933 100.562 90.761 70.556 160.997 100.501 7
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 71.000 10.758 130.582 160.539 50.826 30.046 170.765 40.372 120.436 110.588 20.539 50.650 21.000 10.577 70.750 90.653 50.997 100.495 8
Dyco3Dcopyleft0.641 81.000 10.841 30.893 10.531 70.802 90.115 110.588 140.448 70.438 90.537 80.430 100.550 140.857 120.534 100.764 60.657 30.987 140.568 2
GICN0.638 91.000 10.895 10.800 40.480 120.676 150.144 50.737 60.354 130.447 70.400 180.365 130.700 11.000 10.569 80.836 10.599 91.000 10.473 9
PointGroup0.636 101.000 10.765 110.624 110.505 110.797 100.116 100.696 70.384 110.441 80.559 60.476 60.596 101.000 10.666 40.756 80.556 150.997 100.513 4
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]
3D-MPA0.611 111.000 10.833 40.765 70.526 80.756 140.136 90.588 140.470 50.438 100.432 160.358 140.650 20.857 120.429 140.765 50.557 141.000 10.430 13
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
SphereNet0.606 121.000 10.776 90.745 90.436 160.834 20.035 190.587 160.518 40.338 200.534 90.352 150.594 121.000 10.391 180.696 140.624 81.000 10.451 11
PCJC0.578 131.000 10.810 70.583 150.449 150.813 60.042 180.603 120.341 150.490 40.465 120.410 110.650 20.835 170.264 220.694 150.561 130.889 200.504 6
SSEN0.575 141.000 10.761 120.473 180.477 130.795 110.066 150.529 170.658 10.460 50.461 130.380 120.331 220.859 110.401 170.692 160.653 41.000 10.348 18
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
MTML0.549 151.000 10.807 80.588 140.327 190.647 160.004 240.815 20.180 190.418 140.364 200.182 180.445 171.000 10.442 130.688 170.571 111.000 10.396 14
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Sparse R-CNN0.515 161.000 10.538 250.282 210.468 140.790 120.173 40.345 210.429 80.413 160.484 110.176 190.595 110.591 210.522 110.668 180.476 190.986 150.327 19
Occipital-SCS0.512 171.000 10.716 160.509 170.506 100.611 180.092 130.602 130.177 200.346 180.383 190.165 200.442 180.850 160.386 190.618 200.543 170.889 200.389 15
3D-BoNet0.488 181.000 10.672 200.590 130.301 200.484 260.098 120.620 100.306 160.341 190.259 220.125 220.434 190.796 180.402 160.499 250.513 180.909 190.439 12
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-inst0.478 190.667 210.712 180.595 120.259 220.550 230.000 270.613 110.175 210.250 240.434 140.437 90.411 210.857 120.485 120.591 230.267 270.944 170.359 17
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SALoss-ResNet0.459 201.000 10.737 150.159 280.259 210.587 200.138 80.475 200.217 180.416 150.408 170.128 210.315 230.714 190.411 150.536 240.590 100.873 230.304 20
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020)
MASCpermissive0.447 210.528 260.555 230.381 190.382 170.633 170.002 250.509 180.260 170.361 170.432 150.327 160.451 160.571 220.367 200.639 190.386 200.980 160.276 21
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
3D-SISpermissive0.382 221.000 10.432 270.245 230.190 230.577 210.013 220.263 230.033 270.320 210.240 230.075 240.422 200.857 120.117 250.699 120.271 260.883 220.235 23
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 230.667 210.542 240.264 220.157 260.550 220.000 270.205 260.009 280.270 230.218 240.075 240.500 150.688 200.007 300.698 130.301 240.459 280.200 24
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 240.667 210.715 170.233 240.189 240.479 270.008 230.218 240.067 260.201 250.173 250.107 230.123 270.438 240.150 230.615 210.355 210.916 180.093 29
R-PointNet0.306 250.500 270.405 280.311 200.348 180.589 190.054 160.068 280.126 220.283 220.290 210.028 280.219 250.214 260.331 210.396 280.275 250.821 250.245 22
RandSA0.250 260.333 280.613 210.229 250.163 250.493 240.000 270.304 220.107 230.147 270.100 260.052 270.231 240.119 270.039 270.445 270.325 220.654 260.141 26
3D-BEVIS0.248 270.667 210.566 220.076 290.035 300.394 280.027 210.035 290.098 240.099 280.030 290.025 290.098 280.375 250.126 240.604 220.181 280.854 240.171 25
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Region0.248 270.667 210.437 260.188 260.153 270.491 250.000 270.208 250.094 250.153 260.099 270.057 260.217 260.119 270.039 270.466 260.302 230.640 270.140 27
Sgpn_scannet0.143 290.208 300.390 290.169 270.065 280.275 290.029 200.069 270.000 290.087 290.043 280.014 300.027 300.000 290.112 260.351 290.168 290.438 290.138 28
MaskRCNN 2d->3d Proj0.058 300.333 280.002 300.000 300.053 290.002 300.002 260.021 300.000 290.045 300.024 300.238 170.065 290.000 290.014 290.107 300.020 300.110 300.006 30

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
Virtual MVFusion (R)0.745 10.861 10.839 10.881 10.672 10.512 10.422 100.898 10.723 10.714 10.954 20.454 10.509 10.773 10.895 10.756 10.820 10.653 10.935 10.891 10.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D0.670 20.822 30.795 30.836 20.659 20.481 20.451 60.769 20.656 30.567 30.931 30.395 30.390 40.700 20.534 30.689 50.770 20.574 30.865 30.831 30.675 2
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021
CU-Hybrid-2D Net0.636 30.825 20.820 20.179 160.648 30.463 30.549 10.742 30.676 20.628 20.961 10.420 20.379 50.684 30.381 100.732 20.723 30.599 20.827 80.851 20.634 3
DMMF_3d0.605 40.651 60.744 70.782 30.637 40.387 40.536 20.732 40.590 50.540 40.856 130.359 60.306 100.596 60.539 20.627 120.706 40.497 60.785 120.757 110.476 13
DMMF0.597 50.543 110.755 60.749 40.585 60.338 60.494 40.704 60.598 40.494 100.911 60.347 80.327 90.593 70.527 40.675 70.646 80.513 40.842 60.774 80.527 11
MCA-Net0.595 60.533 120.756 50.746 50.590 50.334 80.506 30.670 70.587 60.500 80.905 80.366 50.352 60.601 50.506 60.669 100.648 60.501 50.839 70.769 90.516 12
RFBNet0.592 70.616 70.758 40.659 60.581 70.330 90.469 50.655 100.543 90.524 50.924 40.355 70.336 80.572 80.479 80.671 80.648 60.480 70.814 100.814 40.614 6
DCRedNet0.583 80.682 50.723 80.542 100.510 110.310 110.451 60.668 80.549 80.520 60.920 50.375 40.446 20.528 110.417 90.670 90.577 130.478 80.862 40.806 50.628 5
SSMAcopyleft0.577 90.695 40.716 100.439 120.563 80.314 100.444 80.719 50.551 70.503 70.887 100.346 90.348 70.603 40.353 120.709 30.600 110.457 100.901 20.786 60.599 7
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
SN_RN152pyrx8_RVCcopyleft0.546 100.572 90.663 130.638 80.518 90.298 120.366 150.633 120.510 110.446 120.864 110.296 110.267 120.542 100.346 130.704 40.575 140.431 120.853 50.766 100.630 4
FuseNetpermissive0.535 110.570 100.681 120.182 150.512 100.290 130.431 90.659 90.504 120.495 90.903 90.308 100.428 30.523 120.365 110.676 60.621 100.470 90.762 130.779 70.541 9
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 120.613 80.722 90.418 130.358 170.337 70.370 140.479 150.443 130.368 150.907 70.207 140.213 160.464 150.525 50.618 130.657 50.450 110.788 110.721 140.408 16
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 130.481 150.612 140.579 90.456 130.343 50.384 120.623 130.525 100.381 140.845 140.254 130.264 140.557 90.182 150.581 150.598 120.429 130.760 140.661 160.446 15
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 140.505 130.709 110.092 170.427 140.241 140.411 110.654 110.385 170.457 110.861 120.053 170.279 110.503 130.481 70.645 110.626 90.365 150.748 150.725 130.529 10
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 150.490 140.581 150.289 140.507 120.067 170.379 130.610 140.417 150.435 130.822 160.278 120.267 120.503 130.228 140.616 140.533 150.375 140.820 90.729 120.560 8
Enet (reimpl)0.376 160.264 170.452 170.452 110.365 150.181 150.143 170.456 160.409 160.346 160.769 170.164 150.218 150.359 160.123 170.403 170.381 170.313 170.571 160.685 150.472 14
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)permissive0.330 170.293 160.521 160.657 70.361 160.161 160.250 160.004 170.440 140.183 170.836 150.125 160.060 170.319 170.132 160.417 160.412 160.344 160.541 170.427 170.109 17
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
UniDet_RVC0.205 10.381 10.323 10.037 10.226 10.177 10.063 10.277 10.120 10.067 10.131 10.074 20.317 10.080 10.235 10.289 10.141 10.678 10.080 1
MaskRCNN_ScanNetpermissive0.119 20.129 20.212 20.002 20.112 20.148 20.014 20.205 20.044 20.066 20.078 20.095 10.142 20.030 20.128 20.139 20.080 20.459 20.057 2
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-taskpermissive0.700 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. IROS 2020
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