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
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 10.843 110.781 10.858 70.575 30.831 200.685 70.714 10.979 10.594 30.310 180.801 10.892 80.841 20.819 30.723 30.940 80.887 10.725 13
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
CU-Hybrid Net0.764 20.924 20.819 90.840 120.757 70.853 90.580 10.848 150.709 20.643 130.958 110.587 70.295 240.753 150.884 120.758 120.815 60.725 20.927 180.867 110.743 6
OccuSeg+Semantic0.764 20.758 470.796 210.839 130.746 120.907 10.562 50.850 140.680 90.672 60.978 20.610 10.335 80.777 40.819 320.847 10.830 10.691 80.972 10.885 20.727 11
O-CNNpermissive0.762 40.924 20.823 60.844 100.770 30.852 100.577 20.847 160.711 10.640 170.958 110.592 40.217 590.762 110.888 90.758 120.813 70.726 10.932 160.868 100.744 5
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
OA-CNN-L_ScanNet200.756 50.783 350.826 50.858 40.776 20.837 200.548 90.896 50.649 170.675 50.962 80.586 80.335 80.771 70.802 360.770 80.787 220.691 80.936 110.880 50.761 3
PointTransformerV20.752 60.742 540.809 150.872 10.758 60.860 60.552 70.891 60.610 310.687 20.960 90.559 150.304 210.766 90.926 20.767 90.797 140.644 230.942 60.876 80.722 15
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
DMF-Net0.752 60.906 60.793 240.802 300.689 280.825 320.556 60.867 100.681 80.602 310.960 90.555 170.365 30.779 30.859 170.747 150.795 180.717 40.917 210.856 190.764 2
PointConvFormer0.749 80.793 320.790 250.807 250.750 110.856 80.524 170.881 80.588 420.642 160.977 40.591 50.274 350.781 20.929 10.804 30.796 150.642 240.947 30.885 20.715 18
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 80.909 40.818 110.811 220.752 90.839 190.485 330.842 170.673 100.644 120.957 140.528 260.305 200.773 60.859 170.788 40.818 50.693 70.916 220.856 190.723 14
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 100.623 800.804 170.859 30.745 130.824 340.501 240.912 20.690 60.685 30.956 150.567 120.320 140.768 80.918 30.720 230.802 100.676 130.921 190.881 40.779 1
StratifiedFormerpermissive0.747 110.901 70.803 180.845 90.757 70.846 140.512 200.825 230.696 50.645 110.956 150.576 100.262 450.744 190.861 160.742 160.770 310.705 50.899 340.860 160.734 7
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 MVFusion0.746 120.771 410.819 90.848 70.702 260.865 50.397 710.899 30.699 30.664 80.948 420.588 60.330 100.746 180.851 240.764 100.796 150.704 60.935 120.866 120.728 9
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 120.870 120.838 20.858 40.729 180.850 120.501 240.874 90.587 430.658 90.956 150.564 130.299 220.765 100.900 50.716 260.812 80.631 290.939 90.858 170.709 19
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)
Retro-FPN0.744 140.842 190.800 190.767 420.740 140.836 230.541 110.914 10.672 110.626 200.958 110.552 180.272 370.777 40.886 110.696 330.801 110.674 150.941 70.858 170.717 16
EQ-Net0.743 150.620 810.799 200.849 60.730 170.822 360.493 310.897 40.664 120.681 40.955 190.562 140.378 10.760 120.903 40.738 170.801 110.673 160.907 270.877 60.745 4
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
SAT0.742 160.860 140.765 360.819 170.769 40.848 130.533 130.829 210.663 130.631 190.955 190.586 80.274 350.753 150.896 60.729 180.760 380.666 180.921 190.855 210.733 8
LRPNet0.742 160.816 270.806 160.807 250.752 90.828 300.575 30.839 190.699 30.637 180.954 240.520 280.320 140.755 140.834 280.760 110.772 280.676 130.915 230.862 140.717 16
TXC0.740 180.842 190.832 40.805 290.715 220.846 140.473 350.885 70.615 270.671 70.971 60.547 190.320 140.697 230.799 380.777 60.819 30.682 110.946 40.871 90.696 24
LargeKernel3D0.739 190.909 40.820 80.806 270.740 140.852 100.545 100.826 220.594 410.643 130.955 190.541 210.263 440.723 210.858 190.775 70.767 320.678 120.933 140.848 250.694 25
MinkowskiNetpermissive0.736 200.859 150.818 110.832 140.709 230.840 180.521 190.853 130.660 150.643 130.951 320.544 200.286 290.731 200.893 70.675 400.772 280.683 100.874 520.852 230.727 11
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 210.890 80.837 30.864 20.726 190.873 20.530 160.824 240.489 740.647 100.978 20.609 20.336 70.624 380.733 470.758 120.776 260.570 540.949 20.877 60.728 9
SparseConvNet0.725 220.647 770.821 70.846 80.721 200.869 30.533 130.754 440.603 370.614 240.955 190.572 110.325 120.710 220.870 130.724 210.823 20.628 300.934 130.865 130.683 28
PointTransformer++0.725 220.727 610.811 140.819 170.765 50.841 170.502 230.814 300.621 260.623 210.955 190.556 160.284 300.620 390.866 140.781 50.757 410.648 210.932 160.862 140.709 19
MatchingNet0.724 240.812 290.812 130.810 230.735 160.834 250.495 300.860 120.572 490.602 310.954 240.512 300.280 320.757 130.845 260.725 200.780 240.606 400.937 100.851 240.700 22
INS-Conv-semantic0.717 250.751 500.759 390.812 210.704 250.868 40.537 120.842 170.609 330.608 270.953 270.534 220.293 250.616 400.864 150.719 250.793 190.640 250.933 140.845 300.663 33
PointMetaBase0.714 260.835 210.785 270.821 150.684 300.846 140.531 150.865 110.614 280.596 350.953 270.500 330.246 510.674 240.888 90.692 340.764 340.624 310.849 670.844 310.675 30
contrastBoundarypermissive0.705 270.769 440.775 320.809 240.687 290.820 390.439 590.812 310.661 140.591 370.945 500.515 290.171 770.633 350.856 200.720 230.796 150.668 170.889 410.847 270.689 26
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 280.889 90.745 480.813 200.672 320.818 430.493 310.815 280.623 240.610 250.947 440.470 430.249 500.594 430.848 250.705 300.779 250.646 220.892 390.823 370.611 47
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 Click0.701 290.825 250.796 210.723 490.716 210.832 260.433 610.816 260.634 220.609 260.969 70.418 680.344 50.559 550.833 290.715 270.808 90.560 580.902 310.847 270.680 29
JSENetpermissive0.699 300.881 110.762 370.821 150.667 330.800 560.522 180.792 360.613 290.607 280.935 700.492 350.205 640.576 480.853 220.691 350.758 400.652 200.872 550.828 340.649 37
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-IIpermissive0.696 310.704 660.790 250.787 340.709 230.837 200.459 430.815 280.543 580.615 230.956 150.529 240.250 480.551 600.790 390.703 310.799 130.619 350.908 260.848 250.700 22
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 320.743 530.794 230.655 730.684 300.822 360.497 290.719 540.622 250.617 220.977 40.447 550.339 60.750 170.664 620.703 310.790 210.596 440.946 40.855 210.647 38
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Feature_GeometricNetpermissive0.690 330.884 100.754 430.795 330.647 390.818 430.422 630.802 340.612 300.604 290.945 500.462 460.189 720.563 540.853 220.726 190.765 330.632 280.904 290.821 400.606 51
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 340.704 660.741 520.754 460.656 350.829 280.501 240.741 490.609 330.548 440.950 360.522 270.371 20.633 350.756 420.715 270.771 300.623 320.861 630.814 420.658 34
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 350.866 130.748 450.819 170.645 410.794 590.450 480.802 340.587 430.604 290.945 500.464 450.201 670.554 570.840 270.723 220.732 500.602 420.907 270.822 390.603 54
VACNN++0.684 360.728 600.757 420.776 390.690 270.804 530.464 410.816 260.577 480.587 380.945 500.508 320.276 340.671 250.710 520.663 450.750 440.589 490.881 460.832 330.653 36
KP-FCNN0.684 360.847 180.758 410.784 360.647 390.814 460.473 350.772 390.605 350.594 360.935 700.450 530.181 750.587 440.805 350.690 360.785 230.614 360.882 450.819 410.632 43
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
DGNet0.684 360.712 650.784 280.782 380.658 340.835 240.499 280.823 250.641 190.597 340.950 360.487 360.281 310.575 490.619 650.647 530.764 340.620 340.871 580.846 290.688 27
Superpoint Network0.683 390.851 170.728 560.800 320.653 370.806 510.468 380.804 320.572 490.602 310.946 470.453 520.239 540.519 660.822 300.689 380.762 370.595 460.895 370.827 350.630 44
PointContrast_LA_SEM0.683 390.757 480.784 280.786 350.639 430.824 340.408 660.775 380.604 360.541 460.934 740.532 230.269 400.552 580.777 400.645 560.793 190.640 250.913 240.824 360.671 31
VI-PointConv0.676 410.770 430.754 430.783 370.621 470.814 460.552 70.758 420.571 510.557 420.954 240.529 240.268 420.530 640.682 570.675 400.719 530.603 410.888 420.833 320.665 32
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 420.789 330.748 450.763 440.635 450.814 460.407 680.747 460.581 470.573 390.950 360.484 370.271 390.607 410.754 430.649 500.774 270.596 440.883 440.823 370.606 51
SALANet0.670 430.816 270.770 340.768 410.652 380.807 500.451 450.747 460.659 160.545 450.924 800.473 420.149 870.571 510.811 340.635 590.746 450.623 320.892 390.794 540.570 64
PointConvpermissive0.666 440.781 360.759 390.699 580.644 420.822 360.475 340.779 370.564 540.504 620.953 270.428 620.203 660.586 460.754 430.661 460.753 420.588 500.902 310.813 440.642 39
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 440.703 680.781 300.751 480.655 360.830 270.471 370.769 400.474 770.537 480.951 320.475 410.279 330.635 330.698 560.675 400.751 430.553 630.816 740.806 460.703 21
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 460.746 510.708 600.722 500.638 440.820 390.451 450.566 810.599 390.541 460.950 360.510 310.313 170.648 300.819 320.616 640.682 690.590 480.869 590.810 450.656 35
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 470.778 370.702 630.806 270.619 480.813 490.468 380.693 620.494 690.524 540.941 610.449 540.298 230.510 680.821 310.675 400.727 520.568 560.826 720.803 480.637 41
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 480.698 690.743 500.650 740.564 650.820 390.505 220.758 420.631 230.479 670.945 500.480 390.226 550.572 500.774 410.690 360.735 480.614 360.853 660.776 690.597 57
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 490.752 490.734 540.664 710.583 600.815 450.399 700.754 440.639 200.535 500.942 590.470 430.309 190.665 260.539 710.650 490.708 580.635 270.857 650.793 560.642 39
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 500.778 370.731 550.699 580.577 610.829 280.446 500.736 500.477 760.523 560.945 500.454 500.269 400.484 750.749 460.618 620.738 460.599 430.827 710.792 590.621 46
MVPNetpermissive0.641 510.831 220.715 580.671 680.590 560.781 650.394 720.679 640.642 180.553 430.937 670.462 460.256 460.649 290.406 840.626 600.691 660.666 180.877 480.792 590.608 50
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 510.776 390.703 620.721 510.557 680.826 310.451 450.672 660.563 550.483 660.943 580.425 650.162 820.644 310.726 480.659 470.709 570.572 530.875 500.786 640.559 69
PointMRNet0.640 530.717 640.701 640.692 610.576 620.801 550.467 400.716 550.563 550.459 720.953 270.429 610.169 790.581 470.854 210.605 650.710 550.550 640.894 380.793 560.575 62
FPConvpermissive0.639 540.785 340.760 380.713 560.603 510.798 570.392 730.534 860.603 370.524 540.948 420.457 480.250 480.538 620.723 500.598 690.696 640.614 360.872 550.799 490.567 66
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 550.797 310.769 350.641 790.590 560.820 390.461 420.537 850.637 210.536 490.947 440.388 760.206 630.656 270.668 600.647 530.732 500.585 510.868 600.793 560.473 88
PointSPNet0.637 560.734 570.692 710.714 550.576 620.797 580.446 500.743 480.598 400.437 770.942 590.403 720.150 860.626 370.800 370.649 500.697 630.557 610.846 680.777 680.563 67
SConv0.636 570.830 230.697 670.752 470.572 640.780 670.445 520.716 550.529 610.530 510.951 320.446 560.170 780.507 700.666 610.636 580.682 690.541 690.886 430.799 490.594 58
Supervoxel-CNN0.635 580.656 750.711 590.719 520.613 490.757 760.444 550.765 410.534 600.566 400.928 780.478 400.272 370.636 320.531 730.664 440.645 790.508 770.864 620.792 590.611 47
joint point-basedpermissive0.634 590.614 820.778 310.667 700.633 460.825 320.420 640.804 320.467 790.561 410.951 320.494 340.291 260.566 520.458 790.579 760.764 340.559 600.838 690.814 420.598 56
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
PointMTL0.632 600.731 580.688 740.675 650.591 550.784 640.444 550.565 820.610 310.492 640.949 400.456 490.254 470.587 440.706 530.599 680.665 750.612 390.868 600.791 630.579 61
3DSM_DMMF0.631 610.626 790.745 480.801 310.607 500.751 770.506 210.729 530.565 530.491 650.866 940.434 570.197 700.595 420.630 640.709 290.705 600.560 580.875 500.740 790.491 83
PointNet2-SFPN0.631 610.771 410.692 710.672 660.524 730.837 200.440 580.706 600.538 590.446 740.944 560.421 670.219 580.552 580.751 450.591 720.737 470.543 680.901 330.768 710.557 70
APCF-Net0.631 610.742 540.687 760.672 660.557 680.792 620.408 660.665 670.545 570.508 590.952 310.428 620.186 730.634 340.702 540.620 610.706 590.555 620.873 530.798 510.581 60
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
FusionAwareConv0.630 640.604 840.741 520.766 430.590 560.747 780.501 240.734 510.503 680.527 520.919 840.454 500.323 130.550 610.420 830.678 390.688 670.544 660.896 360.795 530.627 45
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 650.800 300.625 860.719 520.545 710.806 510.445 520.597 760.448 830.519 570.938 660.481 380.328 110.489 740.499 780.657 480.759 390.592 470.881 460.797 520.634 42
SegGroup_sempermissive0.627 660.818 260.747 470.701 570.602 520.764 730.385 770.629 730.490 720.508 590.931 770.409 700.201 670.564 530.725 490.618 620.692 650.539 700.873 530.794 540.548 73
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 670.830 230.694 690.757 450.563 660.772 710.448 490.647 700.520 630.509 580.949 400.431 600.191 710.496 720.614 660.647 530.672 730.535 720.876 490.783 650.571 63
HPEIN0.618 680.729 590.668 770.647 760.597 540.766 720.414 650.680 630.520 630.525 530.946 470.432 580.215 600.493 730.599 670.638 570.617 840.570 540.897 350.806 460.605 53
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-GCNpermissive0.610 690.858 160.772 330.489 910.532 720.792 620.404 690.643 720.570 520.507 610.935 700.414 690.046 960.510 680.702 540.602 670.705 600.549 650.859 640.773 700.534 76
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 700.760 460.667 780.649 750.521 740.793 600.457 440.648 690.528 620.434 790.947 440.401 730.153 850.454 770.721 510.648 520.717 540.536 710.904 290.765 720.485 84
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 710.634 780.743 500.697 600.601 530.781 650.437 600.585 790.493 700.446 740.933 750.394 740.011 980.654 280.661 630.603 660.733 490.526 730.832 700.761 740.480 85
dtc_net0.596 720.683 700.725 570.715 540.549 700.803 540.444 550.647 700.493 700.495 630.941 610.409 700.000 1000.424 820.544 700.598 690.703 620.522 740.912 250.792 590.520 79
LAP-D0.594 730.720 620.692 710.637 800.456 830.773 700.391 750.730 520.587 430.445 760.940 640.381 770.288 270.434 800.453 810.591 720.649 770.581 520.777 780.749 780.610 49
DPC0.592 740.720 620.700 650.602 840.480 790.762 750.380 780.713 580.585 460.437 770.940 640.369 790.288 270.434 800.509 770.590 740.639 820.567 570.772 790.755 760.592 59
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 750.766 450.659 810.683 630.470 820.740 800.387 760.620 750.490 720.476 680.922 820.355 820.245 520.511 670.511 760.571 770.643 800.493 810.872 550.762 730.600 55
ROSMRF0.580 760.772 400.707 610.681 640.563 660.764 730.362 800.515 870.465 800.465 710.936 690.427 640.207 620.438 780.577 680.536 800.675 720.486 820.723 850.779 660.524 78
SD-DETR0.576 770.746 510.609 900.445 950.517 750.643 910.366 790.714 570.456 810.468 700.870 930.432 580.264 430.558 560.674 580.586 750.688 670.482 830.739 830.733 810.537 75
SQN_0.1%0.569 780.676 720.696 680.657 720.497 760.779 680.424 620.548 830.515 650.376 840.902 910.422 660.357 40.379 850.456 800.596 710.659 760.544 660.685 880.665 920.556 71
TextureNetpermissive0.566 790.672 740.664 790.671 680.494 770.719 810.445 520.678 650.411 890.396 820.935 700.356 810.225 560.412 830.535 720.565 780.636 830.464 850.794 770.680 890.568 65
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
DVVNet0.562 800.648 760.700 650.770 400.586 590.687 850.333 840.650 680.514 660.475 690.906 880.359 800.223 570.340 870.442 820.422 910.668 740.501 780.708 860.779 660.534 76
Pointnet++ & Featurepermissive0.557 810.735 560.661 800.686 620.491 780.744 790.392 730.539 840.451 820.375 850.946 470.376 780.205 640.403 840.356 870.553 790.643 800.497 790.824 730.756 750.515 80
GMLPs0.538 820.495 920.693 700.647 760.471 810.793 600.300 870.477 880.505 670.358 860.903 900.327 850.081 930.472 760.529 740.448 890.710 550.509 750.746 810.737 800.554 72
PanopticFusion-label0.529 830.491 930.688 740.604 830.386 880.632 920.225 970.705 610.434 860.293 920.815 950.348 830.241 530.499 710.669 590.507 820.649 770.442 910.796 760.602 950.561 68
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_weak0.516 840.676 720.591 930.609 810.442 840.774 690.335 830.597 760.422 880.357 870.932 760.341 840.094 920.298 890.528 750.473 870.676 710.495 800.602 940.721 840.349 95
Online SegFusion0.515 850.607 830.644 840.579 860.434 850.630 930.353 810.628 740.440 840.410 800.762 980.307 870.167 800.520 650.403 850.516 810.565 870.447 890.678 890.701 860.514 81
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, FTSDF0.501 860.558 880.608 910.424 970.478 800.690 840.246 930.586 780.468 780.450 730.911 860.394 740.160 830.438 780.212 940.432 900.541 920.475 840.742 820.727 820.477 86
PCNN0.498 870.559 870.644 840.560 880.420 870.711 830.229 950.414 890.436 850.352 880.941 610.324 860.155 840.238 940.387 860.493 830.529 930.509 750.813 750.751 770.504 82
3DMV0.484 880.484 940.538 950.643 780.424 860.606 960.310 850.574 800.433 870.378 830.796 960.301 880.214 610.537 630.208 950.472 880.507 960.413 940.693 870.602 950.539 74
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 890.577 860.611 890.356 990.321 960.715 820.299 890.376 930.328 960.319 900.944 560.285 900.164 810.216 970.229 920.484 850.545 910.456 870.755 800.709 850.475 87
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 900.679 710.604 920.578 870.380 890.682 860.291 900.106 990.483 750.258 970.920 830.258 940.025 970.231 960.325 880.480 860.560 890.463 860.725 840.666 910.231 99
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 910.474 950.623 870.463 930.366 910.651 890.310 850.389 920.349 940.330 890.937 670.271 920.126 890.285 900.224 930.350 960.577 860.445 900.625 920.723 830.394 91
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
SurfaceConvPF0.442 920.505 910.622 880.380 980.342 940.654 880.227 960.397 910.367 920.276 940.924 800.240 950.198 690.359 860.262 900.366 930.581 850.435 920.640 910.668 900.398 90
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET20.442 920.548 890.548 940.597 850.363 920.628 940.300 870.292 940.374 910.307 910.881 920.268 930.186 730.238 940.204 960.407 920.506 970.449 880.667 900.620 940.462 89
Tangent Convolutionspermissive0.438 940.437 970.646 830.474 920.369 900.645 900.353 810.258 960.282 980.279 930.918 850.298 890.147 880.283 910.294 890.487 840.562 880.427 930.619 930.633 930.352 94
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 950.525 900.647 820.522 890.324 950.488 990.077 1000.712 590.353 930.401 810.636 1000.281 910.176 760.340 870.565 690.175 1000.551 900.398 950.370 1000.602 950.361 93
SPLAT Netcopyleft0.393 960.472 960.511 960.606 820.311 970.656 870.245 940.405 900.328 960.197 980.927 790.227 970.000 1000.001 1010.249 910.271 990.510 940.383 970.593 950.699 870.267 97
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+FTSDF0.383 970.297 990.491 970.432 960.358 930.612 950.274 910.116 980.411 890.265 950.904 890.229 960.079 940.250 920.185 970.320 970.510 940.385 960.548 960.597 980.394 91
PointNet++permissive0.339 980.584 850.478 980.458 940.256 990.360 1000.250 920.247 970.278 990.261 960.677 990.183 980.117 900.212 980.145 990.364 940.346 1000.232 1000.548 960.523 990.252 98
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 990.353 980.290 1000.278 1000.166 1000.553 970.169 990.286 950.147 1000.148 1000.908 870.182 990.064 950.023 1000.018 1010.354 950.363 980.345 980.546 980.685 880.278 96
ScanNetpermissive0.306 1000.203 1000.366 990.501 900.311 970.524 980.211 980.002 1010.342 950.189 990.786 970.145 1000.102 910.245 930.152 980.318 980.348 990.300 990.460 990.437 1000.182 100
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 1010.000 1010.041 1010.172 1010.030 1010.062 1010.001 1010.035 1000.004 1010.051 1010.143 1010.019 1010.003 990.041 990.050 1000.003 1010.054 1010.018 1010.005 1010.264 1010.082 101