Mix3D |  | 0.781 1 | 0.964 1 | 0.855 1 | 0.843 10 | 0.781 1 | 0.858 7 | 0.575 3 | 0.831 17 | 0.685 6 | 0.714 1 | 0.979 1 | 0.594 3 | 0.310 15 | 0.801 1 | 0.892 8 | 0.841 2 | 0.819 3 | 0.723 3 | 0.940 7 | 0.887 1 | 0.725 12 |
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral) |
O-CNN |  | 0.762 4 | 0.924 2 | 0.823 4 | 0.844 9 | 0.770 2 | 0.852 10 | 0.577 2 | 0.847 14 | 0.711 1 | 0.640 14 | 0.958 9 | 0.592 4 | 0.217 55 | 0.762 10 | 0.888 9 | 0.758 8 | 0.813 6 | 0.726 1 | 0.932 13 | 0.868 8 | 0.744 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 |
SAT | | 0.742 15 | 0.860 13 | 0.765 32 | 0.819 16 | 0.769 3 | 0.848 12 | 0.533 10 | 0.829 18 | 0.663 12 | 0.631 15 | 0.955 17 | 0.586 8 | 0.274 31 | 0.753 13 | 0.896 6 | 0.729 14 | 0.760 34 | 0.666 14 | 0.921 16 | 0.855 18 | 0.733 7 |
|
PointTransformer++ | | 0.725 18 | 0.727 57 | 0.811 11 | 0.819 16 | 0.765 4 | 0.841 15 | 0.502 20 | 0.814 25 | 0.621 23 | 0.623 17 | 0.955 17 | 0.556 15 | 0.284 27 | 0.620 35 | 0.866 14 | 0.781 5 | 0.757 37 | 0.648 17 | 0.932 13 | 0.862 12 | 0.709 17 |
|
PointTransformerV2 | | 0.752 5 | 0.742 50 | 0.809 12 | 0.872 1 | 0.758 5 | 0.860 6 | 0.552 6 | 0.891 5 | 0.610 27 | 0.687 2 | 0.960 7 | 0.559 14 | 0.304 18 | 0.766 8 | 0.926 2 | 0.767 6 | 0.797 13 | 0.644 19 | 0.942 5 | 0.876 7 | 0.722 14 |
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022 |
CU-Hybrid Net | | 0.764 2 | 0.924 2 | 0.819 6 | 0.840 11 | 0.757 6 | 0.853 9 | 0.580 1 | 0.848 13 | 0.709 2 | 0.643 11 | 0.958 9 | 0.587 7 | 0.295 21 | 0.753 13 | 0.884 12 | 0.758 8 | 0.815 5 | 0.725 2 | 0.927 15 | 0.867 9 | 0.743 5 |
|
StratifiedFormer |  | 0.747 10 | 0.901 6 | 0.803 14 | 0.845 8 | 0.757 6 | 0.846 13 | 0.512 17 | 0.825 19 | 0.696 4 | 0.645 9 | 0.956 13 | 0.576 9 | 0.262 41 | 0.744 17 | 0.861 16 | 0.742 12 | 0.770 29 | 0.705 5 | 0.899 29 | 0.860 13 | 0.734 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 |
BPNet |  | 0.749 7 | 0.909 4 | 0.818 8 | 0.811 21 | 0.752 8 | 0.839 17 | 0.485 29 | 0.842 15 | 0.673 9 | 0.644 10 | 0.957 12 | 0.528 23 | 0.305 17 | 0.773 6 | 0.859 17 | 0.788 4 | 0.818 4 | 0.693 7 | 0.916 19 | 0.856 16 | 0.723 13 |
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral) |
PointConvFormer | | 0.749 7 | 0.793 29 | 0.790 21 | 0.807 24 | 0.750 9 | 0.856 8 | 0.524 14 | 0.881 6 | 0.588 37 | 0.642 13 | 0.977 4 | 0.591 5 | 0.274 31 | 0.781 2 | 0.929 1 | 0.804 3 | 0.796 14 | 0.642 20 | 0.947 3 | 0.885 2 | 0.715 16 |
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution. |
OccuSeg+Semantic | | 0.764 2 | 0.758 43 | 0.796 17 | 0.839 12 | 0.746 10 | 0.907 1 | 0.562 4 | 0.850 12 | 0.680 8 | 0.672 5 | 0.978 2 | 0.610 1 | 0.335 8 | 0.777 4 | 0.819 30 | 0.847 1 | 0.830 1 | 0.691 8 | 0.972 1 | 0.885 2 | 0.727 10 |
|
MSP | | 0.748 9 | 0.623 74 | 0.804 13 | 0.859 3 | 0.745 11 | 0.824 30 | 0.501 21 | 0.912 2 | 0.690 5 | 0.685 3 | 0.956 13 | 0.567 11 | 0.320 13 | 0.768 7 | 0.918 3 | 0.720 19 | 0.802 9 | 0.676 10 | 0.921 16 | 0.881 4 | 0.779 1 |
|
Retro-FPN | | 0.744 13 | 0.842 18 | 0.800 15 | 0.767 38 | 0.740 12 | 0.836 21 | 0.541 8 | 0.914 1 | 0.672 10 | 0.626 16 | 0.958 9 | 0.552 17 | 0.272 33 | 0.777 4 | 0.886 11 | 0.696 29 | 0.801 10 | 0.674 11 | 0.941 6 | 0.858 14 | 0.717 15 |
|
MatchingNet | | 0.724 20 | 0.812 26 | 0.812 10 | 0.810 22 | 0.735 13 | 0.834 22 | 0.495 26 | 0.860 10 | 0.572 44 | 0.602 27 | 0.954 21 | 0.512 26 | 0.280 28 | 0.757 12 | 0.845 25 | 0.725 16 | 0.780 23 | 0.606 36 | 0.937 9 | 0.851 21 | 0.700 20 |
|
EQ-Net | | 0.743 14 | 0.620 75 | 0.799 16 | 0.849 5 | 0.730 14 | 0.822 32 | 0.493 27 | 0.897 4 | 0.664 11 | 0.681 4 | 0.955 17 | 0.562 13 | 0.378 1 | 0.760 11 | 0.903 4 | 0.738 13 | 0.801 10 | 0.673 12 | 0.907 22 | 0.877 5 | 0.745 3 |
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022 |
VMNet |  | 0.746 11 | 0.870 11 | 0.838 2 | 0.858 4 | 0.729 15 | 0.850 11 | 0.501 21 | 0.874 7 | 0.587 38 | 0.658 7 | 0.956 13 | 0.564 12 | 0.299 19 | 0.765 9 | 0.900 5 | 0.716 22 | 0.812 7 | 0.631 25 | 0.939 8 | 0.858 14 | 0.709 17 |
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) |
IPCA | | 0.731 17 | 0.890 7 | 0.837 3 | 0.864 2 | 0.726 16 | 0.873 2 | 0.530 13 | 0.824 20 | 0.489 68 | 0.647 8 | 0.978 2 | 0.609 2 | 0.336 7 | 0.624 34 | 0.733 43 | 0.758 8 | 0.776 25 | 0.570 50 | 0.949 2 | 0.877 5 | 0.728 8 |
|
SimConv | | 0.410 90 | 0.000 95 | 0.782 25 | 0.772 35 | 0.722 17 | 0.838 18 | 0.407 62 | 0.000 96 | 0.000 96 | 0.595 31 | 0.947 39 | 0.000 96 | 0.270 36 | 0.000 96 | 0.000 96 | 0.000 96 | 0.786 21 | 0.621 30 | 0.000 96 | 0.841 27 | 0.621 41 |
|
SparseConvNet | | 0.725 18 | 0.647 71 | 0.821 5 | 0.846 7 | 0.721 18 | 0.869 3 | 0.533 10 | 0.754 39 | 0.603 33 | 0.614 20 | 0.955 17 | 0.572 10 | 0.325 11 | 0.710 19 | 0.870 13 | 0.724 17 | 0.823 2 | 0.628 26 | 0.934 11 | 0.865 11 | 0.683 23 |
|
One Thing One Click | | 0.701 25 | 0.825 23 | 0.796 17 | 0.723 45 | 0.716 19 | 0.832 23 | 0.433 55 | 0.816 21 | 0.634 19 | 0.609 22 | 0.969 6 | 0.418 63 | 0.344 5 | 0.559 50 | 0.833 27 | 0.715 23 | 0.808 8 | 0.560 54 | 0.902 26 | 0.847 23 | 0.680 24 |
|
PicassoNet-II |  | 0.696 27 | 0.704 61 | 0.790 21 | 0.787 30 | 0.709 20 | 0.837 19 | 0.459 38 | 0.815 23 | 0.543 53 | 0.615 19 | 0.956 13 | 0.529 21 | 0.250 44 | 0.551 55 | 0.790 35 | 0.703 27 | 0.799 12 | 0.619 31 | 0.908 21 | 0.848 22 | 0.700 20 |
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes. |
MinkowskiNet |  | 0.736 16 | 0.859 14 | 0.818 8 | 0.832 13 | 0.709 20 | 0.840 16 | 0.521 16 | 0.853 11 | 0.660 14 | 0.643 11 | 0.951 28 | 0.544 18 | 0.286 26 | 0.731 18 | 0.893 7 | 0.675 36 | 0.772 27 | 0.683 9 | 0.874 47 | 0.852 20 | 0.727 10 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |
INS-Conv-semantic | | 0.717 21 | 0.751 46 | 0.759 35 | 0.812 20 | 0.704 22 | 0.868 4 | 0.537 9 | 0.842 15 | 0.609 29 | 0.608 23 | 0.953 23 | 0.534 19 | 0.293 22 | 0.616 36 | 0.864 15 | 0.719 21 | 0.793 18 | 0.640 21 | 0.933 12 | 0.845 25 | 0.663 28 |
|
Virtual MVFusion | | 0.746 11 | 0.771 37 | 0.819 6 | 0.848 6 | 0.702 23 | 0.865 5 | 0.397 66 | 0.899 3 | 0.699 3 | 0.664 6 | 0.948 37 | 0.588 6 | 0.330 9 | 0.746 16 | 0.851 23 | 0.764 7 | 0.796 14 | 0.704 6 | 0.935 10 | 0.866 10 | 0.728 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 |
VACNN++ | | 0.684 32 | 0.728 56 | 0.757 38 | 0.776 34 | 0.690 24 | 0.804 49 | 0.464 36 | 0.816 21 | 0.577 43 | 0.587 34 | 0.945 46 | 0.508 28 | 0.276 30 | 0.671 21 | 0.710 48 | 0.663 41 | 0.750 40 | 0.589 45 | 0.881 41 | 0.832 29 | 0.653 31 |
|
DMF-Net | | 0.752 5 | 0.906 5 | 0.793 20 | 0.802 26 | 0.689 25 | 0.825 28 | 0.556 5 | 0.867 8 | 0.681 7 | 0.602 27 | 0.960 7 | 0.555 16 | 0.365 3 | 0.779 3 | 0.859 17 | 0.747 11 | 0.795 17 | 0.717 4 | 0.917 18 | 0.856 16 | 0.764 2 |
|
contrastBoundary |  | 0.705 23 | 0.769 40 | 0.775 28 | 0.809 23 | 0.687 26 | 0.820 35 | 0.439 53 | 0.812 26 | 0.661 13 | 0.591 33 | 0.945 46 | 0.515 25 | 0.171 73 | 0.633 31 | 0.856 19 | 0.720 19 | 0.796 14 | 0.668 13 | 0.889 36 | 0.847 23 | 0.689 22 |
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022 |
One-Thing-One-Click | | 0.693 28 | 0.743 49 | 0.794 19 | 0.655 68 | 0.684 27 | 0.822 32 | 0.497 25 | 0.719 49 | 0.622 22 | 0.617 18 | 0.977 4 | 0.447 50 | 0.339 6 | 0.750 15 | 0.664 58 | 0.703 27 | 0.790 20 | 0.596 40 | 0.946 4 | 0.855 18 | 0.647 33 |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 |
PointMetaBase | | 0.714 22 | 0.835 19 | 0.785 23 | 0.821 14 | 0.684 27 | 0.846 13 | 0.531 12 | 0.865 9 | 0.614 24 | 0.596 30 | 0.953 23 | 0.500 29 | 0.246 47 | 0.674 20 | 0.888 9 | 0.692 30 | 0.764 31 | 0.624 27 | 0.849 61 | 0.844 26 | 0.675 25 |
|
RFCR | | 0.702 24 | 0.889 8 | 0.745 44 | 0.813 19 | 0.672 29 | 0.818 39 | 0.493 27 | 0.815 23 | 0.623 21 | 0.610 21 | 0.947 39 | 0.470 38 | 0.249 46 | 0.594 39 | 0.848 24 | 0.705 26 | 0.779 24 | 0.646 18 | 0.892 34 | 0.823 33 | 0.611 43 |
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 |
JSENet |  | 0.699 26 | 0.881 10 | 0.762 33 | 0.821 14 | 0.667 30 | 0.800 51 | 0.522 15 | 0.792 31 | 0.613 25 | 0.607 24 | 0.935 65 | 0.492 31 | 0.205 60 | 0.576 44 | 0.853 21 | 0.691 31 | 0.758 36 | 0.652 16 | 0.872 50 | 0.828 30 | 0.649 32 |
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 |
FusionNet | | 0.688 30 | 0.704 61 | 0.741 48 | 0.754 42 | 0.656 31 | 0.829 25 | 0.501 21 | 0.741 44 | 0.609 29 | 0.548 40 | 0.950 32 | 0.522 24 | 0.371 2 | 0.633 31 | 0.756 38 | 0.715 23 | 0.771 28 | 0.623 28 | 0.861 57 | 0.814 38 | 0.658 29 |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 |
PointASNL |  | 0.666 39 | 0.703 63 | 0.781 26 | 0.751 44 | 0.655 32 | 0.830 24 | 0.471 32 | 0.769 35 | 0.474 71 | 0.537 44 | 0.951 28 | 0.475 36 | 0.279 29 | 0.635 29 | 0.698 52 | 0.675 36 | 0.751 39 | 0.553 59 | 0.816 68 | 0.806 42 | 0.703 19 |
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020 |
Superpoint Network | | 0.683 34 | 0.851 16 | 0.728 52 | 0.800 28 | 0.653 33 | 0.806 47 | 0.468 33 | 0.804 27 | 0.572 44 | 0.602 27 | 0.946 43 | 0.453 47 | 0.239 50 | 0.519 61 | 0.822 28 | 0.689 34 | 0.762 33 | 0.595 42 | 0.895 32 | 0.827 31 | 0.630 39 |
|
SALANet | | 0.670 38 | 0.816 25 | 0.770 30 | 0.768 37 | 0.652 34 | 0.807 46 | 0.451 40 | 0.747 41 | 0.659 15 | 0.545 41 | 0.924 75 | 0.473 37 | 0.149 83 | 0.571 46 | 0.811 32 | 0.635 54 | 0.746 41 | 0.623 28 | 0.892 34 | 0.794 50 | 0.570 60 |
|
KP-FCNN | | 0.684 32 | 0.847 17 | 0.758 37 | 0.784 32 | 0.647 35 | 0.814 42 | 0.473 31 | 0.772 34 | 0.605 31 | 0.594 32 | 0.935 65 | 0.450 48 | 0.181 71 | 0.587 40 | 0.805 33 | 0.690 32 | 0.785 22 | 0.614 32 | 0.882 40 | 0.819 37 | 0.632 38 |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 |
Feature_GeometricNet |  | 0.690 29 | 0.884 9 | 0.754 39 | 0.795 29 | 0.647 35 | 0.818 39 | 0.422 57 | 0.802 29 | 0.612 26 | 0.604 25 | 0.945 46 | 0.462 41 | 0.189 68 | 0.563 49 | 0.853 21 | 0.726 15 | 0.765 30 | 0.632 24 | 0.904 24 | 0.821 36 | 0.606 47 |
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint |
Feature-Geometry Net |  | 0.685 31 | 0.866 12 | 0.748 41 | 0.819 16 | 0.645 37 | 0.794 54 | 0.450 43 | 0.802 29 | 0.587 38 | 0.604 25 | 0.945 46 | 0.464 40 | 0.201 63 | 0.554 52 | 0.840 26 | 0.723 18 | 0.732 46 | 0.602 38 | 0.907 22 | 0.822 35 | 0.603 50 |
|
PointConv |  | 0.666 39 | 0.781 32 | 0.759 35 | 0.699 53 | 0.644 38 | 0.822 32 | 0.475 30 | 0.779 32 | 0.564 49 | 0.504 58 | 0.953 23 | 0.428 57 | 0.203 62 | 0.586 42 | 0.754 39 | 0.661 42 | 0.753 38 | 0.588 46 | 0.902 26 | 0.813 40 | 0.642 34 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 |
PointContrast_LA_SEM | | 0.683 34 | 0.757 44 | 0.784 24 | 0.786 31 | 0.639 39 | 0.824 30 | 0.408 60 | 0.775 33 | 0.604 32 | 0.541 42 | 0.934 69 | 0.532 20 | 0.269 37 | 0.552 53 | 0.777 36 | 0.645 51 | 0.793 18 | 0.640 21 | 0.913 20 | 0.824 32 | 0.671 26 |
|
PPCNN++ |  | 0.663 41 | 0.746 47 | 0.708 55 | 0.722 46 | 0.638 40 | 0.820 35 | 0.451 40 | 0.566 75 | 0.599 35 | 0.541 42 | 0.950 32 | 0.510 27 | 0.313 14 | 0.648 26 | 0.819 30 | 0.616 59 | 0.682 64 | 0.590 44 | 0.869 53 | 0.810 41 | 0.656 30 |
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access |
ROSMRF3D | | 0.673 37 | 0.789 30 | 0.748 41 | 0.763 40 | 0.635 41 | 0.814 42 | 0.407 62 | 0.747 41 | 0.581 42 | 0.573 35 | 0.950 32 | 0.484 32 | 0.271 35 | 0.607 37 | 0.754 39 | 0.649 46 | 0.774 26 | 0.596 40 | 0.883 39 | 0.823 33 | 0.606 47 |
|
joint point-based |  | 0.634 54 | 0.614 76 | 0.778 27 | 0.667 65 | 0.633 42 | 0.825 28 | 0.420 58 | 0.804 27 | 0.467 73 | 0.561 37 | 0.951 28 | 0.494 30 | 0.291 23 | 0.566 47 | 0.458 73 | 0.579 70 | 0.764 31 | 0.559 56 | 0.838 63 | 0.814 38 | 0.598 52 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 |
VI-PointConv | | 0.676 36 | 0.770 39 | 0.754 39 | 0.783 33 | 0.621 43 | 0.814 42 | 0.552 6 | 0.758 37 | 0.571 46 | 0.557 38 | 0.954 21 | 0.529 21 | 0.268 39 | 0.530 59 | 0.682 53 | 0.675 36 | 0.719 49 | 0.603 37 | 0.888 37 | 0.833 28 | 0.665 27 |
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions. |
DCM-Net | | 0.658 42 | 0.778 33 | 0.702 58 | 0.806 25 | 0.619 44 | 0.813 45 | 0.468 33 | 0.693 57 | 0.494 64 | 0.524 50 | 0.941 57 | 0.449 49 | 0.298 20 | 0.510 63 | 0.821 29 | 0.675 36 | 0.727 48 | 0.568 52 | 0.826 66 | 0.803 44 | 0.637 36 |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] |
Supervoxel-CNN | | 0.635 53 | 0.656 69 | 0.711 54 | 0.719 48 | 0.613 45 | 0.757 71 | 0.444 50 | 0.765 36 | 0.534 55 | 0.566 36 | 0.928 73 | 0.478 35 | 0.272 33 | 0.636 28 | 0.531 67 | 0.664 40 | 0.645 74 | 0.508 72 | 0.864 56 | 0.792 55 | 0.611 43 |
|
3DSM_DMMF | | 0.631 56 | 0.626 73 | 0.745 44 | 0.801 27 | 0.607 46 | 0.751 72 | 0.506 18 | 0.729 48 | 0.565 48 | 0.491 60 | 0.866 89 | 0.434 52 | 0.197 66 | 0.595 38 | 0.630 60 | 0.709 25 | 0.705 56 | 0.560 54 | 0.875 45 | 0.740 74 | 0.491 78 |
|
FPConv |  | 0.639 49 | 0.785 31 | 0.760 34 | 0.713 51 | 0.603 47 | 0.798 52 | 0.392 68 | 0.534 80 | 0.603 33 | 0.524 50 | 0.948 37 | 0.457 43 | 0.250 44 | 0.538 57 | 0.723 46 | 0.598 64 | 0.696 59 | 0.614 32 | 0.872 50 | 0.799 45 | 0.567 62 |
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020 |
SegGroup_sem |  | 0.627 61 | 0.818 24 | 0.747 43 | 0.701 52 | 0.602 48 | 0.764 68 | 0.385 72 | 0.629 67 | 0.490 66 | 0.508 55 | 0.931 72 | 0.409 65 | 0.201 63 | 0.564 48 | 0.725 45 | 0.618 57 | 0.692 60 | 0.539 66 | 0.873 48 | 0.794 50 | 0.548 69 |
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022 |
wsss-transformer | | 0.600 66 | 0.634 72 | 0.743 46 | 0.697 55 | 0.601 49 | 0.781 60 | 0.437 54 | 0.585 73 | 0.493 65 | 0.446 69 | 0.933 70 | 0.394 68 | 0.011 94 | 0.654 24 | 0.661 59 | 0.603 61 | 0.733 45 | 0.526 69 | 0.832 64 | 0.761 69 | 0.480 80 |
|
HPEIN | | 0.618 63 | 0.729 55 | 0.668 72 | 0.647 71 | 0.597 50 | 0.766 67 | 0.414 59 | 0.680 58 | 0.520 58 | 0.525 49 | 0.946 43 | 0.432 53 | 0.215 56 | 0.493 68 | 0.599 62 | 0.638 52 | 0.617 79 | 0.570 50 | 0.897 30 | 0.806 42 | 0.605 49 |
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 |
PointMTL | | 0.632 55 | 0.731 54 | 0.688 69 | 0.675 60 | 0.591 51 | 0.784 59 | 0.444 50 | 0.565 76 | 0.610 27 | 0.492 59 | 0.949 35 | 0.456 44 | 0.254 43 | 0.587 40 | 0.706 49 | 0.599 63 | 0.665 70 | 0.612 35 | 0.868 54 | 0.791 58 | 0.579 57 |
|
FusionAwareConv | | 0.630 59 | 0.604 78 | 0.741 48 | 0.766 39 | 0.590 52 | 0.747 73 | 0.501 21 | 0.734 46 | 0.503 63 | 0.527 48 | 0.919 79 | 0.454 45 | 0.323 12 | 0.550 56 | 0.420 77 | 0.678 35 | 0.688 62 | 0.544 62 | 0.896 31 | 0.795 49 | 0.627 40 |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 |
PD-Net | | 0.638 50 | 0.797 28 | 0.769 31 | 0.641 74 | 0.590 52 | 0.820 35 | 0.461 37 | 0.537 79 | 0.637 18 | 0.536 45 | 0.947 39 | 0.388 70 | 0.206 59 | 0.656 23 | 0.668 56 | 0.647 49 | 0.732 46 | 0.585 47 | 0.868 54 | 0.793 52 | 0.473 83 |
|
MVPNet |  | 0.641 46 | 0.831 20 | 0.715 53 | 0.671 63 | 0.590 52 | 0.781 60 | 0.394 67 | 0.679 59 | 0.642 16 | 0.553 39 | 0.937 62 | 0.462 41 | 0.256 42 | 0.649 25 | 0.406 78 | 0.626 55 | 0.691 61 | 0.666 14 | 0.877 43 | 0.792 55 | 0.608 46 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 |
DVVNet | | 0.562 74 | 0.648 70 | 0.700 60 | 0.770 36 | 0.586 55 | 0.687 80 | 0.333 79 | 0.650 63 | 0.514 61 | 0.475 64 | 0.906 83 | 0.359 74 | 0.223 53 | 0.340 81 | 0.442 76 | 0.422 85 | 0.668 69 | 0.501 73 | 0.708 80 | 0.779 61 | 0.534 72 |
|
SAFNet-seg |  | 0.654 44 | 0.752 45 | 0.734 50 | 0.664 66 | 0.583 56 | 0.815 41 | 0.399 65 | 0.754 39 | 0.639 17 | 0.535 46 | 0.942 55 | 0.470 38 | 0.309 16 | 0.665 22 | 0.539 65 | 0.650 45 | 0.708 54 | 0.635 23 | 0.857 59 | 0.793 52 | 0.642 34 |
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021 |
RandLA-Net |  | 0.645 45 | 0.778 33 | 0.731 51 | 0.699 53 | 0.577 57 | 0.829 25 | 0.446 45 | 0.736 45 | 0.477 70 | 0.523 52 | 0.945 46 | 0.454 45 | 0.269 37 | 0.484 70 | 0.749 42 | 0.618 57 | 0.738 42 | 0.599 39 | 0.827 65 | 0.792 55 | 0.621 41 |
|
PointSPNet | | 0.637 51 | 0.734 53 | 0.692 66 | 0.714 50 | 0.576 58 | 0.797 53 | 0.446 45 | 0.743 43 | 0.598 36 | 0.437 72 | 0.942 55 | 0.403 66 | 0.150 82 | 0.626 33 | 0.800 34 | 0.649 46 | 0.697 58 | 0.557 57 | 0.846 62 | 0.777 63 | 0.563 63 |
|
PointMRNet | | 0.640 48 | 0.717 60 | 0.701 59 | 0.692 56 | 0.576 58 | 0.801 50 | 0.467 35 | 0.716 50 | 0.563 50 | 0.459 67 | 0.953 23 | 0.429 56 | 0.169 75 | 0.581 43 | 0.854 20 | 0.605 60 | 0.710 51 | 0.550 60 | 0.894 33 | 0.793 52 | 0.575 58 |
|
SConv | | 0.636 52 | 0.830 21 | 0.697 62 | 0.752 43 | 0.572 60 | 0.780 62 | 0.445 47 | 0.716 50 | 0.529 56 | 0.530 47 | 0.951 28 | 0.446 51 | 0.170 74 | 0.507 65 | 0.666 57 | 0.636 53 | 0.682 64 | 0.541 65 | 0.886 38 | 0.799 45 | 0.594 54 |
|
HPGCNN | | 0.656 43 | 0.698 64 | 0.743 46 | 0.650 69 | 0.564 61 | 0.820 35 | 0.505 19 | 0.758 37 | 0.631 20 | 0.479 62 | 0.945 46 | 0.480 34 | 0.226 51 | 0.572 45 | 0.774 37 | 0.690 32 | 0.735 44 | 0.614 32 | 0.853 60 | 0.776 64 | 0.597 53 |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. |
ROSMRF | | 0.580 70 | 0.772 36 | 0.707 56 | 0.681 59 | 0.563 62 | 0.764 68 | 0.362 75 | 0.515 81 | 0.465 74 | 0.465 66 | 0.936 64 | 0.427 59 | 0.207 58 | 0.438 73 | 0.577 63 | 0.536 74 | 0.675 67 | 0.486 77 | 0.723 79 | 0.779 61 | 0.524 74 |
|
SIConv | | 0.625 62 | 0.830 21 | 0.694 64 | 0.757 41 | 0.563 62 | 0.772 66 | 0.448 44 | 0.647 65 | 0.520 58 | 0.509 54 | 0.949 35 | 0.431 55 | 0.191 67 | 0.496 67 | 0.614 61 | 0.647 49 | 0.672 68 | 0.535 68 | 0.876 44 | 0.783 60 | 0.571 59 |
|
PointConv-SFPN | | 0.641 46 | 0.776 35 | 0.703 57 | 0.721 47 | 0.557 64 | 0.826 27 | 0.451 40 | 0.672 61 | 0.563 50 | 0.483 61 | 0.943 54 | 0.425 60 | 0.162 78 | 0.644 27 | 0.726 44 | 0.659 43 | 0.709 53 | 0.572 49 | 0.875 45 | 0.786 59 | 0.559 65 |
|
APCF-Net | | 0.631 56 | 0.742 50 | 0.687 71 | 0.672 61 | 0.557 64 | 0.792 57 | 0.408 60 | 0.665 62 | 0.545 52 | 0.508 55 | 0.952 27 | 0.428 57 | 0.186 69 | 0.634 30 | 0.702 50 | 0.620 56 | 0.706 55 | 0.555 58 | 0.873 48 | 0.798 47 | 0.581 56 |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL |
DenSeR | | 0.628 60 | 0.800 27 | 0.625 81 | 0.719 48 | 0.545 66 | 0.806 47 | 0.445 47 | 0.597 70 | 0.448 77 | 0.519 53 | 0.938 61 | 0.481 33 | 0.328 10 | 0.489 69 | 0.499 72 | 0.657 44 | 0.759 35 | 0.592 43 | 0.881 41 | 0.797 48 | 0.634 37 |
|
SPH3D-GCN |  | 0.610 64 | 0.858 15 | 0.772 29 | 0.489 86 | 0.532 67 | 0.792 57 | 0.404 64 | 0.643 66 | 0.570 47 | 0.507 57 | 0.935 65 | 0.414 64 | 0.046 92 | 0.510 63 | 0.702 50 | 0.602 62 | 0.705 56 | 0.549 61 | 0.859 58 | 0.773 65 | 0.534 72 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 |
PointNet2-SFPN | | 0.631 56 | 0.771 37 | 0.692 66 | 0.672 61 | 0.524 68 | 0.837 19 | 0.440 52 | 0.706 55 | 0.538 54 | 0.446 69 | 0.944 52 | 0.421 62 | 0.219 54 | 0.552 53 | 0.751 41 | 0.591 66 | 0.737 43 | 0.543 64 | 0.901 28 | 0.768 66 | 0.557 66 |
|
AttAN | | 0.609 65 | 0.760 42 | 0.667 73 | 0.649 70 | 0.521 69 | 0.793 55 | 0.457 39 | 0.648 64 | 0.528 57 | 0.434 74 | 0.947 39 | 0.401 67 | 0.153 81 | 0.454 72 | 0.721 47 | 0.648 48 | 0.717 50 | 0.536 67 | 0.904 24 | 0.765 67 | 0.485 79 |
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020 |
SD-DETR | | 0.576 71 | 0.746 47 | 0.609 85 | 0.445 90 | 0.517 70 | 0.643 86 | 0.366 74 | 0.714 52 | 0.456 75 | 0.468 65 | 0.870 88 | 0.432 53 | 0.264 40 | 0.558 51 | 0.674 54 | 0.586 69 | 0.688 62 | 0.482 78 | 0.739 77 | 0.733 76 | 0.537 71 |
|
SQN_0.1% | | 0.569 72 | 0.676 66 | 0.696 63 | 0.657 67 | 0.497 71 | 0.779 63 | 0.424 56 | 0.548 77 | 0.515 60 | 0.376 79 | 0.902 86 | 0.422 61 | 0.357 4 | 0.379 79 | 0.456 74 | 0.596 65 | 0.659 71 | 0.544 62 | 0.685 82 | 0.665 87 | 0.556 67 |
|
TextureNet |  | 0.566 73 | 0.672 68 | 0.664 74 | 0.671 63 | 0.494 72 | 0.719 76 | 0.445 47 | 0.678 60 | 0.411 83 | 0.396 77 | 0.935 65 | 0.356 75 | 0.225 52 | 0.412 77 | 0.535 66 | 0.565 72 | 0.636 78 | 0.464 80 | 0.794 71 | 0.680 84 | 0.568 61 |
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++ & Feature |  | 0.557 75 | 0.735 52 | 0.661 75 | 0.686 57 | 0.491 73 | 0.744 74 | 0.392 68 | 0.539 78 | 0.451 76 | 0.375 80 | 0.946 43 | 0.376 72 | 0.205 60 | 0.403 78 | 0.356 81 | 0.553 73 | 0.643 75 | 0.497 74 | 0.824 67 | 0.756 70 | 0.515 75 |
|
DPC | | 0.592 68 | 0.720 58 | 0.700 60 | 0.602 79 | 0.480 74 | 0.762 70 | 0.380 73 | 0.713 53 | 0.585 41 | 0.437 72 | 0.940 59 | 0.369 73 | 0.288 24 | 0.434 75 | 0.509 71 | 0.590 68 | 0.639 77 | 0.567 53 | 0.772 73 | 0.755 71 | 0.592 55 |
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020 |
3DMV, FTSDF | | 0.501 80 | 0.558 82 | 0.608 86 | 0.424 92 | 0.478 75 | 0.690 79 | 0.246 88 | 0.586 72 | 0.468 72 | 0.450 68 | 0.911 81 | 0.394 68 | 0.160 79 | 0.438 73 | 0.212 88 | 0.432 84 | 0.541 87 | 0.475 79 | 0.742 76 | 0.727 77 | 0.477 81 |
|
GMLPs | | 0.538 76 | 0.495 86 | 0.693 65 | 0.647 71 | 0.471 76 | 0.793 55 | 0.300 82 | 0.477 82 | 0.505 62 | 0.358 81 | 0.903 85 | 0.327 79 | 0.081 89 | 0.472 71 | 0.529 68 | 0.448 83 | 0.710 51 | 0.509 70 | 0.746 75 | 0.737 75 | 0.554 68 |
|
CCRFNet | | 0.589 69 | 0.766 41 | 0.659 76 | 0.683 58 | 0.470 77 | 0.740 75 | 0.387 71 | 0.620 69 | 0.490 66 | 0.476 63 | 0.922 77 | 0.355 76 | 0.245 48 | 0.511 62 | 0.511 70 | 0.571 71 | 0.643 75 | 0.493 76 | 0.872 50 | 0.762 68 | 0.600 51 |
|
LAP-D | | 0.594 67 | 0.720 58 | 0.692 66 | 0.637 75 | 0.456 78 | 0.773 65 | 0.391 70 | 0.730 47 | 0.587 38 | 0.445 71 | 0.940 59 | 0.381 71 | 0.288 24 | 0.434 75 | 0.453 75 | 0.591 66 | 0.649 72 | 0.581 48 | 0.777 72 | 0.749 73 | 0.610 45 |
|
subcloud_weak | | 0.516 78 | 0.676 66 | 0.591 88 | 0.609 76 | 0.442 79 | 0.774 64 | 0.335 78 | 0.597 70 | 0.422 82 | 0.357 82 | 0.932 71 | 0.341 78 | 0.094 88 | 0.298 83 | 0.528 69 | 0.473 81 | 0.676 66 | 0.495 75 | 0.602 88 | 0.721 79 | 0.349 90 |
|
Online SegFusion | | 0.515 79 | 0.607 77 | 0.644 79 | 0.579 81 | 0.434 80 | 0.630 88 | 0.353 76 | 0.628 68 | 0.440 78 | 0.410 75 | 0.762 93 | 0.307 81 | 0.167 76 | 0.520 60 | 0.403 79 | 0.516 75 | 0.565 82 | 0.447 84 | 0.678 83 | 0.701 81 | 0.514 76 |
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 | | 0.484 82 | 0.484 88 | 0.538 90 | 0.643 73 | 0.424 81 | 0.606 91 | 0.310 80 | 0.574 74 | 0.433 81 | 0.378 78 | 0.796 91 | 0.301 82 | 0.214 57 | 0.537 58 | 0.208 89 | 0.472 82 | 0.507 91 | 0.413 89 | 0.693 81 | 0.602 90 | 0.539 70 |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 |
PCNN | | 0.498 81 | 0.559 81 | 0.644 79 | 0.560 83 | 0.420 82 | 0.711 78 | 0.229 90 | 0.414 83 | 0.436 79 | 0.352 83 | 0.941 57 | 0.324 80 | 0.155 80 | 0.238 88 | 0.387 80 | 0.493 77 | 0.529 88 | 0.509 70 | 0.813 69 | 0.751 72 | 0.504 77 |
|
PanopticFusion-label | | 0.529 77 | 0.491 87 | 0.688 69 | 0.604 78 | 0.386 83 | 0.632 87 | 0.225 92 | 0.705 56 | 0.434 80 | 0.293 87 | 0.815 90 | 0.348 77 | 0.241 49 | 0.499 66 | 0.669 55 | 0.507 76 | 0.649 72 | 0.442 86 | 0.796 70 | 0.602 90 | 0.561 64 |
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear) |
FCPN |  | 0.447 84 | 0.679 65 | 0.604 87 | 0.578 82 | 0.380 84 | 0.682 81 | 0.291 85 | 0.106 93 | 0.483 69 | 0.258 92 | 0.920 78 | 0.258 88 | 0.025 93 | 0.231 90 | 0.325 82 | 0.480 80 | 0.560 84 | 0.463 81 | 0.725 78 | 0.666 86 | 0.231 94 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 |
Tangent Convolutions |  | 0.438 88 | 0.437 91 | 0.646 78 | 0.474 87 | 0.369 85 | 0.645 85 | 0.353 76 | 0.258 90 | 0.282 92 | 0.279 88 | 0.918 80 | 0.298 83 | 0.147 84 | 0.283 85 | 0.294 83 | 0.487 78 | 0.562 83 | 0.427 88 | 0.619 87 | 0.633 88 | 0.352 89 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 |
DGCNN_reproduce |  | 0.446 85 | 0.474 89 | 0.623 82 | 0.463 88 | 0.366 86 | 0.651 84 | 0.310 80 | 0.389 86 | 0.349 88 | 0.330 84 | 0.937 62 | 0.271 86 | 0.126 85 | 0.285 84 | 0.224 87 | 0.350 90 | 0.577 81 | 0.445 85 | 0.625 86 | 0.723 78 | 0.394 86 |
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 |
PNET2 | | 0.442 86 | 0.548 83 | 0.548 89 | 0.597 80 | 0.363 87 | 0.628 89 | 0.300 82 | 0.292 88 | 0.374 85 | 0.307 86 | 0.881 87 | 0.268 87 | 0.186 69 | 0.238 88 | 0.204 90 | 0.407 86 | 0.506 92 | 0.449 83 | 0.667 84 | 0.620 89 | 0.462 84 |
|
ScanNet+FTSDF | | 0.383 92 | 0.297 93 | 0.491 92 | 0.432 91 | 0.358 88 | 0.612 90 | 0.274 86 | 0.116 92 | 0.411 83 | 0.265 90 | 0.904 84 | 0.229 90 | 0.079 90 | 0.250 86 | 0.185 91 | 0.320 91 | 0.510 89 | 0.385 91 | 0.548 90 | 0.597 93 | 0.394 86 |
|
SurfaceConvPF | | 0.442 86 | 0.505 85 | 0.622 83 | 0.380 93 | 0.342 89 | 0.654 83 | 0.227 91 | 0.397 85 | 0.367 86 | 0.276 89 | 0.924 75 | 0.240 89 | 0.198 65 | 0.359 80 | 0.262 84 | 0.366 87 | 0.581 80 | 0.435 87 | 0.640 85 | 0.668 85 | 0.398 85 |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. |
3DWSSS | | 0.425 89 | 0.525 84 | 0.647 77 | 0.522 84 | 0.324 90 | 0.488 94 | 0.077 95 | 0.712 54 | 0.353 87 | 0.401 76 | 0.636 95 | 0.281 85 | 0.176 72 | 0.340 81 | 0.565 64 | 0.175 94 | 0.551 85 | 0.398 90 | 0.370 94 | 0.602 90 | 0.361 88 |
|
PointCNN with RGB |  | 0.458 83 | 0.577 80 | 0.611 84 | 0.356 94 | 0.321 91 | 0.715 77 | 0.299 84 | 0.376 87 | 0.328 90 | 0.319 85 | 0.944 52 | 0.285 84 | 0.164 77 | 0.216 91 | 0.229 86 | 0.484 79 | 0.545 86 | 0.456 82 | 0.755 74 | 0.709 80 | 0.475 82 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 |
ScanNet |  | 0.306 95 | 0.203 94 | 0.366 94 | 0.501 85 | 0.311 92 | 0.524 93 | 0.211 93 | 0.002 95 | 0.342 89 | 0.189 94 | 0.786 92 | 0.145 94 | 0.102 87 | 0.245 87 | 0.152 92 | 0.318 92 | 0.348 94 | 0.300 94 | 0.460 93 | 0.437 95 | 0.182 95 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 |
SPLAT Net |  | 0.393 91 | 0.472 90 | 0.511 91 | 0.606 77 | 0.311 92 | 0.656 82 | 0.245 89 | 0.405 84 | 0.328 90 | 0.197 93 | 0.927 74 | 0.227 91 | 0.000 96 | 0.001 95 | 0.249 85 | 0.271 93 | 0.510 89 | 0.383 92 | 0.593 89 | 0.699 82 | 0.267 92 |
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 |
PointNet++ |  | 0.339 93 | 0.584 79 | 0.478 93 | 0.458 89 | 0.256 94 | 0.360 95 | 0.250 87 | 0.247 91 | 0.278 93 | 0.261 91 | 0.677 94 | 0.183 92 | 0.117 86 | 0.212 92 | 0.145 93 | 0.364 88 | 0.346 95 | 0.232 95 | 0.548 90 | 0.523 94 | 0.252 93 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. |
SSC-UNet |  | 0.308 94 | 0.353 92 | 0.290 95 | 0.278 95 | 0.166 95 | 0.553 92 | 0.169 94 | 0.286 89 | 0.147 94 | 0.148 95 | 0.908 82 | 0.182 93 | 0.064 91 | 0.023 94 | 0.018 95 | 0.354 89 | 0.363 93 | 0.345 93 | 0.546 92 | 0.685 83 | 0.278 91 |
|
ERROR | | 0.054 96 | 0.000 95 | 0.041 96 | 0.172 96 | 0.030 96 | 0.062 96 | 0.001 96 | 0.035 94 | 0.004 95 | 0.051 96 | 0.143 96 | 0.019 95 | 0.003 95 | 0.041 93 | 0.050 94 | 0.003 95 | 0.054 96 | 0.018 96 | 0.005 95 | 0.264 96 | 0.082 96 |
|