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
Mix3Dpermissive0.781 10.964 10.855 10.843 80.781 10.858 60.575 20.831 110.685 40.714 10.979 10.594 30.310 130.801 10.892 40.841 20.819 30.723 20.940 40.887 10.725 8
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
OccuSeg+Semantic0.764 20.758 380.796 120.839 90.746 50.907 10.562 30.850 60.680 50.672 30.978 20.610 10.335 70.777 20.819 220.847 10.830 10.691 60.972 10.885 20.727 6
O-CNNpermissive0.762 30.924 20.823 40.844 70.770 20.852 70.577 10.847 70.711 10.640 100.958 60.592 40.217 450.762 50.888 50.758 50.813 50.726 10.932 100.868 50.744 2
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
BPNetcopyleft0.749 40.909 30.818 70.811 140.752 40.839 110.485 200.842 90.673 60.644 80.957 70.528 150.305 150.773 30.859 90.788 30.818 40.693 50.916 110.856 100.723 9
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
StratifiedFormerpermissive0.747 50.901 40.803 100.845 60.757 30.846 90.512 100.825 120.696 30.645 70.956 80.576 60.262 310.744 100.861 80.742 70.770 230.705 30.899 220.860 80.734 3
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 60.771 320.819 60.848 40.702 150.865 50.397 580.899 10.699 20.664 40.948 290.588 50.330 80.746 90.851 150.764 40.796 110.704 40.935 70.866 60.728 4
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 60.870 100.838 20.858 20.729 80.850 80.501 130.874 30.587 300.658 50.956 80.564 80.299 160.765 40.900 20.716 160.812 60.631 180.939 50.858 90.709 10
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)
EQ-Net0.743 80.620 670.799 110.849 30.730 70.822 220.493 180.897 20.664 70.681 20.955 110.562 90.378 10.760 60.903 10.738 80.801 90.673 80.907 140.877 30.745 1
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
MinkowskiNetpermissive0.736 90.859 120.818 70.832 100.709 120.840 100.521 90.853 50.660 90.643 90.951 190.544 100.286 220.731 110.893 30.675 280.772 210.683 70.874 420.852 120.727 6
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 100.890 50.837 30.864 10.726 90.873 20.530 70.824 130.489 610.647 60.978 20.609 20.336 60.624 250.733 340.758 50.776 190.570 410.949 20.877 30.728 4
SparseConvNet0.725 110.647 630.821 50.846 50.721 100.869 30.533 60.754 310.603 260.614 130.955 110.572 70.325 100.710 120.870 60.724 130.823 20.628 190.934 80.865 70.683 15
MatchingNet0.724 120.812 220.812 90.810 150.735 60.834 140.495 170.860 40.572 350.602 200.954 130.512 180.280 230.757 70.845 180.725 120.780 170.606 270.937 60.851 130.700 12
INS-Conv-semantic0.717 130.751 410.759 260.812 130.704 140.868 40.537 50.842 90.609 220.608 160.953 150.534 110.293 180.616 260.864 70.719 150.793 130.640 130.933 90.845 170.663 19
contrastBoundarypermissive0.705 140.769 350.775 200.809 160.687 170.820 250.439 430.812 180.661 80.591 230.945 380.515 170.171 630.633 220.856 100.720 140.796 110.668 90.889 300.847 150.689 14
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 150.889 60.745 350.813 120.672 200.818 280.493 180.815 160.623 160.610 140.947 320.470 290.249 360.594 300.848 160.705 200.779 180.646 120.892 280.823 240.611 35
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
One Thing One Click0.701 160.825 190.796 120.723 370.716 110.832 150.433 450.816 140.634 140.609 150.969 50.418 550.344 40.559 440.833 190.715 170.808 70.560 450.902 190.847 150.680 16
JSENetpermissive0.699 170.881 90.762 240.821 110.667 210.800 420.522 80.792 230.613 180.607 170.935 570.492 220.205 500.576 360.853 120.691 230.758 290.652 110.872 450.828 210.649 24
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
PicassoNet-IIpermissive0.696 180.704 530.790 150.787 230.709 120.837 120.459 300.815 160.543 450.615 120.956 80.529 130.250 340.551 470.790 260.703 210.799 100.619 220.908 130.848 140.700 12
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 190.743 420.794 140.655 610.684 180.822 220.497 160.719 410.622 170.617 110.977 40.447 430.339 50.750 80.664 490.703 210.790 150.596 300.946 30.855 110.647 25
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
CU-Hybrid Net0.693 190.596 710.789 160.803 180.677 190.800 420.469 240.846 80.554 430.591 230.948 290.500 200.316 120.609 270.847 170.732 90.808 70.593 330.894 260.839 180.652 22
Feature_GeometricNetpermissive0.690 210.884 70.754 300.795 210.647 260.818 280.422 470.802 210.612 190.604 180.945 380.462 320.189 570.563 420.853 120.726 100.765 240.632 160.904 160.821 260.606 39
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
Feature-Geometry Netpermissive0.690 210.884 70.754 300.795 210.647 260.818 280.422 470.802 210.612 190.604 180.945 380.462 320.189 570.563 420.853 120.726 100.765 240.632 160.904 160.821 260.606 39
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 230.704 530.741 390.754 340.656 220.829 170.501 130.741 360.609 220.548 310.950 230.522 160.371 20.633 220.756 290.715 170.771 220.623 200.861 510.814 290.658 20
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
KP-FCNN0.684 240.847 150.758 280.784 250.647 260.814 320.473 220.772 260.605 240.594 220.935 570.450 410.181 610.587 310.805 240.690 240.785 160.614 230.882 340.819 280.632 30
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 240.728 480.757 290.776 270.690 160.804 400.464 280.816 140.577 340.587 250.945 380.508 190.276 250.671 130.710 390.663 330.750 320.589 350.881 360.832 200.653 21
Superpoint Network0.683 260.851 140.728 440.800 200.653 240.806 380.468 250.804 190.572 350.602 200.946 350.453 390.239 390.519 530.822 200.689 260.762 270.595 320.895 250.827 220.630 31
PointContrast_LA_SEM0.683 260.757 390.784 170.786 240.639 300.824 210.408 520.775 250.604 250.541 330.934 610.532 120.269 280.552 450.777 270.645 440.793 130.640 130.913 120.824 230.671 17
VI-PointConv0.676 280.770 340.754 300.783 260.621 340.814 320.552 40.758 290.571 370.557 290.954 130.529 130.268 300.530 510.682 440.675 280.719 400.603 280.888 310.833 190.665 18
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 290.789 250.748 330.763 320.635 320.814 320.407 540.747 330.581 330.573 260.950 230.484 230.271 270.607 280.754 300.649 390.774 200.596 300.883 330.823 240.606 39
SALANet0.670 300.816 210.770 220.768 300.652 250.807 370.451 320.747 330.659 100.545 320.924 670.473 280.149 730.571 380.811 230.635 470.746 330.623 200.892 280.794 420.570 52
PointConvpermissive0.666 310.781 270.759 260.699 440.644 290.822 220.475 210.779 240.564 400.504 490.953 150.428 490.203 520.586 330.754 300.661 350.753 300.588 360.902 190.813 310.642 26
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 310.703 550.781 180.751 360.655 230.830 160.471 230.769 270.474 640.537 340.951 190.475 270.279 240.635 200.698 430.675 280.751 310.553 500.816 620.806 330.703 11
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
DCM-Net0.658 330.778 280.702 490.806 170.619 350.813 350.468 250.693 480.494 570.524 410.941 490.449 420.298 170.510 550.821 210.675 280.727 390.568 430.826 590.803 350.637 28
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 340.698 560.743 370.650 620.564 530.820 250.505 120.758 290.631 150.479 540.945 380.480 250.226 400.572 370.774 280.690 240.735 360.614 230.853 540.776 560.597 45
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 350.752 400.734 410.664 580.583 470.815 310.399 570.754 310.639 120.535 360.942 470.470 290.309 140.665 140.539 570.650 380.708 460.635 150.857 530.793 440.642 26
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 360.778 280.731 420.699 440.577 480.829 170.446 350.736 370.477 630.523 430.945 380.454 370.269 280.484 620.749 330.618 510.738 340.599 290.827 580.792 470.621 33
PointConv-SFPN0.641 370.776 300.703 480.721 380.557 560.826 190.451 320.672 530.563 410.483 530.943 460.425 520.162 680.644 180.726 350.659 360.709 450.572 400.875 400.786 510.559 57
MVPNetpermissive0.641 370.831 160.715 450.671 550.590 430.781 520.394 590.679 510.642 110.553 300.937 550.462 320.256 320.649 170.406 710.626 480.691 530.666 100.877 380.792 470.608 38
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointMRNet0.640 390.717 520.701 500.692 470.576 490.801 410.467 270.716 420.563 410.459 580.953 150.429 480.169 650.581 340.854 110.605 530.710 430.550 510.894 260.793 440.575 50
FPConvpermissive0.639 400.785 260.760 250.713 420.603 380.798 450.392 600.534 730.603 260.524 410.948 290.457 350.250 340.538 490.723 370.598 570.696 510.614 230.872 450.799 360.567 54
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 410.797 240.769 230.641 670.590 430.820 250.461 290.537 720.637 130.536 350.947 320.388 620.206 490.656 150.668 470.647 420.732 380.585 370.868 480.793 440.473 74
PointSPNet0.637 420.734 450.692 580.714 410.576 490.797 460.446 350.743 350.598 280.437 630.942 470.403 580.150 720.626 240.800 250.649 390.697 500.557 480.846 550.777 550.563 55
SConv0.636 430.830 170.697 530.752 350.572 520.780 540.445 370.716 420.529 480.530 380.951 190.446 440.170 640.507 570.666 480.636 460.682 550.541 570.886 320.799 360.594 46
PPCNN++permissive0.636 430.724 490.697 530.672 520.636 310.775 560.403 560.582 670.588 290.533 370.949 250.453 390.218 440.571 380.676 450.663 330.635 700.580 390.906 150.808 320.650 23
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
Supervoxel-CNN0.635 450.656 610.711 460.719 390.613 360.757 640.444 400.765 280.534 470.566 270.928 650.478 260.272 260.636 190.531 590.664 320.645 650.508 640.864 500.792 470.611 35
joint point-basedpermissive0.634 460.614 680.778 190.667 570.633 330.825 200.420 490.804 190.467 660.561 280.951 190.494 210.291 190.566 400.458 650.579 620.764 260.559 470.838 560.814 290.598 44
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 470.866 110.731 420.771 280.576 490.809 360.410 510.684 490.497 560.491 510.949 250.466 310.105 770.581 340.646 510.620 490.680 560.542 560.817 610.795 400.618 34
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
PointMTL0.632 480.731 460.688 610.675 510.591 420.784 510.444 400.565 690.610 210.492 500.949 250.456 360.254 330.587 310.706 400.599 560.665 610.612 260.868 480.791 500.579 49
3DSM_DMMF0.631 490.626 660.745 350.801 190.607 370.751 650.506 110.729 400.565 390.491 510.866 800.434 450.197 550.595 290.630 520.709 190.705 480.560 450.875 400.740 660.491 69
APCF-Net0.631 490.742 430.687 630.672 520.557 560.792 490.408 520.665 540.545 440.508 460.952 180.428 490.186 590.634 210.702 410.620 490.706 470.555 490.873 430.798 380.581 48
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN0.631 490.771 320.692 580.672 520.524 600.837 120.440 420.706 460.538 460.446 600.944 440.421 540.219 430.552 450.751 320.591 590.737 350.543 550.901 210.768 580.557 58
FusionAwareConv0.630 520.604 700.741 390.766 310.590 430.747 660.501 130.734 380.503 550.527 390.919 710.454 370.323 110.550 480.420 700.678 270.688 540.544 530.896 240.795 400.627 32
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 530.800 230.625 740.719 390.545 580.806 380.445 370.597 620.448 700.519 440.938 540.481 240.328 90.489 610.499 640.657 370.759 280.592 340.881 360.797 390.634 29
SegGroup_sempermissive0.627 540.818 200.747 340.701 430.602 390.764 610.385 650.629 590.490 590.508 460.931 640.409 570.201 530.564 410.725 360.618 510.692 520.539 580.873 430.794 420.548 61
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation.
SIConv0.625 550.830 170.694 560.757 330.563 540.772 590.448 340.647 570.520 500.509 450.949 250.431 470.191 560.496 590.614 530.647 420.672 590.535 600.876 390.783 520.571 51
HPEIN0.618 560.729 470.668 640.647 640.597 410.766 600.414 500.680 500.520 500.525 400.946 350.432 460.215 460.493 600.599 540.638 450.617 710.570 410.897 230.806 330.605 42
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-GCNpermissive0.610 570.858 130.772 210.489 790.532 590.792 490.404 550.643 580.570 380.507 480.935 570.414 560.046 830.510 550.702 410.602 550.705 480.549 520.859 520.773 570.534 63
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 580.760 370.667 650.649 630.521 610.793 470.457 310.648 560.528 490.434 650.947 320.401 590.153 710.454 640.721 380.648 410.717 410.536 590.904 160.765 590.485 70
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 590.634 640.743 370.697 460.601 400.781 520.437 440.585 660.493 580.446 600.933 620.394 600.011 850.654 160.661 500.603 540.733 370.526 610.832 570.761 610.480 71
LAP-D0.594 600.720 500.692 580.637 680.456 690.773 580.391 620.730 390.587 300.445 620.940 510.381 630.288 200.434 670.453 670.591 590.649 630.581 380.777 660.749 650.610 37
DPC0.592 610.720 500.700 510.602 720.480 650.762 630.380 660.713 440.585 320.437 630.940 510.369 650.288 200.434 670.509 630.590 610.639 680.567 440.772 670.755 630.592 47
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 620.766 360.659 680.683 490.470 680.740 680.387 640.620 610.490 590.476 550.922 690.355 690.245 370.511 540.511 620.571 630.643 660.493 680.872 450.762 600.600 43
ROSMRF0.580 630.772 310.707 470.681 500.563 540.764 610.362 670.515 740.465 670.465 570.936 560.427 510.207 480.438 650.577 550.536 670.675 580.486 690.723 720.779 530.524 65
SQN_0.1%0.569 640.676 580.696 550.657 600.497 620.779 550.424 460.548 700.515 520.376 700.902 780.422 530.357 30.379 710.456 660.596 580.659 620.544 530.685 750.665 780.556 59
TextureNetpermissive0.566 650.672 600.664 660.671 550.494 630.719 690.445 370.678 520.411 760.396 680.935 570.356 680.225 410.412 690.535 580.565 640.636 690.464 720.794 650.680 750.568 53
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
DVVNet0.562 660.648 620.700 510.770 290.586 460.687 730.333 710.650 550.514 530.475 560.906 750.359 670.223 420.340 740.442 690.422 780.668 600.501 650.708 730.779 530.534 63
Pointnet++ & Featurepermissive0.557 670.735 440.661 670.686 480.491 640.744 670.392 600.539 710.451 690.375 710.946 350.376 640.205 500.403 700.356 740.553 660.643 660.497 660.824 600.756 620.515 66
PointMRNet-lite0.553 680.633 650.648 690.659 590.430 720.800 420.390 630.592 640.454 680.371 720.939 530.368 660.136 750.368 720.448 680.560 650.715 420.486 690.882 340.720 700.462 75
GMLPs0.538 690.495 790.693 570.647 640.471 670.793 470.300 730.477 750.505 540.358 730.903 770.327 720.081 800.472 630.529 600.448 760.710 430.509 620.746 690.737 670.554 60
PanopticFusion-label0.529 700.491 800.688 610.604 710.386 750.632 780.225 830.705 470.434 730.293 780.815 810.348 700.241 380.499 580.669 460.507 690.649 630.442 770.796 640.602 810.561 56
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 710.676 580.591 790.609 690.442 700.774 570.335 700.597 620.422 750.357 740.932 630.341 710.094 790.298 760.528 610.473 740.676 570.495 670.602 800.721 690.349 81
Online SegFusion0.515 720.607 690.644 720.579 740.434 710.630 790.353 680.628 600.440 710.410 660.762 840.307 740.167 660.520 520.403 720.516 680.565 730.447 760.678 760.701 720.514 67
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
3DMV, FTSDF0.501 730.558 750.608 770.424 830.478 660.690 720.246 790.586 650.468 650.450 590.911 730.394 600.160 690.438 650.212 800.432 770.541 780.475 710.742 700.727 680.477 72
PCNN0.498 740.559 740.644 720.560 760.420 740.711 710.229 810.414 760.436 720.352 750.941 490.324 730.155 700.238 800.387 730.493 700.529 790.509 620.813 630.751 640.504 68
3DMV0.484 750.484 810.538 810.643 660.424 730.606 820.310 720.574 680.433 740.378 690.796 820.301 750.214 470.537 500.208 810.472 750.507 820.413 800.693 740.602 810.539 62
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 760.577 730.611 760.356 850.321 820.715 700.299 750.376 790.328 820.319 760.944 440.285 770.164 670.216 830.229 790.484 720.545 770.456 740.755 680.709 710.475 73
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 770.679 570.604 780.578 750.380 760.682 740.291 760.106 850.483 620.258 830.920 700.258 800.025 840.231 820.325 750.480 730.560 750.463 730.725 710.666 770.231 85
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
SurfaceConvPF0.442 780.505 780.622 750.380 840.342 800.654 760.227 820.397 780.367 790.276 800.924 670.240 810.198 540.359 730.262 770.366 800.581 720.435 780.640 780.668 760.398 77
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 780.548 760.548 800.597 730.363 780.628 800.300 730.292 800.374 780.307 770.881 790.268 790.186 590.238 800.204 820.407 790.506 830.449 750.667 770.620 800.462 75
Tangent Convolutionspermissive0.438 800.437 830.646 710.474 800.369 770.645 770.353 680.258 820.282 840.279 790.918 720.298 760.147 740.283 770.294 760.487 710.562 740.427 790.619 790.633 790.352 80
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 810.525 770.647 700.522 770.324 810.488 850.077 860.712 450.353 800.401 670.636 860.281 780.176 620.340 740.565 560.175 860.551 760.398 810.370 860.602 810.361 79
SPLAT Netcopyleft0.393 820.472 820.511 820.606 700.311 830.656 750.245 800.405 770.328 820.197 840.927 660.227 830.000 870.001 870.249 780.271 850.510 800.383 830.593 810.699 730.267 83
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+FTSDF0.383 830.297 850.491 830.432 820.358 790.612 810.274 770.116 840.411 760.265 810.904 760.229 820.079 810.250 780.185 830.320 830.510 800.385 820.548 820.597 840.394 78
PointNet++permissive0.339 840.584 720.478 840.458 810.256 850.360 860.250 780.247 830.278 850.261 820.677 850.183 840.117 760.212 840.145 850.364 810.346 860.232 860.548 820.523 850.252 84
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 850.353 840.290 860.278 860.166 860.553 830.169 850.286 810.147 860.148 860.908 740.182 850.064 820.023 860.018 870.354 820.363 840.345 840.546 840.685 740.278 82
ScanNetpermissive0.306 860.203 860.366 850.501 780.311 830.524 840.211 840.002 870.342 810.189 850.786 830.145 860.102 780.245 790.152 840.318 840.348 850.300 850.460 850.437 860.182 86
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 870.000 870.041 870.172 870.030 870.062 870.001 870.035 860.004 870.051 870.143 870.019 870.003 860.041 850.050 860.003 870.054 870.018 870.005 870.264 870.082 87

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
SoftGroup++0.769 11.000 10.803 130.937 10.684 30.865 30.213 110.870 20.664 20.571 40.758 10.702 40.807 11.000 10.653 110.902 10.792 21.000 10.626 1
SoftGrouppermissive0.761 21.000 10.808 110.845 50.716 10.862 50.243 80.824 30.655 40.620 20.734 20.699 50.791 30.981 180.716 40.844 40.769 31.000 10.594 5
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral]
GraphCut0.732 31.000 10.788 160.724 150.642 50.859 60.248 70.787 80.618 60.596 30.653 30.722 20.583 211.000 10.766 20.861 20.825 11.000 10.504 13
IPCA-Inst0.731 41.000 10.788 170.884 40.698 20.788 190.252 60.760 100.646 50.511 100.637 50.665 60.804 21.000 10.644 120.778 90.747 41.000 10.561 8
DKNet0.718 51.000 10.814 80.782 90.619 60.872 20.224 90.751 120.569 80.677 10.585 80.724 10.633 140.981 180.515 200.819 60.736 51.000 10.617 2
HAISpermissive0.699 61.000 10.849 30.820 60.675 40.808 130.279 40.757 110.465 130.517 90.596 60.559 80.600 161.000 10.654 100.767 100.676 90.994 230.560 9
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021
SSTNetpermissive0.698 71.000 10.697 300.888 30.556 140.803 140.387 20.626 190.417 170.556 70.585 90.702 30.600 161.000 10.824 10.720 210.692 71.000 10.509 12
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021
SphereSeg0.680 81.000 10.856 20.744 140.618 70.893 10.151 130.651 170.713 10.537 80.579 110.430 180.651 61.000 10.389 290.744 170.697 60.991 240.601 4
MaskVoteNet_Coarse0.677 91.000 10.847 40.771 100.509 200.816 90.277 50.558 260.482 100.562 60.640 40.448 140.700 41.000 10.666 60.852 30.578 190.997 190.488 17
OccuSeg+instance0.672 101.000 10.758 240.682 170.576 120.842 70.477 10.504 290.524 90.567 50.585 100.451 130.557 221.000 10.751 30.797 80.563 221.000 10.467 20
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020
Mask-Group0.664 111.000 10.822 70.764 130.616 80.815 100.139 170.694 150.597 70.459 150.566 120.599 70.600 160.516 350.715 50.819 70.635 131.000 10.603 3
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022
INS-Conv-instance0.657 121.000 10.760 220.667 190.581 100.863 40.323 30.655 160.477 110.473 130.549 140.432 170.650 71.000 10.655 90.738 180.585 180.944 280.472 19
CSC-Pretrained0.648 131.000 10.810 90.768 110.523 190.813 110.143 160.819 40.389 180.422 220.511 180.443 150.650 71.000 10.624 140.732 190.634 141.000 10.375 26
PE0.645 141.000 10.773 190.798 80.538 160.786 200.088 240.799 70.350 220.435 210.547 150.545 90.646 130.933 200.562 170.761 130.556 270.997 190.501 15
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021
RPGN0.643 151.000 10.758 230.582 270.539 150.826 80.046 280.765 90.372 200.436 200.588 70.539 100.650 71.000 10.577 150.750 150.653 120.997 190.495 16
Dyco3Dcopyleft0.641 161.000 10.841 50.893 20.531 170.802 150.115 210.588 240.448 140.438 180.537 170.430 190.550 230.857 220.534 180.764 120.657 100.987 250.568 6
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021
GICN0.638 171.000 10.895 10.800 70.480 230.676 240.144 150.737 130.354 210.447 160.400 270.365 240.700 41.000 10.569 160.836 50.599 161.000 10.473 18
PointGroup0.636 181.000 10.765 200.624 210.505 220.797 160.116 200.696 140.384 190.441 170.559 130.476 110.596 191.000 10.666 60.756 140.556 260.997 190.513 11
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]
DD-UNet+Group0.635 190.667 290.797 150.714 160.562 130.774 210.146 140.810 60.429 160.476 120.546 160.399 210.633 141.000 10.632 130.722 200.609 151.000 10.514 10
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. ICCVW 2021
DENet0.629 201.000 10.797 140.608 220.589 90.627 280.219 100.882 10.310 240.402 260.383 290.396 220.650 71.000 10.663 80.543 350.691 81.000 10.568 7
3D-MPA0.611 211.000 10.833 60.765 120.526 180.756 220.136 190.588 240.470 120.438 190.432 250.358 250.650 70.857 220.429 250.765 110.557 251.000 10.430 22
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020
PCJC0.578 221.000 10.810 100.583 260.449 260.813 120.042 290.603 220.341 230.490 110.465 210.410 200.650 70.835 280.264 340.694 250.561 230.889 320.504 14
SSEN0.575 231.000 10.761 210.473 290.477 240.795 170.066 250.529 270.658 30.460 140.461 220.380 230.331 340.859 210.401 280.692 260.653 111.000 10.348 28
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv
RWSeg0.567 240.528 370.708 290.626 200.580 110.745 230.063 260.627 180.240 280.400 270.497 190.464 120.515 241.000 10.475 220.745 160.571 201.000 10.429 23
MTML0.549 251.000 10.807 120.588 250.327 300.647 260.004 340.815 50.180 300.418 230.364 300.182 290.445 281.000 10.442 240.688 270.571 211.000 10.396 24
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Sparse R-CNN0.515 261.000 10.538 370.282 320.468 250.790 180.173 120.345 330.429 150.413 250.484 200.176 300.595 200.591 330.522 190.668 280.476 310.986 260.327 29
Occipital-SCS0.512 271.000 10.716 260.509 280.506 210.611 290.092 230.602 230.177 310.346 300.383 280.165 310.442 290.850 270.386 300.618 310.543 280.889 320.389 25
3D-BoNet0.488 281.000 10.672 320.590 240.301 320.484 380.098 220.620 200.306 250.341 310.259 340.125 330.434 310.796 290.402 270.499 370.513 300.909 310.439 21
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-inst0.478 290.667 290.712 280.595 230.259 340.550 350.000 370.613 210.175 320.250 360.434 230.437 160.411 330.857 220.485 210.591 340.267 400.944 280.359 27
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SPG_WSIS0.470 300.667 290.685 310.677 180.372 280.562 330.000 370.482 300.244 270.316 330.298 310.052 390.442 300.857 220.267 330.702 220.559 241.000 10.287 31
SALoss-ResNet0.459 311.000 10.737 250.159 410.259 330.587 310.138 180.475 310.217 290.416 240.408 260.128 320.315 350.714 300.411 260.536 360.590 170.873 350.304 30
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)
MASCpermissive0.447 320.528 370.555 350.381 300.382 270.633 270.002 350.509 280.260 260.361 290.432 240.327 260.451 270.571 340.367 310.639 290.386 320.980 270.276 32
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
SegGroup_inspermissive0.445 330.667 290.773 180.185 380.317 310.656 250.000 370.407 320.134 330.381 280.267 330.217 280.476 260.714 300.452 230.629 300.514 291.000 10.222 35
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation.
3D-SISpermissive0.382 341.000 10.432 390.245 340.190 350.577 320.013 320.263 350.033 390.320 320.240 350.075 350.422 320.857 220.117 370.699 230.271 390.883 340.235 34
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
Hier3Dcopyleft0.323 350.667 290.542 360.264 330.157 380.550 340.000 370.205 380.009 400.270 350.218 360.075 350.500 250.688 320.007 430.698 240.301 360.459 400.200 36
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation.
UNet-backbone0.319 360.667 290.715 270.233 350.189 360.479 390.008 330.218 360.067 380.201 370.173 370.107 340.123 400.438 360.150 350.615 320.355 330.916 300.093 42
R-PointNet0.306 370.500 390.405 400.311 310.348 290.589 300.054 270.068 410.126 340.283 340.290 320.028 400.219 380.214 390.331 320.396 410.275 370.821 370.245 33
SemRegionNet0.250 380.333 400.613 330.229 360.163 370.493 360.000 370.304 340.107 350.147 390.100 380.052 380.231 360.119 400.039 390.445 390.325 340.654 380.141 38
3D-BEVIS0.248 390.667 290.566 340.076 420.035 430.394 410.027 310.035 420.098 360.099 410.030 420.025 410.098 410.375 380.126 360.604 330.181 410.854 360.171 37
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Region0.248 390.667 290.437 380.188 370.153 390.491 370.000 370.208 370.094 370.153 380.099 390.057 370.217 390.119 400.039 390.466 380.302 350.640 390.140 39
ASIS0.199 410.333 400.253 420.167 400.140 400.438 400.000 370.177 390.008 410.121 400.069 400.004 430.231 370.429 370.036 410.445 400.273 380.333 420.119 41
Sgpn_scannet0.143 420.208 430.390 410.169 390.065 410.275 420.029 300.069 400.000 420.087 420.043 410.014 420.027 430.000 420.112 380.351 420.168 420.438 410.138 40
MaskRCNN 2d->3d Proj0.058 430.333 400.002 430.000 430.053 420.002 430.002 360.021 430.000 420.045 430.024 430.238 270.065 420.000 420.014 420.107 430.020 430.110 430.006 43

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 10.422 110.898 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 70.769 30.656 30.567 30.931 30.395 30.390 40.700 20.534 30.689 60.770 20.574 30.865 40.831 30.675 3
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 20.179 170.648 30.463 30.549 10.742 40.676 20.628 20.961 10.420 20.379 50.684 40.381 110.732 20.723 30.599 20.827 90.851 20.634 4
CMX0.613 40.681 60.725 80.502 110.634 50.297 130.478 50.830 20.651 40.537 50.924 40.375 40.315 100.686 30.451 90.714 30.543 150.504 50.894 30.823 40.688 2
DMMF_3d0.605 50.651 70.744 70.782 30.637 40.387 40.536 20.732 50.590 60.540 40.856 140.359 70.306 110.596 70.539 20.627 130.706 40.497 70.785 130.757 120.476 14
DMMF0.597 60.543 120.755 60.749 40.585 70.338 60.494 40.704 70.598 50.494 110.911 70.347 90.327 90.593 80.527 40.675 80.646 80.513 40.842 70.774 90.527 12
MCA-Net0.595 70.533 130.756 50.746 50.590 60.334 80.506 30.670 80.587 70.500 90.905 90.366 60.352 60.601 60.506 60.669 110.648 60.501 60.839 80.769 100.516 13
RFBNet0.592 80.616 80.758 40.659 60.581 80.330 90.469 60.655 110.543 100.524 60.924 40.355 80.336 80.572 90.479 80.671 90.648 60.480 80.814 110.814 50.614 7
DCRedNet0.583 90.682 50.723 90.542 100.510 120.310 110.451 70.668 90.549 90.520 70.920 60.375 40.446 20.528 120.417 100.670 100.577 130.478 90.862 50.806 60.628 6
SSMAcopyleft0.577 100.695 40.716 110.439 130.563 90.314 100.444 90.719 60.551 80.503 80.887 110.346 100.348 70.603 50.353 130.709 40.600 110.457 110.901 20.786 70.599 8
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
SN_RN152pyrx8_RVCcopyleft0.546 110.572 100.663 140.638 80.518 100.298 120.366 160.633 130.510 120.446 130.864 120.296 120.267 130.542 110.346 140.704 50.575 140.431 130.853 60.766 110.630 5
FuseNetpermissive0.535 120.570 110.681 130.182 160.512 110.290 140.431 100.659 100.504 130.495 100.903 100.308 110.428 30.523 130.365 120.676 70.621 100.470 100.762 140.779 80.541 10
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
AdapNet++copyleft0.503 130.613 90.722 100.418 140.358 180.337 70.370 150.479 160.443 140.368 160.907 80.207 150.213 170.464 160.525 50.618 140.657 50.450 120.788 120.721 150.408 17
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
3DMV (2d proj)0.498 140.481 160.612 150.579 90.456 140.343 50.384 130.623 140.525 110.381 150.845 150.254 140.264 150.557 100.182 160.581 160.598 120.429 140.760 150.661 170.446 16
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
MSeg1080_RVCpermissive0.485 150.505 140.709 120.092 180.427 150.241 150.411 120.654 120.385 180.457 120.861 130.053 180.279 120.503 140.481 70.645 120.626 90.365 160.748 160.725 140.529 11
John Lambert*, Zhuang Liu*, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
ILC-PSPNet0.475 160.490 150.581 160.289 150.507 130.067 180.379 140.610 150.417 160.435 140.822 170.278 130.267 130.503 140.228 150.616 150.533 160.375 150.820 100.729 130.560 9
Enet (reimpl)0.376 170.264 180.452 180.452 120.365 160.181 160.143 180.456 170.409 170.346 170.769 180.164 160.218 160.359 170.123 180.403 180.381 180.313 180.571 170.685 160.472 15
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)permissive0.330 180.293 170.521 170.657 70.361 170.161 170.250 170.004 180.440 150.183 180.836 160.125 170.060 180.319 180.132 170.417 170.412 170.344 170.541 180.427 180.109 18
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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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