The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).



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


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysorted 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 10.843 100.781 10.858 70.575 30.831 180.685 70.714 10.979 10.594 30.310 160.801 10.892 80.841 20.819 30.723 30.940 70.887 10.725 12
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
VMNetpermissive0.746 110.870 120.838 20.858 40.729 170.850 120.501 230.874 70.587 400.658 70.956 130.564 120.299 200.765 90.900 50.716 240.812 70.631 270.939 80.858 150.709 18
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)
IPCA0.731 190.890 80.837 30.864 20.726 180.873 20.530 150.824 220.489 700.647 80.978 20.609 20.336 70.624 360.733 450.758 100.776 250.570 520.949 20.877 50.728 8
O-CNNpermissive0.762 40.924 20.823 40.844 90.770 20.852 100.577 20.847 140.711 10.640 150.958 90.592 40.217 570.762 100.888 90.758 100.813 60.726 10.932 140.868 80.744 4
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
SparseConvNet0.725 200.647 730.821 50.846 70.721 200.869 30.533 120.754 410.603 340.614 220.955 170.572 100.325 110.710 210.870 130.724 190.823 20.628 280.934 110.865 110.683 25
LargeKernel3D0.739 170.909 40.820 60.806 260.740 130.852 100.545 90.826 200.594 380.643 110.955 170.541 190.263 420.723 200.858 190.775 60.767 310.678 100.933 120.848 230.694 23
CU-Hybrid Net0.764 20.924 20.819 70.840 110.757 60.853 90.580 10.848 130.709 20.643 110.958 90.587 70.295 220.753 140.884 120.758 100.815 50.725 20.927 160.867 90.743 5
Virtual MVFusion0.746 110.771 390.819 70.848 60.702 250.865 50.397 680.899 30.699 30.664 60.948 390.588 60.330 90.746 170.851 240.764 80.796 140.704 60.935 100.866 100.728 8
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNetcopyleft0.749 70.909 40.818 90.811 210.752 80.839 180.485 310.842 150.673 100.644 100.957 120.528 240.305 180.773 60.859 170.788 40.818 40.693 70.916 200.856 170.723 13
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MinkowskiNetpermissive0.736 180.859 150.818 90.832 130.709 220.840 170.521 180.853 110.660 150.643 110.951 300.544 180.286 270.731 190.893 70.675 380.772 270.683 90.874 490.852 210.727 10
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
MatchingNet0.724 220.812 280.812 110.810 220.735 150.834 230.495 280.860 100.572 460.602 290.954 220.512 280.280 290.757 120.845 260.725 180.780 230.606 380.937 90.851 220.700 21
PointTransformer++0.725 200.727 590.811 120.819 160.765 40.841 160.502 220.814 270.621 240.623 190.955 170.556 150.284 280.620 370.866 140.781 50.757 390.648 190.932 140.862 120.709 18
PointTransformerV20.752 50.742 520.809 130.872 10.758 50.860 60.552 70.891 50.610 280.687 20.960 70.559 140.304 190.766 80.926 20.767 70.797 130.644 210.942 50.876 70.722 14
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
LRPNet0.742 150.816 260.806 140.807 240.752 80.828 280.575 30.839 170.699 30.637 160.954 220.520 260.320 130.755 130.834 280.760 90.772 270.676 110.915 210.862 120.717 15
MSP0.748 90.623 760.804 150.859 30.745 120.824 320.501 230.912 20.690 60.685 30.956 130.567 110.320 130.768 70.918 30.720 210.802 90.676 110.921 170.881 40.779 1
StratifiedFormerpermissive0.747 100.901 70.803 160.845 80.757 60.846 140.512 190.825 210.696 50.645 90.956 130.576 90.262 430.744 180.861 160.742 140.770 300.705 50.899 310.860 140.734 6
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
Retro-FPN0.744 130.842 190.800 170.767 400.740 130.836 220.541 100.914 10.672 110.626 180.958 90.552 170.272 340.777 40.886 110.696 310.801 100.674 130.941 60.858 150.717 15
EQ-Net0.743 140.620 770.799 180.849 50.730 160.822 340.493 290.897 40.664 120.681 40.955 170.562 130.378 10.760 110.903 40.738 150.801 100.673 140.907 240.877 50.745 3
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
One Thing One Click0.701 270.825 240.796 190.723 470.716 210.832 240.433 570.816 230.634 200.609 240.969 60.418 650.344 50.559 520.833 290.715 250.808 80.560 560.902 280.847 250.680 26
OccuSeg+Semantic0.764 20.758 450.796 190.839 120.746 110.907 10.562 50.850 120.680 90.672 50.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 10
One-Thing-One-Click0.693 300.743 510.794 210.655 700.684 290.822 340.497 270.719 510.622 230.617 200.977 40.447 520.339 60.750 160.664 600.703 290.790 200.596 420.946 40.855 190.647 35
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
DMF-Net0.752 50.906 60.793 220.802 280.689 270.825 300.556 60.867 80.681 80.602 290.960 70.555 160.365 30.779 30.859 170.747 130.795 170.717 40.917 190.856 170.764 2
PicassoNet-IIpermissive0.696 290.704 630.790 230.787 320.709 220.837 200.459 400.815 250.543 550.615 210.956 130.529 220.250 460.551 570.790 370.703 290.799 120.619 330.908 230.848 230.700 21
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
PointConvFormer0.749 70.793 310.790 230.807 240.750 100.856 80.524 160.881 60.588 390.642 140.977 40.591 50.274 320.781 20.929 10.804 30.796 140.642 220.947 30.885 20.715 17
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
PointMetaBase0.714 240.835 200.785 250.821 140.684 290.846 140.531 140.865 90.614 250.596 320.953 250.500 310.246 490.674 220.888 90.692 320.764 330.624 290.849 630.844 280.675 27
PointContrast_LA_SEM0.683 360.757 460.784 260.786 330.639 410.824 320.408 620.775 350.604 330.541 440.934 710.532 210.269 380.552 550.777 380.645 530.793 180.640 230.913 220.824 340.671 28
SimConv0.410 920.000 970.782 270.772 370.722 190.838 190.407 640.000 980.000 980.595 330.947 410.000 980.270 370.000 980.000 980.000 980.786 210.621 320.000 980.841 290.621 43
PointASNLpermissive0.666 410.703 650.781 280.751 460.655 340.830 250.471 340.769 370.474 730.537 460.951 300.475 380.279 300.635 310.698 540.675 380.751 410.553 610.816 700.806 440.703 20
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
joint point-basedpermissive0.634 560.614 780.778 290.667 670.633 440.825 300.420 600.804 290.467 750.561 390.951 300.494 320.291 240.566 490.458 750.579 720.764 330.559 580.838 650.814 400.598 54
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
contrastBoundarypermissive0.705 250.769 420.775 300.809 230.687 280.820 370.439 550.812 280.661 140.591 350.945 480.515 270.171 750.633 330.856 200.720 210.796 140.668 150.889 380.847 250.689 24
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
SPH3D-GCNpermissive0.610 660.858 160.772 310.489 880.532 690.792 590.404 660.643 680.570 490.507 590.935 670.414 660.046 940.510 650.702 520.602 640.705 580.549 630.859 600.773 670.534 74
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
SALANet0.670 400.816 260.770 320.768 390.652 360.807 480.451 420.747 430.659 160.545 430.924 770.473 390.149 850.571 480.811 340.635 560.746 430.623 300.892 360.794 520.570 62
PD-Net0.638 520.797 300.769 330.641 760.590 540.820 370.461 390.537 810.637 190.536 470.947 410.388 720.206 610.656 250.668 580.647 510.732 480.585 490.868 560.793 540.473 85
SAT0.742 150.860 140.765 340.819 160.769 30.848 130.533 120.829 190.663 130.631 170.955 170.586 80.274 320.753 140.896 60.729 160.760 360.666 160.921 170.855 190.733 7
JSENetpermissive0.699 280.881 110.762 350.821 140.667 320.800 530.522 170.792 330.613 260.607 260.935 670.492 330.205 620.576 460.853 220.691 330.758 380.652 180.872 520.828 320.649 34
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
FPConvpermissive0.639 510.785 330.760 360.713 530.603 490.798 540.392 700.534 820.603 340.524 520.948 390.457 450.250 460.538 590.723 480.598 660.696 610.614 340.872 520.799 470.567 64
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PointConvpermissive0.666 410.781 340.759 370.699 550.644 400.822 340.475 320.779 340.564 510.504 600.953 250.428 590.203 640.586 440.754 410.661 440.753 400.588 480.902 280.813 420.642 36
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
INS-Conv-semantic0.717 230.751 480.759 370.812 200.704 240.868 40.537 110.842 150.609 300.608 250.953 250.534 200.293 230.616 380.864 150.719 230.793 180.640 230.933 120.845 270.663 30
KP-FCNN0.684 340.847 180.758 390.784 340.647 370.814 440.473 330.772 360.605 320.594 340.935 670.450 500.181 730.587 420.805 350.690 340.785 220.614 340.882 420.819 390.632 40
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 340.728 580.757 400.776 360.690 260.804 510.464 380.816 230.577 450.587 360.945 480.508 300.276 310.671 230.710 500.663 430.750 420.589 470.881 430.832 310.653 33
Feature_GeometricNetpermissive0.690 310.884 100.754 410.795 310.647 370.818 410.422 590.802 310.612 270.604 270.945 480.462 430.189 700.563 510.853 220.726 170.765 320.632 260.904 260.821 380.606 49
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
VI-PointConv0.676 380.770 410.754 410.783 350.621 450.814 440.552 70.758 390.571 480.557 400.954 220.529 220.268 400.530 610.682 550.675 380.719 510.603 390.888 390.833 300.665 29
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 390.789 320.748 430.763 420.635 430.814 440.407 640.747 430.581 440.573 370.950 340.484 340.271 360.607 390.754 410.649 480.774 260.596 420.883 410.823 350.606 49
Feature-Geometry Netpermissive0.685 330.866 130.748 430.819 160.645 390.794 560.450 450.802 310.587 400.604 270.945 480.464 420.201 650.554 540.840 270.723 200.732 480.602 400.907 240.822 370.603 52
SegGroup_sempermissive0.627 630.818 250.747 450.701 540.602 500.764 700.385 740.629 690.490 680.508 570.931 740.409 670.201 650.564 500.725 470.618 590.692 620.539 680.873 500.794 520.548 71
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
RFCR0.702 260.889 90.745 460.813 190.672 310.818 410.493 290.815 250.623 220.610 230.947 410.470 400.249 480.594 410.848 250.705 280.779 240.646 200.892 360.823 350.611 45
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
3DSM_DMMF0.631 580.626 750.745 460.801 290.607 480.751 740.506 200.729 500.565 500.491 620.866 910.434 540.197 680.595 400.630 620.709 270.705 580.560 560.875 470.740 760.491 80
HPGCNN0.656 450.698 660.743 480.650 710.564 630.820 370.505 210.758 390.631 210.479 640.945 480.480 360.226 530.572 470.774 390.690 340.735 460.614 340.853 620.776 660.597 55
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
wsss-transformer0.600 680.634 740.743 480.697 570.601 510.781 620.437 560.585 750.493 670.446 710.933 720.394 700.011 960.654 260.661 610.603 630.733 470.526 710.832 660.761 710.480 82
FusionNet0.688 320.704 630.741 500.754 440.656 330.829 260.501 230.741 460.609 300.548 420.950 340.522 250.371 20.633 330.756 400.715 250.771 290.623 300.861 590.814 400.658 31
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
FusionAwareConv0.630 610.604 800.741 500.766 410.590 540.747 750.501 230.734 480.503 650.527 500.919 810.454 470.323 120.550 580.420 790.678 370.688 640.544 640.896 330.795 510.627 42
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
SAFNet-segpermissive0.654 460.752 470.734 520.664 680.583 580.815 430.399 670.754 410.639 180.535 480.942 570.470 400.309 170.665 240.539 670.650 470.708 560.635 250.857 610.793 540.642 36
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 470.778 350.731 530.699 550.577 590.829 260.446 470.736 470.477 720.523 540.945 480.454 470.269 380.484 720.749 440.618 590.738 440.599 410.827 670.792 570.621 43
Superpoint Network0.683 360.851 170.728 540.800 300.653 350.806 490.468 350.804 290.572 460.602 290.946 450.453 490.239 520.519 630.822 300.689 360.762 350.595 440.895 340.827 330.630 41
MVPNetpermissive0.641 480.831 210.715 550.671 650.590 540.781 620.394 690.679 610.642 170.553 410.937 640.462 430.256 440.649 270.406 800.626 570.691 630.666 160.877 450.792 570.608 48
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
Supervoxel-CNN0.635 550.656 710.711 560.719 500.613 470.757 730.444 520.765 380.534 570.566 380.928 750.478 370.272 340.636 300.531 690.664 420.645 760.508 740.864 580.792 570.611 45
PPCNN++permissive0.663 430.746 490.708 570.722 480.638 420.820 370.451 420.566 770.599 360.541 440.950 340.510 290.313 150.648 280.819 320.616 610.682 660.590 460.869 550.810 430.656 32
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
ROSMRF0.580 720.772 380.707 580.681 610.563 640.764 700.362 770.515 830.465 760.465 680.936 660.427 610.207 600.438 750.577 650.536 760.675 690.486 790.723 810.779 630.524 76
PointConv-SFPN0.641 480.776 370.703 590.721 490.557 660.826 290.451 420.672 630.563 520.483 630.943 560.425 620.162 800.644 290.726 460.659 450.709 550.572 510.875 470.786 610.559 67
DCM-Net0.658 440.778 350.702 600.806 260.619 460.813 470.468 350.693 590.494 660.524 520.941 590.449 510.298 210.510 650.821 310.675 380.727 500.568 540.826 680.803 460.637 38
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
PointMRNet0.640 500.717 620.701 610.692 580.576 600.801 520.467 370.716 520.563 520.459 690.953 250.429 580.169 770.581 450.854 210.605 620.710 530.550 620.894 350.793 540.575 60
DPC0.592 700.720 600.700 620.602 810.480 760.762 720.380 750.713 550.585 430.437 740.940 610.369 750.288 250.434 770.509 730.590 700.639 790.567 550.772 750.755 730.592 57
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
DVVNet0.562 760.648 720.700 620.770 380.586 570.687 820.333 810.650 650.514 630.475 660.906 850.359 760.223 550.340 830.442 780.422 870.668 710.501 750.708 820.779 630.534 74
SConv0.636 540.830 220.697 640.752 450.572 620.780 640.445 490.716 520.529 580.530 490.951 300.446 530.170 760.507 670.666 590.636 550.682 660.541 670.886 400.799 470.594 56
SQN_0.1%0.569 740.676 680.696 650.657 690.497 730.779 650.424 580.548 790.515 620.376 810.902 880.422 630.357 40.379 810.456 760.596 670.659 730.544 640.685 840.665 890.556 69
SIConv0.625 640.830 220.694 660.757 430.563 640.772 680.448 460.647 670.520 600.509 560.949 370.431 570.191 690.496 690.614 630.647 510.672 700.535 700.876 460.783 620.571 61
GMLPs0.538 780.495 880.693 670.647 730.471 780.793 570.300 840.477 840.505 640.358 830.903 870.327 810.081 910.472 730.529 700.448 850.710 530.509 720.746 770.737 770.554 70
LAP-D0.594 690.720 600.692 680.637 770.456 800.773 670.391 720.730 490.587 400.445 730.940 610.381 730.288 250.434 770.453 770.591 680.649 740.581 500.777 740.749 750.610 47
PointSPNet0.637 530.734 550.692 680.714 520.576 600.797 550.446 470.743 450.598 370.437 740.942 570.403 680.150 840.626 350.800 360.649 480.697 600.557 590.846 640.777 650.563 65
PointNet2-SFPN0.631 580.771 390.692 680.672 630.524 700.837 200.440 540.706 570.538 560.446 710.944 540.421 640.219 560.552 550.751 430.591 680.737 450.543 660.901 300.768 680.557 68
PointMTL0.632 570.731 560.688 710.675 620.591 530.784 610.444 520.565 780.610 280.492 610.949 370.456 460.254 450.587 420.706 510.599 650.665 720.612 370.868 560.791 600.579 59
PanopticFusion-label0.529 790.491 890.688 710.604 800.386 850.632 890.225 940.705 580.434 820.293 890.815 920.348 790.241 510.499 680.669 570.507 780.649 740.442 880.796 720.602 920.561 66
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
APCF-Net0.631 580.742 520.687 730.672 630.557 660.792 590.408 620.665 640.545 540.508 570.952 290.428 590.186 710.634 320.702 520.620 580.706 570.555 600.873 500.798 490.581 58
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
HPEIN0.618 650.729 570.668 740.647 730.597 520.766 690.414 610.680 600.520 600.525 510.946 450.432 550.215 580.493 700.599 640.638 540.617 810.570 520.897 320.806 440.605 51
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
AttAN0.609 670.760 440.667 750.649 720.521 710.793 570.457 410.648 660.528 590.434 760.947 410.401 690.153 830.454 740.721 490.648 500.717 520.536 690.904 260.765 690.485 81
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
TextureNetpermissive0.566 750.672 700.664 760.671 650.494 740.719 780.445 490.678 620.411 850.396 790.935 670.356 770.225 540.412 790.535 680.565 740.636 800.464 820.794 730.680 860.568 63
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++ & Featurepermissive0.557 770.735 540.661 770.686 590.491 750.744 760.392 700.539 800.451 780.375 820.946 450.376 740.205 620.403 800.356 830.553 750.643 770.497 760.824 690.756 720.515 77
CCRFNet0.589 710.766 430.659 780.683 600.470 790.740 770.387 730.620 710.490 680.476 650.922 790.355 780.245 500.511 640.511 720.571 730.643 770.493 780.872 520.762 700.600 53
3DWSSS0.425 910.525 860.647 790.522 860.324 920.488 960.077 970.712 560.353 890.401 780.636 970.281 870.176 740.340 830.565 660.175 960.551 870.398 920.370 960.602 920.361 90
Tangent Convolutionspermissive0.438 900.437 930.646 800.474 890.369 870.645 870.353 780.258 920.282 940.279 900.918 820.298 850.147 860.283 870.294 850.487 800.562 850.427 900.619 890.633 900.352 91
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PCNN0.498 830.559 830.644 810.560 850.420 840.711 800.229 920.414 850.436 810.352 850.941 590.324 820.155 820.238 900.387 820.493 790.529 900.509 720.813 710.751 740.504 79
Online SegFusion0.515 810.607 790.644 810.579 830.434 820.630 900.353 780.628 700.440 800.410 770.762 950.307 830.167 780.520 620.403 810.516 770.565 840.447 860.678 850.701 830.514 78
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
DenSeR0.628 620.800 290.625 830.719 500.545 680.806 490.445 490.597 720.448 790.519 550.938 630.481 350.328 100.489 710.499 740.657 460.759 370.592 450.881 430.797 500.634 39
DGCNN_reproducecopyleft0.446 870.474 910.623 840.463 900.366 880.651 860.310 820.389 880.349 900.330 860.937 640.271 880.126 870.285 860.224 890.350 920.577 830.445 870.625 880.723 800.394 88
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
SurfaceConvPF0.442 880.505 870.622 850.380 950.342 910.654 850.227 930.397 870.367 880.276 910.924 770.240 910.198 670.359 820.262 860.366 890.581 820.435 890.640 870.668 870.398 87
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PointCNN with RGBpermissive0.458 850.577 820.611 860.356 960.321 930.715 790.299 860.376 890.328 920.319 870.944 540.285 860.164 790.216 930.229 880.484 810.545 880.456 840.755 760.709 820.475 84
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
SD-DETR0.576 730.746 490.609 870.445 920.517 720.643 880.366 760.714 540.456 770.468 670.870 900.432 550.264 410.558 530.674 560.586 710.688 640.482 800.739 790.733 780.537 73
3DMV, FTSDF0.501 820.558 840.608 880.424 940.478 770.690 810.246 900.586 740.468 740.450 700.911 830.394 700.160 810.438 750.212 900.432 860.541 890.475 810.742 780.727 790.477 83
FCPNpermissive0.447 860.679 670.604 890.578 840.380 860.682 830.291 870.106 950.483 710.258 940.920 800.258 900.025 950.231 920.325 840.480 820.560 860.463 830.725 800.666 880.231 96
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
subcloud_weak0.516 800.676 680.591 900.609 780.442 810.774 660.335 800.597 720.422 840.357 840.932 730.341 800.094 900.298 850.528 710.473 830.676 680.495 770.602 900.721 810.349 92
PNET20.442 880.548 850.548 910.597 820.363 890.628 910.300 840.292 900.374 870.307 880.881 890.268 890.186 710.238 900.204 920.407 880.506 940.449 850.667 860.620 910.462 86
3DMV0.484 840.484 900.538 920.643 750.424 830.606 930.310 820.574 760.433 830.378 800.796 930.301 840.214 590.537 600.208 910.472 840.507 930.413 910.693 830.602 920.539 72
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
SPLAT Netcopyleft0.393 930.472 920.511 930.606 790.311 940.656 840.245 910.405 860.328 920.197 950.927 760.227 930.000 980.001 970.249 870.271 950.510 910.383 940.593 910.699 840.267 94
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+FTSDF0.383 940.297 950.491 940.432 930.358 900.612 920.274 880.116 940.411 850.265 920.904 860.229 920.079 920.250 880.185 930.320 930.510 910.385 930.548 920.597 950.394 88
PointNet++permissive0.339 950.584 810.478 950.458 910.256 960.360 970.250 890.247 930.278 950.261 930.677 960.183 940.117 880.212 940.145 950.364 900.346 970.232 970.548 920.523 960.252 95
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
ScanNetpermissive0.306 970.203 960.366 960.501 870.311 940.524 950.211 950.002 970.342 910.189 960.786 940.145 960.102 890.245 890.152 940.318 940.348 960.300 960.460 950.437 970.182 97
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SSC-UNetpermissive0.308 960.353 940.290 970.278 970.166 970.553 940.169 960.286 910.147 960.148 970.908 840.182 950.064 930.023 960.018 970.354 910.363 950.345 950.546 940.685 850.278 93
ERROR0.054 980.000 970.041 980.172 980.030 980.062 980.001 980.035 960.004 970.051 980.143 980.019 970.003 970.041 950.050 960.003 970.054 980.018 980.005 970.264 980.082 98