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 bysorted bysort bysort bysort bysort bysort bysort bysort by
OccuSeg+Semantic0.764 10.758 310.796 70.839 40.746 20.907 10.562 10.850 50.680 20.672 10.978 10.610 10.335 30.777 10.819 160.847 10.830 10.691 30.972 10.885 10.727 2
BPNetcopyleft0.749 20.909 10.818 40.811 80.752 10.839 60.485 140.842 70.673 30.644 40.957 30.528 80.305 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 (Oral)
VMNetpermissive0.746 30.870 60.838 10.858 10.729 40.850 40.501 80.874 20.587 210.658 30.956 40.564 40.299 100.765 30.900 10.716 80.812 40.631 110.939 20.858 40.709 5
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
MatchingNet0.724 70.812 180.812 60.810 90.735 30.834 80.495 110.860 30.572 270.602 120.954 60.512 100.280 160.757 40.845 110.725 60.780 110.606 200.937 30.851 70.700 7
Virtual MVFusion0.746 30.771 260.819 30.848 20.702 80.865 30.397 450.899 10.699 10.664 20.948 200.588 20.330 40.746 50.851 80.764 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 80.818 40.832 50.709 70.840 50.521 50.853 40.660 40.643 50.951 110.544 50.286 150.731 60.893 20.675 190.772 140.683 40.874 310.852 60.727 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
SparseConvNet0.725 60.647 500.821 20.846 30.721 50.869 20.533 30.754 230.603 180.614 60.955 50.572 30.325 50.710 70.870 30.724 70.823 20.628 120.934 50.865 30.683 8
VACNN++0.684 150.728 390.757 190.776 180.690 90.804 290.464 220.816 80.577 250.587 160.945 290.508 110.276 180.671 80.710 300.663 240.750 220.589 260.881 260.832 110.653 13
SAFNet-segpermissive0.654 260.752 330.734 290.664 460.583 340.815 210.399 440.754 230.639 70.535 260.942 360.470 200.309 80.665 90.539 440.650 270.708 330.635 90.857 410.793 320.642 16
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
MVPNetpermissive0.641 280.831 130.715 340.671 430.590 310.781 410.394 460.679 430.642 60.553 210.937 430.462 240.256 240.649 100.406 560.626 360.691 400.666 50.877 270.792 340.608 27
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 280.776 240.703 370.721 300.557 430.826 140.451 240.672 450.563 330.483 410.943 350.425 410.162 540.644 110.726 270.659 260.709 320.572 290.875 290.786 380.559 46
Supervoxel-CNN0.635 340.656 480.711 350.719 310.613 260.757 480.444 320.765 200.534 380.566 180.928 510.478 170.272 190.636 120.531 460.664 230.645 500.508 490.864 380.792 340.611 24
PointASNLpermissive0.666 220.703 440.781 110.751 280.655 150.830 100.471 170.769 190.474 500.537 250.951 110.475 180.279 170.635 130.698 340.675 190.751 210.553 390.816 490.806 230.703 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
APCF-Net0.631 380.742 340.687 490.672 410.557 430.792 370.408 400.665 460.545 360.508 340.952 100.428 380.186 460.634 140.702 320.620 370.706 340.555 380.873 330.798 280.581 37
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionNet0.688 140.704 430.741 260.754 260.656 140.829 110.501 80.741 280.609 150.548 220.950 150.522 90.371 10.633 150.756 220.715 90.771 150.623 130.861 390.814 200.658 12
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
PointSPNet0.637 320.734 360.692 440.714 320.576 360.797 350.446 280.743 270.598 200.437 500.942 360.403 450.150 580.626 160.800 190.649 280.697 380.557 370.846 430.777 420.563 44
CU-Hybrid Net0.693 120.596 560.789 90.803 110.677 100.800 310.469 180.846 60.554 350.591 150.948 200.500 120.316 70.609 170.847 100.732 40.808 50.593 250.894 160.839 90.652 14
ROSMRF3D0.673 200.789 190.748 220.763 240.635 220.814 220.407 420.747 250.581 240.573 170.950 150.484 150.271 200.607 180.754 230.649 280.774 130.596 230.883 230.823 150.606 28
3DSM_DMMF0.631 380.626 520.745 230.801 120.607 270.751 490.506 60.729 320.565 310.491 380.866 640.434 340.197 430.595 190.630 390.709 110.705 350.560 340.875 290.740 530.491 56
RFCR0.702 80.889 30.745 230.813 70.672 120.818 190.493 120.815 100.623 100.610 80.947 240.470 200.249 280.594 200.848 90.705 130.779 120.646 70.892 180.823 150.611 24
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
PointMTL0.632 370.731 370.688 470.675 400.591 300.784 400.444 320.565 560.610 140.492 370.949 170.456 270.254 250.587 210.706 310.599 420.665 470.612 190.868 370.791 370.579 38
KP-FCNN0.684 150.847 110.758 180.784 160.647 180.814 220.473 160.772 180.605 160.594 140.935 460.450 310.181 480.587 210.805 180.690 150.785 90.614 150.882 240.819 190.632 19
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
PointConvpermissive0.666 220.781 210.759 170.699 340.644 200.822 170.475 150.779 160.564 320.504 360.953 80.428 380.203 410.586 230.754 230.661 250.753 200.588 270.902 100.813 220.642 16
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
MCCNNpermissive0.633 360.866 70.731 300.771 190.576 360.809 260.410 390.684 410.497 450.491 380.949 170.466 220.105 640.581 240.646 380.620 370.680 430.542 440.817 480.795 290.618 23
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
PointMRNet0.640 300.717 420.701 390.692 360.576 360.801 300.467 210.716 340.563 330.459 460.953 80.429 370.169 510.581 240.854 50.605 400.710 310.550 400.894 160.793 320.575 39
JSENetpermissive0.699 100.881 50.762 150.821 60.667 130.800 310.522 40.792 150.613 110.607 100.935 460.492 140.205 390.576 260.853 60.691 140.758 190.652 60.872 340.828 120.649 15
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
HPGCNN0.656 250.698 450.743 250.650 480.564 400.820 180.505 70.758 210.631 90.479 420.945 290.480 160.226 320.572 270.774 210.690 150.735 260.614 150.853 420.776 430.597 33
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SALANet0.670 210.816 170.770 140.768 210.652 170.807 270.451 240.747 250.659 50.545 230.924 530.473 190.149 590.571 280.811 170.635 350.746 230.623 130.892 180.794 310.570 41
joint point-basedpermissive0.634 350.614 530.778 120.667 450.633 230.825 150.420 370.804 120.467 520.561 190.951 110.494 130.291 120.566 290.458 500.579 480.764 170.559 360.838 440.814 200.598 32
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
Feature_GeometricNetpermissive0.690 130.884 40.754 200.795 140.647 180.818 190.422 360.802 140.612 120.604 110.945 290.462 240.189 450.563 300.853 60.726 50.765 160.632 100.904 80.821 180.606 28
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
One Thing One Click0.701 90.825 160.796 70.723 290.716 60.832 90.433 350.816 80.634 80.609 90.969 20.418 430.344 20.559 310.833 120.715 90.808 50.560 340.902 100.847 80.680 9
Feature-Geometry Netpermissive0.694 110.894 20.741 260.768 210.677 100.827 130.491 130.811 110.612 120.612 70.948 200.464 230.250 260.554 320.828 130.708 120.781 100.614 150.884 220.822 170.593 35
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
PointNet2-SFPN0.631 380.771 260.692 440.672 410.524 460.837 70.440 340.706 380.538 370.446 480.944 330.421 420.219 350.552 330.751 250.591 450.737 250.543 430.901 120.768 460.557 47
PointContrast_LA_SEM0.683 170.757 320.784 100.786 150.639 210.824 160.408 400.775 170.604 170.541 240.934 500.532 60.269 210.552 330.777 200.645 320.793 80.640 80.913 70.824 140.671 10
FusionAwareConv0.630 410.604 550.741 260.766 230.590 310.747 500.501 80.734 300.503 440.527 280.919 570.454 280.323 60.550 350.420 550.678 180.688 410.544 420.896 140.795 290.627 21
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
FPConvpermissive0.639 310.785 200.760 160.713 330.603 280.798 340.392 470.534 580.603 180.524 300.948 200.457 260.250 260.538 360.723 280.598 430.696 390.614 150.872 340.799 260.567 43
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
3DMV0.484 590.484 650.538 660.643 510.424 570.606 660.310 570.574 550.433 590.378 570.796 660.301 590.214 370.537 370.208 660.472 600.507 670.413 640.693 600.602 660.539 48
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
VI-PointConv0.676 190.770 280.754 200.783 170.621 240.814 220.552 20.758 210.571 290.557 200.954 60.529 70.268 230.530 380.682 350.675 190.719 280.603 210.888 200.833 100.665 11
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
Online SegFusion0.515 560.607 540.644 590.579 570.434 550.630 630.353 540.628 510.440 560.410 530.762 680.307 580.167 520.520 390.403 570.516 540.565 580.447 600.678 610.701 570.514 53
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
Superpoint Network0.683 170.851 100.728 330.800 130.653 160.806 280.468 190.804 120.572 270.602 120.946 260.453 300.239 310.519 400.822 140.689 170.762 180.595 240.895 150.827 130.630 20
CCRFNet0.589 480.766 290.659 540.683 380.470 530.740 520.387 510.620 520.490 470.476 430.922 550.355 550.245 290.511 410.511 470.571 490.643 510.493 520.872 340.762 480.600 31
SPH3D-GCNpermissive0.610 440.858 90.772 130.489 630.532 450.792 370.404 430.643 500.570 300.507 350.935 460.414 440.046 690.510 420.702 320.602 410.705 350.549 410.859 400.773 440.534 49
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
DCM-Net0.658 240.778 220.702 380.806 100.619 250.813 250.468 190.693 400.494 460.524 300.941 380.449 320.298 110.510 420.821 150.675 190.727 270.568 310.826 460.803 250.637 18
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
SConv0.636 330.830 140.697 420.752 270.572 390.780 420.445 300.716 340.529 390.530 270.951 110.446 330.170 500.507 440.666 370.636 340.682 420.541 450.886 210.799 260.594 34
SegGCNpermissive0.589 480.833 120.731 300.539 600.514 480.789 390.448 260.467 600.573 260.484 400.936 440.396 470.061 680.501 450.507 490.594 440.700 370.563 330.874 310.771 450.493 55
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020
PanopticFusion-label0.529 550.491 640.688 470.604 540.386 590.632 620.225 670.705 390.434 580.293 630.815 650.348 560.241 300.499 460.669 360.507 550.649 480.442 610.796 510.602 660.561 45
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SIConv0.625 420.830 140.694 430.757 250.563 410.772 440.448 260.647 490.520 410.509 330.949 170.431 360.191 440.496 470.614 400.647 310.672 450.535 470.876 280.783 390.571 40
HPEIN0.618 430.729 380.668 500.647 500.597 290.766 450.414 380.680 420.520 410.525 290.946 260.432 350.215 360.493 480.599 410.638 330.617 550.570 300.897 130.806 230.605 30
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
RandLA-Netpermissive0.645 270.778 220.731 300.699 340.577 350.829 110.446 280.736 290.477 490.523 320.945 290.454 280.269 210.484 490.749 260.618 390.738 240.599 220.827 450.792 340.621 22
AttAN0.609 450.760 300.667 510.649 490.521 470.793 360.457 230.648 480.528 400.434 520.947 240.401 460.153 570.454 500.721 290.648 300.717 290.536 460.904 80.765 470.485 57
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
3DMV, FTSDF0.501 570.558 600.608 630.424 680.478 520.690 560.246 630.586 540.468 510.450 470.911 590.394 480.160 550.438 510.212 650.432 610.541 630.475 550.742 560.727 540.477 58
ROSMRF0.580 500.772 250.707 360.681 390.563 410.764 460.362 530.515 590.465 530.465 450.936 440.427 400.207 380.438 510.577 420.536 530.675 440.486 530.723 580.779 400.524 51
LAP-D0.594 460.720 400.692 440.637 520.456 540.773 430.391 490.730 310.587 210.445 490.940 400.381 490.288 130.434 530.453 510.591 450.649 480.581 280.777 530.749 520.610 26
DPC0.592 470.720 400.700 400.602 550.480 510.762 470.380 520.713 360.585 230.437 500.940 400.369 510.288 130.434 530.509 480.590 470.639 530.567 320.772 540.755 500.592 36
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
TextureNetpermissive0.566 510.672 470.664 520.671 430.494 490.719 530.445 300.678 440.411 600.396 550.935 460.356 540.225 330.412 550.535 450.565 500.636 540.464 560.794 520.680 600.568 42
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
Pointnet++ & Featurepermissive0.557 530.735 350.661 530.686 370.491 500.744 510.392 470.539 570.451 550.375 580.946 260.376 500.205 390.403 560.356 590.553 520.643 510.497 510.824 470.756 490.515 52
PointMRNet-lite0.553 540.633 510.648 560.659 470.430 560.800 310.390 500.592 530.454 540.371 590.939 420.368 520.136 610.368 570.448 520.560 510.715 300.486 530.882 240.720 550.462 60
SurfaceConvPF0.442 620.505 630.622 610.380 690.342 640.654 600.227 660.397 630.367 640.276 650.924 530.240 660.198 420.359 580.262 620.366 640.581 570.435 620.640 630.668 610.398 62
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
3DWSSS0.425 650.525 620.647 570.522 610.324 650.488 700.077 710.712 370.353 650.401 540.636 700.281 620.176 490.340 590.565 430.175 710.551 610.398 650.370 710.602 660.361 64
DVVNet0.562 520.648 490.700 400.770 200.586 330.687 570.333 560.650 470.514 430.475 440.906 610.359 530.223 340.340 590.442 540.422 620.668 460.501 500.708 590.779 400.534 49
subcloud_weak0.411 660.479 660.650 550.475 640.285 690.519 690.087 700.725 330.396 620.386 560.621 710.250 650.117 620.338 610.443 530.188 700.594 560.369 680.377 700.616 650.306 66
Tangent Convolutionspermissive0.438 640.437 680.646 580.474 650.369 610.645 610.353 540.258 670.282 690.279 640.918 580.298 600.147 600.283 620.294 610.487 570.562 590.427 630.619 640.633 630.352 65
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
ScanNet+FTSDF0.383 680.297 700.491 680.432 670.358 630.612 650.274 610.116 690.411 600.265 660.904 620.229 670.079 660.250 630.185 680.320 670.510 650.385 660.548 660.597 690.394 63
ScanNetpermissive0.306 710.203 710.366 700.501 620.311 670.524 680.211 680.002 720.342 660.189 700.786 670.145 710.102 650.245 640.152 690.318 680.348 700.300 700.460 690.437 710.182 71
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
PCNN0.498 580.559 590.644 590.560 590.420 580.711 550.229 650.414 610.436 570.352 600.941 380.324 570.155 560.238 650.387 580.493 560.529 640.509 480.813 500.751 510.504 54
PNET20.442 620.548 610.548 650.597 560.363 620.628 640.300 580.292 650.374 630.307 620.881 630.268 630.186 460.238 650.204 670.407 630.506 680.449 590.667 620.620 640.462 60
FCPNpermissive0.447 610.679 460.604 640.578 580.380 600.682 580.291 600.106 700.483 480.258 680.920 560.258 640.025 700.231 670.325 600.480 590.560 600.463 570.725 570.666 620.231 70
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
PointCNN with RGBpermissive0.458 600.577 580.611 620.356 700.321 660.715 540.299 590.376 640.328 670.319 610.944 330.285 610.164 530.216 680.229 640.484 580.545 620.456 580.755 550.709 560.475 59
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PointNet++permissive0.339 690.584 570.478 690.458 660.256 700.360 710.250 620.247 680.278 700.261 670.677 690.183 690.117 620.212 690.145 700.364 650.346 710.232 710.548 660.523 700.252 69
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ERROR0.054 720.000 720.041 720.172 720.030 720.062 720.001 720.035 710.004 720.051 720.143 720.019 720.003 710.041 700.050 710.003 720.054 720.018 720.005 720.264 720.082 72
SSC-UNetpermissive0.308 700.353 690.290 710.278 710.166 710.553 670.169 690.286 660.147 710.148 710.908 600.182 700.064 670.023 710.018 720.354 660.363 690.345 690.546 680.685 590.278 67
SPLAT Netcopyleft0.393 670.472 670.511 670.606 530.311 670.656 590.245 640.405 620.328 670.197 690.927 520.227 680.000 720.001 720.249 630.271 690.510 650.383 670.593 650.699 580.267 68
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

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 bysorted bysort bysort bysort bysort bysort bysort by
GICN0.638 91.000 10.895 10.800 40.480 130.676 160.144 60.737 70.354 140.447 80.400 190.365 140.700 11.000 10.569 90.836 10.599 101.000 10.473 10
CSC-Pretrained0.648 51.000 10.810 60.768 60.523 100.813 50.143 70.819 10.389 110.422 140.511 110.443 80.650 21.000 10.624 70.732 100.634 71.000 10.375 17
3D-MPA0.611 121.000 10.833 40.765 70.526 90.756 150.136 100.588 150.470 50.438 110.432 170.358 150.650 20.857 130.429 150.765 50.557 151.000 10.430 14
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
PCJC0.578 141.000 10.810 70.583 160.449 160.813 60.042 190.603 130.341 160.490 40.465 130.410 110.650 20.835 180.264 230.694 160.561 140.889 210.504 7
RPGN0.643 71.000 10.758 140.582 170.539 60.826 30.046 180.765 50.372 130.436 120.588 20.539 50.650 21.000 10.577 80.750 90.653 50.997 110.495 9
PE0.645 61.000 10.773 110.798 50.538 70.786 130.088 150.799 40.350 150.435 130.547 70.545 40.646 60.933 110.562 100.761 70.556 170.997 110.501 8
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
DD-UNet+Group0.635 110.667 210.797 90.714 100.562 40.774 140.146 50.810 30.429 90.476 50.546 80.399 120.633 71.000 10.632 60.722 110.609 91.000 10.514 4
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor.
Mask-Group0.664 41.000 10.822 50.764 80.616 20.815 40.139 80.694 90.597 20.459 70.566 50.599 20.600 80.516 240.715 30.819 20.635 61.000 10.603 1
HAISpermissive0.699 11.000 10.849 20.820 30.675 10.808 70.279 30.757 60.465 60.517 30.596 10.559 30.600 81.000 10.654 50.767 40.676 20.994 140.560 3
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNet0.698 21.000 10.697 200.888 20.556 50.803 80.387 20.626 100.417 100.556 20.585 30.702 10.600 81.000 10.824 10.720 120.692 11.000 10.509 6
PointGroup0.636 101.000 10.765 120.624 120.505 120.797 100.116 110.696 80.384 120.441 90.559 60.476 60.596 111.000 10.666 40.756 80.556 160.997 110.513 5
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]
Sparse R-CNN0.515 171.000 10.538 260.282 220.468 150.790 120.173 40.345 220.429 80.413 170.484 120.176 200.595 120.591 220.522 120.668 190.476 200.986 160.327 20
SphereNet0.606 131.000 10.776 100.745 90.436 170.834 20.035 200.587 170.518 40.338 210.534 100.352 160.594 131.000 10.391 190.696 150.624 81.000 10.451 12
OccuSeg+instance0.672 31.000 10.758 150.682 110.576 30.842 10.477 10.504 200.524 30.567 10.585 40.451 70.557 141.000 10.751 20.797 30.563 131.000 10.467 11
Dyco3Dcopyleft0.641 81.000 10.841 30.893 10.531 80.802 90.115 120.588 150.448 70.438 100.537 90.430 100.550 150.857 130.534 110.764 60.657 30.987 150.568 2
Hier3Dcopyleft0.323 240.667 210.542 250.264 230.157 270.550 230.000 280.205 270.009 290.270 240.218 250.075 250.500 160.688 210.007 310.698 140.301 250.459 290.200 25
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
MASCpermissive0.447 220.528 270.555 240.381 200.382 180.633 180.002 260.509 190.260 180.361 180.432 160.327 170.451 170.571 230.367 210.639 200.386 210.980 170.276 22
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
MTML0.549 161.000 10.807 80.588 150.327 200.647 170.004 250.815 20.180 200.418 150.364 210.182 190.445 181.000 10.442 140.688 180.571 121.000 10.396 15
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Occipital-SCS0.512 181.000 10.716 170.509 180.506 110.611 190.092 140.602 140.177 210.346 190.383 200.165 210.442 190.850 170.386 200.618 210.543 180.889 210.389 16
3D-BoNet0.488 191.000 10.672 210.590 140.301 210.484 270.098 130.620 110.306 170.341 200.259 230.125 230.434 200.796 190.402 170.499 260.513 190.909 200.439 13
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-SISpermissive0.382 231.000 10.432 280.245 240.190 240.577 220.013 230.263 240.033 280.320 220.240 240.075 250.422 210.857 130.117 260.699 130.271 270.883 230.235 24
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
PanopticFusion-inst0.478 200.667 210.712 190.595 130.259 230.550 240.000 280.613 120.175 220.250 250.434 150.437 90.411 220.857 130.485 130.591 240.267 280.944 180.359 18
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SSEN0.575 151.000 10.761 130.473 190.477 140.795 110.066 160.529 180.658 10.460 60.461 140.380 130.331 230.859 120.401 180.692 170.653 41.000 10.348 19
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
SALoss-ResNet0.459 211.000 10.737 160.159 290.259 220.587 210.138 90.475 210.217 190.416 160.408 180.128 220.315 240.714 200.411 160.536 250.590 110.873 240.304 21
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)
RandSA0.250 270.333 290.613 220.229 260.163 260.493 250.000 280.304 230.107 240.147 280.100 270.052 280.231 250.119 280.039 280.445 280.325 230.654 270.141 27
R-PointNet0.306 260.500 280.405 290.311 210.348 190.589 200.054 170.068 290.126 230.283 230.290 220.028 290.219 260.214 270.331 220.396 290.275 260.821 260.245 23
Region0.248 280.667 210.437 270.188 270.153 280.491 260.000 280.208 260.094 260.153 270.099 280.057 270.217 270.119 280.039 280.466 270.302 240.640 280.140 28
UNet-backbone0.319 250.667 210.715 180.233 250.189 250.479 280.008 240.218 250.067 270.201 260.173 260.107 240.123 280.438 250.150 240.615 220.355 220.916 190.093 30
3D-BEVIS0.248 280.667 210.566 230.076 300.035 310.394 290.027 220.035 300.098 250.099 290.030 300.025 300.098 290.375 260.126 250.604 230.181 290.854 250.171 26
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
MaskRCNN 2d->3d Proj0.058 310.333 290.002 310.000 310.053 300.002 310.002 270.021 310.000 300.045 310.024 310.238 180.065 300.000 300.014 300.107 310.020 310.110 310.006 31
Sgpn_scannet0.143 300.208 310.390 300.169 280.065 290.275 300.029 210.069 280.000 300.087 300.043 290.014 310.027 310.000 300.112 270.351 300.168 300.438 300.138 29

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 bysorted 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_2Dcopyleft0.670 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 (Oral)
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
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
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
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
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
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
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
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
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
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
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
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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 21.000 11.000 10.500 21.000 11.000 10.778 10.000 20.938 10.000 2
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D. IROS 2020
3DASPP-SCE0.691 20.500 10.938 20.824 31.000 11.000 10.500 21.000 10.857 20.500 20.556 30.000 20.812 20.500 1
SE-ResNeXt-SSMA0.498 30.000 40.812 30.941 10.500 20.500 30.500 20.500 20.429 40.500 20.667 20.500 10.625 30.000 2
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet0.353 40.250 30.812 30.529 40.500 20.500 30.000 40.500 20.571 30.000 40.556 30.000 20.375 40.000 2