Mix3D |  | 0.781 1 | 0.964 1 | 0.855 1 | 0.843 10 | 0.781 1 | 0.858 7 | 0.575 3 | 0.831 18 | 0.685 7 | 0.714 1 | 0.979 1 | 0.594 3 | 0.310 16 | 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) |
PointConvFormer | | 0.749 7 | 0.793 31 | 0.790 23 | 0.807 24 | 0.750 10 | 0.856 8 | 0.524 16 | 0.881 6 | 0.588 39 | 0.642 14 | 0.977 4 | 0.591 5 | 0.274 32 | 0.781 2 | 0.929 1 | 0.804 3 | 0.796 14 | 0.642 22 | 0.947 3 | 0.885 2 | 0.715 17 |
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution. |
DMF-Net | | 0.752 5 | 0.906 6 | 0.793 22 | 0.802 28 | 0.689 27 | 0.825 30 | 0.556 6 | 0.867 8 | 0.681 8 | 0.602 29 | 0.960 7 | 0.555 16 | 0.365 3 | 0.779 3 | 0.859 17 | 0.747 13 | 0.795 17 | 0.717 4 | 0.917 19 | 0.856 17 | 0.764 2 |
|
OccuSeg+Semantic | | 0.764 2 | 0.758 45 | 0.796 19 | 0.839 12 | 0.746 11 | 0.907 1 | 0.562 5 | 0.850 12 | 0.680 9 | 0.672 5 | 0.978 2 | 0.610 1 | 0.335 8 | 0.777 4 | 0.819 32 | 0.847 1 | 0.830 1 | 0.691 8 | 0.972 1 | 0.885 2 | 0.727 10 |
|
Retro-FPN | | 0.744 13 | 0.842 19 | 0.800 17 | 0.767 40 | 0.740 13 | 0.836 22 | 0.541 10 | 0.914 1 | 0.672 11 | 0.626 18 | 0.958 9 | 0.552 17 | 0.272 34 | 0.777 4 | 0.886 11 | 0.696 31 | 0.801 10 | 0.674 13 | 0.941 6 | 0.858 15 | 0.717 15 |
|
BPNet |  | 0.749 7 | 0.909 4 | 0.818 9 | 0.811 21 | 0.752 8 | 0.839 18 | 0.485 31 | 0.842 15 | 0.673 10 | 0.644 10 | 0.957 12 | 0.528 24 | 0.305 18 | 0.773 6 | 0.859 17 | 0.788 4 | 0.818 4 | 0.693 7 | 0.916 20 | 0.856 17 | 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) |
MSP | | 0.748 9 | 0.623 76 | 0.804 15 | 0.859 3 | 0.745 12 | 0.824 32 | 0.501 23 | 0.912 2 | 0.690 6 | 0.685 3 | 0.956 13 | 0.567 11 | 0.320 13 | 0.768 7 | 0.918 3 | 0.720 21 | 0.802 9 | 0.676 11 | 0.921 17 | 0.881 4 | 0.779 1 |
|
PointTransformerV2 | | 0.752 5 | 0.742 52 | 0.809 13 | 0.872 1 | 0.758 5 | 0.860 6 | 0.552 7 | 0.891 5 | 0.610 28 | 0.687 2 | 0.960 7 | 0.559 14 | 0.304 19 | 0.766 8 | 0.926 2 | 0.767 7 | 0.797 13 | 0.644 21 | 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 |
VMNet |  | 0.746 11 | 0.870 12 | 0.838 2 | 0.858 4 | 0.729 17 | 0.850 12 | 0.501 23 | 0.874 7 | 0.587 40 | 0.658 7 | 0.956 13 | 0.564 12 | 0.299 20 | 0.765 9 | 0.900 5 | 0.716 24 | 0.812 7 | 0.631 27 | 0.939 8 | 0.858 15 | 0.709 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) |
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 15 | 0.958 9 | 0.592 4 | 0.217 57 | 0.762 10 | 0.888 9 | 0.758 10 | 0.813 6 | 0.726 1 | 0.932 14 | 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 |
EQ-Net | | 0.743 14 | 0.620 77 | 0.799 18 | 0.849 5 | 0.730 16 | 0.822 34 | 0.493 29 | 0.897 4 | 0.664 12 | 0.681 4 | 0.955 17 | 0.562 13 | 0.378 1 | 0.760 11 | 0.903 4 | 0.738 15 | 0.801 10 | 0.673 14 | 0.907 24 | 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 |
MatchingNet | | 0.724 22 | 0.812 28 | 0.812 11 | 0.810 22 | 0.735 15 | 0.834 23 | 0.495 28 | 0.860 10 | 0.572 46 | 0.602 29 | 0.954 22 | 0.512 28 | 0.280 29 | 0.757 12 | 0.845 26 | 0.725 18 | 0.780 23 | 0.606 38 | 0.937 9 | 0.851 22 | 0.700 21 |
|
LRPNet | | 0.742 15 | 0.816 26 | 0.806 14 | 0.807 24 | 0.752 8 | 0.828 28 | 0.575 3 | 0.839 17 | 0.699 3 | 0.637 16 | 0.954 22 | 0.520 26 | 0.320 13 | 0.755 13 | 0.834 28 | 0.760 9 | 0.772 27 | 0.676 11 | 0.915 21 | 0.862 12 | 0.717 15 |
|
SAT | | 0.742 15 | 0.860 14 | 0.765 34 | 0.819 16 | 0.769 3 | 0.848 13 | 0.533 12 | 0.829 19 | 0.663 13 | 0.631 17 | 0.955 17 | 0.586 8 | 0.274 32 | 0.753 14 | 0.896 6 | 0.729 16 | 0.760 36 | 0.666 16 | 0.921 17 | 0.855 19 | 0.733 7 |
|
CU-Hybrid Net | | 0.764 2 | 0.924 2 | 0.819 7 | 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 22 | 0.753 14 | 0.884 12 | 0.758 10 | 0.815 5 | 0.725 2 | 0.927 16 | 0.867 9 | 0.743 5 |
|
One-Thing-One-Click | | 0.693 30 | 0.743 51 | 0.794 21 | 0.655 70 | 0.684 29 | 0.822 34 | 0.497 27 | 0.719 51 | 0.622 23 | 0.617 20 | 0.977 4 | 0.447 52 | 0.339 6 | 0.750 16 | 0.664 60 | 0.703 29 | 0.790 20 | 0.596 42 | 0.946 4 | 0.855 19 | 0.647 35 |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 |
Virtual MVFusion | | 0.746 11 | 0.771 39 | 0.819 7 | 0.848 6 | 0.702 25 | 0.865 5 | 0.397 68 | 0.899 3 | 0.699 3 | 0.664 6 | 0.948 39 | 0.588 6 | 0.330 9 | 0.746 17 | 0.851 24 | 0.764 8 | 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 |
StratifiedFormer |  | 0.747 10 | 0.901 7 | 0.803 16 | 0.845 8 | 0.757 6 | 0.846 14 | 0.512 19 | 0.825 21 | 0.696 5 | 0.645 9 | 0.956 13 | 0.576 9 | 0.262 43 | 0.744 18 | 0.861 16 | 0.742 14 | 0.770 30 | 0.705 5 | 0.899 31 | 0.860 14 | 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 |
MinkowskiNet |  | 0.736 18 | 0.859 15 | 0.818 9 | 0.832 13 | 0.709 22 | 0.840 17 | 0.521 18 | 0.853 11 | 0.660 15 | 0.643 11 | 0.951 30 | 0.544 18 | 0.286 27 | 0.731 19 | 0.893 7 | 0.675 38 | 0.772 27 | 0.683 9 | 0.874 49 | 0.852 21 | 0.727 10 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |
LargeKernel3D | | 0.739 17 | 0.909 4 | 0.820 6 | 0.806 26 | 0.740 13 | 0.852 10 | 0.545 9 | 0.826 20 | 0.594 38 | 0.643 11 | 0.955 17 | 0.541 19 | 0.263 42 | 0.723 20 | 0.858 19 | 0.775 6 | 0.767 31 | 0.678 10 | 0.933 12 | 0.848 23 | 0.694 23 |
|
SparseConvNet | | 0.725 20 | 0.647 73 | 0.821 5 | 0.846 7 | 0.721 20 | 0.869 3 | 0.533 12 | 0.754 41 | 0.603 34 | 0.614 22 | 0.955 17 | 0.572 10 | 0.325 11 | 0.710 21 | 0.870 13 | 0.724 19 | 0.823 2 | 0.628 28 | 0.934 11 | 0.865 11 | 0.683 25 |
|
PointMetaBase | | 0.714 24 | 0.835 20 | 0.785 25 | 0.821 14 | 0.684 29 | 0.846 14 | 0.531 14 | 0.865 9 | 0.614 25 | 0.596 32 | 0.953 25 | 0.500 31 | 0.246 49 | 0.674 22 | 0.888 9 | 0.692 32 | 0.764 33 | 0.624 29 | 0.849 63 | 0.844 28 | 0.675 27 |
|
VACNN++ | | 0.684 34 | 0.728 58 | 0.757 40 | 0.776 36 | 0.690 26 | 0.804 51 | 0.464 38 | 0.816 23 | 0.577 45 | 0.587 36 | 0.945 48 | 0.508 30 | 0.276 31 | 0.671 23 | 0.710 50 | 0.663 43 | 0.750 42 | 0.589 47 | 0.881 43 | 0.832 31 | 0.653 33 |
|
SAFNet-seg |  | 0.654 46 | 0.752 47 | 0.734 52 | 0.664 68 | 0.583 58 | 0.815 43 | 0.399 67 | 0.754 41 | 0.639 18 | 0.535 48 | 0.942 57 | 0.470 40 | 0.309 17 | 0.665 24 | 0.539 67 | 0.650 47 | 0.708 56 | 0.635 25 | 0.857 61 | 0.793 54 | 0.642 36 |
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021 |
PD-Net | | 0.638 52 | 0.797 30 | 0.769 33 | 0.641 76 | 0.590 54 | 0.820 37 | 0.461 39 | 0.537 81 | 0.637 19 | 0.536 47 | 0.947 41 | 0.388 72 | 0.206 61 | 0.656 25 | 0.668 58 | 0.647 51 | 0.732 48 | 0.585 49 | 0.868 56 | 0.793 54 | 0.473 85 |
|
wsss-transformer | | 0.600 68 | 0.634 74 | 0.743 48 | 0.697 57 | 0.601 51 | 0.781 62 | 0.437 56 | 0.585 75 | 0.493 67 | 0.446 71 | 0.933 72 | 0.394 70 | 0.011 96 | 0.654 26 | 0.661 61 | 0.603 63 | 0.733 47 | 0.526 71 | 0.832 66 | 0.761 71 | 0.480 82 |
|
MVPNet |  | 0.641 48 | 0.831 21 | 0.715 55 | 0.671 65 | 0.590 54 | 0.781 62 | 0.394 69 | 0.679 61 | 0.642 17 | 0.553 41 | 0.937 64 | 0.462 43 | 0.256 44 | 0.649 27 | 0.406 80 | 0.626 57 | 0.691 63 | 0.666 16 | 0.877 45 | 0.792 57 | 0.608 48 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 |
PPCNN++ |  | 0.663 43 | 0.746 49 | 0.708 57 | 0.722 48 | 0.638 42 | 0.820 37 | 0.451 42 | 0.566 77 | 0.599 36 | 0.541 44 | 0.950 34 | 0.510 29 | 0.313 15 | 0.648 28 | 0.819 32 | 0.616 61 | 0.682 66 | 0.590 46 | 0.869 55 | 0.810 43 | 0.656 32 |
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access |
PointConv-SFPN | | 0.641 48 | 0.776 37 | 0.703 59 | 0.721 49 | 0.557 66 | 0.826 29 | 0.451 42 | 0.672 63 | 0.563 52 | 0.483 63 | 0.943 56 | 0.425 62 | 0.162 80 | 0.644 29 | 0.726 46 | 0.659 45 | 0.709 55 | 0.572 51 | 0.875 47 | 0.786 61 | 0.559 67 |
|
Supervoxel-CNN | | 0.635 55 | 0.656 71 | 0.711 56 | 0.719 50 | 0.613 47 | 0.757 73 | 0.444 52 | 0.765 38 | 0.534 57 | 0.566 38 | 0.928 75 | 0.478 37 | 0.272 34 | 0.636 30 | 0.531 69 | 0.664 42 | 0.645 76 | 0.508 74 | 0.864 58 | 0.792 57 | 0.611 45 |
|
PointASNL |  | 0.666 41 | 0.703 65 | 0.781 28 | 0.751 46 | 0.655 34 | 0.830 25 | 0.471 34 | 0.769 37 | 0.474 73 | 0.537 46 | 0.951 30 | 0.475 38 | 0.279 30 | 0.635 31 | 0.698 54 | 0.675 38 | 0.751 41 | 0.553 61 | 0.816 70 | 0.806 44 | 0.703 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 |
APCF-Net | | 0.631 58 | 0.742 52 | 0.687 73 | 0.672 63 | 0.557 66 | 0.792 59 | 0.408 62 | 0.665 64 | 0.545 54 | 0.508 57 | 0.952 29 | 0.428 59 | 0.186 71 | 0.634 32 | 0.702 52 | 0.620 58 | 0.706 57 | 0.555 60 | 0.873 50 | 0.798 49 | 0.581 58 |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL |
FusionNet | | 0.688 32 | 0.704 63 | 0.741 50 | 0.754 44 | 0.656 33 | 0.829 26 | 0.501 23 | 0.741 46 | 0.609 30 | 0.548 42 | 0.950 34 | 0.522 25 | 0.371 2 | 0.633 33 | 0.756 40 | 0.715 25 | 0.771 29 | 0.623 30 | 0.861 59 | 0.814 40 | 0.658 31 |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 |
contrastBoundary |  | 0.705 25 | 0.769 42 | 0.775 30 | 0.809 23 | 0.687 28 | 0.820 37 | 0.439 55 | 0.812 28 | 0.661 14 | 0.591 35 | 0.945 48 | 0.515 27 | 0.171 75 | 0.633 33 | 0.856 20 | 0.720 21 | 0.796 14 | 0.668 15 | 0.889 38 | 0.847 25 | 0.689 24 |
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022 |
PointSPNet | | 0.637 53 | 0.734 55 | 0.692 68 | 0.714 52 | 0.576 60 | 0.797 55 | 0.446 47 | 0.743 45 | 0.598 37 | 0.437 74 | 0.942 57 | 0.403 68 | 0.150 84 | 0.626 35 | 0.800 36 | 0.649 48 | 0.697 60 | 0.557 59 | 0.846 64 | 0.777 65 | 0.563 65 |
|
IPCA | | 0.731 19 | 0.890 8 | 0.837 3 | 0.864 2 | 0.726 18 | 0.873 2 | 0.530 15 | 0.824 22 | 0.489 70 | 0.647 8 | 0.978 2 | 0.609 2 | 0.336 7 | 0.624 36 | 0.733 45 | 0.758 10 | 0.776 25 | 0.570 52 | 0.949 2 | 0.877 5 | 0.728 8 |
|
PointTransformer++ | | 0.725 20 | 0.727 59 | 0.811 12 | 0.819 16 | 0.765 4 | 0.841 16 | 0.502 22 | 0.814 27 | 0.621 24 | 0.623 19 | 0.955 17 | 0.556 15 | 0.284 28 | 0.620 37 | 0.866 14 | 0.781 5 | 0.757 39 | 0.648 19 | 0.932 14 | 0.862 12 | 0.709 18 |
|
INS-Conv-semantic | | 0.717 23 | 0.751 48 | 0.759 37 | 0.812 20 | 0.704 24 | 0.868 4 | 0.537 11 | 0.842 15 | 0.609 30 | 0.608 25 | 0.953 25 | 0.534 20 | 0.293 23 | 0.616 38 | 0.864 15 | 0.719 23 | 0.793 18 | 0.640 23 | 0.933 12 | 0.845 27 | 0.663 30 |
|
ROSMRF3D | | 0.673 39 | 0.789 32 | 0.748 43 | 0.763 42 | 0.635 43 | 0.814 44 | 0.407 64 | 0.747 43 | 0.581 44 | 0.573 37 | 0.950 34 | 0.484 34 | 0.271 36 | 0.607 39 | 0.754 41 | 0.649 48 | 0.774 26 | 0.596 42 | 0.883 41 | 0.823 35 | 0.606 49 |
|
3DSM_DMMF | | 0.631 58 | 0.626 75 | 0.745 46 | 0.801 29 | 0.607 48 | 0.751 74 | 0.506 20 | 0.729 50 | 0.565 50 | 0.491 62 | 0.866 91 | 0.434 54 | 0.197 68 | 0.595 40 | 0.630 62 | 0.709 27 | 0.705 58 | 0.560 56 | 0.875 47 | 0.740 76 | 0.491 80 |
|
RFCR | | 0.702 26 | 0.889 9 | 0.745 46 | 0.813 19 | 0.672 31 | 0.818 41 | 0.493 29 | 0.815 25 | 0.623 22 | 0.610 23 | 0.947 41 | 0.470 40 | 0.249 48 | 0.594 41 | 0.848 25 | 0.705 28 | 0.779 24 | 0.646 20 | 0.892 36 | 0.823 35 | 0.611 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 |
KP-FCNN | | 0.684 34 | 0.847 18 | 0.758 39 | 0.784 34 | 0.647 37 | 0.814 44 | 0.473 33 | 0.772 36 | 0.605 32 | 0.594 34 | 0.935 67 | 0.450 50 | 0.181 73 | 0.587 42 | 0.805 35 | 0.690 34 | 0.785 22 | 0.614 34 | 0.882 42 | 0.819 39 | 0.632 40 |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 |
PointMTL | | 0.632 57 | 0.731 56 | 0.688 71 | 0.675 62 | 0.591 53 | 0.784 61 | 0.444 52 | 0.565 78 | 0.610 28 | 0.492 61 | 0.949 37 | 0.456 46 | 0.254 45 | 0.587 42 | 0.706 51 | 0.599 65 | 0.665 72 | 0.612 37 | 0.868 56 | 0.791 60 | 0.579 59 |
|
PointConv |  | 0.666 41 | 0.781 34 | 0.759 37 | 0.699 55 | 0.644 40 | 0.822 34 | 0.475 32 | 0.779 34 | 0.564 51 | 0.504 60 | 0.953 25 | 0.428 59 | 0.203 64 | 0.586 44 | 0.754 41 | 0.661 44 | 0.753 40 | 0.588 48 | 0.902 28 | 0.813 42 | 0.642 36 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 |
PointMRNet | | 0.640 50 | 0.717 62 | 0.701 61 | 0.692 58 | 0.576 60 | 0.801 52 | 0.467 37 | 0.716 52 | 0.563 52 | 0.459 69 | 0.953 25 | 0.429 58 | 0.169 77 | 0.581 45 | 0.854 21 | 0.605 62 | 0.710 53 | 0.550 62 | 0.894 35 | 0.793 54 | 0.575 60 |
|
JSENet |  | 0.699 28 | 0.881 11 | 0.762 35 | 0.821 14 | 0.667 32 | 0.800 53 | 0.522 17 | 0.792 33 | 0.613 26 | 0.607 26 | 0.935 67 | 0.492 33 | 0.205 62 | 0.576 46 | 0.853 22 | 0.691 33 | 0.758 38 | 0.652 18 | 0.872 52 | 0.828 32 | 0.649 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 |
HPGCNN | | 0.656 45 | 0.698 66 | 0.743 48 | 0.650 71 | 0.564 63 | 0.820 37 | 0.505 21 | 0.758 39 | 0.631 21 | 0.479 64 | 0.945 48 | 0.480 36 | 0.226 53 | 0.572 47 | 0.774 39 | 0.690 34 | 0.735 46 | 0.614 34 | 0.853 62 | 0.776 66 | 0.597 55 |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. |
SALANet | | 0.670 40 | 0.816 26 | 0.770 32 | 0.768 39 | 0.652 36 | 0.807 48 | 0.451 42 | 0.747 43 | 0.659 16 | 0.545 43 | 0.924 77 | 0.473 39 | 0.149 85 | 0.571 48 | 0.811 34 | 0.635 56 | 0.746 43 | 0.623 30 | 0.892 36 | 0.794 52 | 0.570 62 |
|
joint point-based |  | 0.634 56 | 0.614 78 | 0.778 29 | 0.667 67 | 0.633 44 | 0.825 30 | 0.420 60 | 0.804 29 | 0.467 75 | 0.561 39 | 0.951 30 | 0.494 32 | 0.291 24 | 0.566 49 | 0.458 75 | 0.579 72 | 0.764 33 | 0.559 58 | 0.838 65 | 0.814 40 | 0.598 54 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 |
SegGroup_sem |  | 0.627 63 | 0.818 25 | 0.747 45 | 0.701 54 | 0.602 50 | 0.764 70 | 0.385 74 | 0.629 69 | 0.490 68 | 0.508 57 | 0.931 74 | 0.409 67 | 0.201 65 | 0.564 50 | 0.725 47 | 0.618 59 | 0.692 62 | 0.539 68 | 0.873 50 | 0.794 52 | 0.548 71 |
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022 |
Feature_GeometricNet |  | 0.690 31 | 0.884 10 | 0.754 41 | 0.795 31 | 0.647 37 | 0.818 41 | 0.422 59 | 0.802 31 | 0.612 27 | 0.604 27 | 0.945 48 | 0.462 43 | 0.189 70 | 0.563 51 | 0.853 22 | 0.726 17 | 0.765 32 | 0.632 26 | 0.904 26 | 0.821 38 | 0.606 49 |
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint |
One Thing One Click | | 0.701 27 | 0.825 24 | 0.796 19 | 0.723 47 | 0.716 21 | 0.832 24 | 0.433 57 | 0.816 23 | 0.634 20 | 0.609 24 | 0.969 6 | 0.418 65 | 0.344 5 | 0.559 52 | 0.833 29 | 0.715 25 | 0.808 8 | 0.560 56 | 0.902 28 | 0.847 25 | 0.680 26 |
|
SD-DETR | | 0.576 73 | 0.746 49 | 0.609 87 | 0.445 92 | 0.517 72 | 0.643 88 | 0.366 76 | 0.714 54 | 0.456 77 | 0.468 67 | 0.870 90 | 0.432 55 | 0.264 41 | 0.558 53 | 0.674 56 | 0.586 71 | 0.688 64 | 0.482 80 | 0.739 79 | 0.733 78 | 0.537 73 |
|
Feature-Geometry Net |  | 0.685 33 | 0.866 13 | 0.748 43 | 0.819 16 | 0.645 39 | 0.794 56 | 0.450 45 | 0.802 31 | 0.587 40 | 0.604 27 | 0.945 48 | 0.464 42 | 0.201 65 | 0.554 54 | 0.840 27 | 0.723 20 | 0.732 48 | 0.602 40 | 0.907 24 | 0.822 37 | 0.603 52 |
|
PointContrast_LA_SEM | | 0.683 36 | 0.757 46 | 0.784 26 | 0.786 33 | 0.639 41 | 0.824 32 | 0.408 62 | 0.775 35 | 0.604 33 | 0.541 44 | 0.934 71 | 0.532 21 | 0.269 38 | 0.552 55 | 0.777 38 | 0.645 53 | 0.793 18 | 0.640 23 | 0.913 22 | 0.824 34 | 0.671 28 |
|
PointNet2-SFPN | | 0.631 58 | 0.771 39 | 0.692 68 | 0.672 63 | 0.524 70 | 0.837 20 | 0.440 54 | 0.706 57 | 0.538 56 | 0.446 71 | 0.944 54 | 0.421 64 | 0.219 56 | 0.552 55 | 0.751 43 | 0.591 68 | 0.737 45 | 0.543 66 | 0.901 30 | 0.768 68 | 0.557 68 |
|
PicassoNet-II |  | 0.696 29 | 0.704 63 | 0.790 23 | 0.787 32 | 0.709 22 | 0.837 20 | 0.459 40 | 0.815 25 | 0.543 55 | 0.615 21 | 0.956 13 | 0.529 22 | 0.250 46 | 0.551 57 | 0.790 37 | 0.703 29 | 0.799 12 | 0.619 33 | 0.908 23 | 0.848 23 | 0.700 21 |
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes. |
FusionAwareConv | | 0.630 61 | 0.604 80 | 0.741 50 | 0.766 41 | 0.590 54 | 0.747 75 | 0.501 23 | 0.734 48 | 0.503 65 | 0.527 50 | 0.919 81 | 0.454 47 | 0.323 12 | 0.550 58 | 0.420 79 | 0.678 37 | 0.688 64 | 0.544 64 | 0.896 33 | 0.795 51 | 0.627 42 |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 |
FPConv |  | 0.639 51 | 0.785 33 | 0.760 36 | 0.713 53 | 0.603 49 | 0.798 54 | 0.392 70 | 0.534 82 | 0.603 34 | 0.524 52 | 0.948 39 | 0.457 45 | 0.250 46 | 0.538 59 | 0.723 48 | 0.598 66 | 0.696 61 | 0.614 34 | 0.872 52 | 0.799 47 | 0.567 64 |
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020 |
3DMV | | 0.484 84 | 0.484 90 | 0.538 92 | 0.643 75 | 0.424 83 | 0.606 93 | 0.310 82 | 0.574 76 | 0.433 83 | 0.378 80 | 0.796 93 | 0.301 84 | 0.214 59 | 0.537 60 | 0.208 91 | 0.472 84 | 0.507 93 | 0.413 91 | 0.693 83 | 0.602 92 | 0.539 72 |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 |
VI-PointConv | | 0.676 38 | 0.770 41 | 0.754 41 | 0.783 35 | 0.621 45 | 0.814 44 | 0.552 7 | 0.758 39 | 0.571 48 | 0.557 40 | 0.954 22 | 0.529 22 | 0.268 40 | 0.530 61 | 0.682 55 | 0.675 38 | 0.719 51 | 0.603 39 | 0.888 39 | 0.833 30 | 0.665 29 |
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions. |
Online SegFusion | | 0.515 81 | 0.607 79 | 0.644 81 | 0.579 83 | 0.434 82 | 0.630 90 | 0.353 78 | 0.628 70 | 0.440 80 | 0.410 77 | 0.762 95 | 0.307 83 | 0.167 78 | 0.520 62 | 0.403 81 | 0.516 77 | 0.565 84 | 0.447 86 | 0.678 85 | 0.701 83 | 0.514 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 |
Superpoint Network | | 0.683 36 | 0.851 17 | 0.728 54 | 0.800 30 | 0.653 35 | 0.806 49 | 0.468 35 | 0.804 29 | 0.572 46 | 0.602 29 | 0.946 45 | 0.453 49 | 0.239 52 | 0.519 63 | 0.822 30 | 0.689 36 | 0.762 35 | 0.595 44 | 0.895 34 | 0.827 33 | 0.630 41 |
|
CCRFNet | | 0.589 71 | 0.766 43 | 0.659 78 | 0.683 60 | 0.470 79 | 0.740 77 | 0.387 73 | 0.620 71 | 0.490 68 | 0.476 65 | 0.922 79 | 0.355 78 | 0.245 50 | 0.511 64 | 0.511 72 | 0.571 73 | 0.643 77 | 0.493 78 | 0.872 52 | 0.762 70 | 0.600 53 |
|
DCM-Net | | 0.658 44 | 0.778 35 | 0.702 60 | 0.806 26 | 0.619 46 | 0.813 47 | 0.468 35 | 0.693 59 | 0.494 66 | 0.524 52 | 0.941 59 | 0.449 51 | 0.298 21 | 0.510 65 | 0.821 31 | 0.675 38 | 0.727 50 | 0.568 54 | 0.826 68 | 0.803 46 | 0.637 38 |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] |
SPH3D-GCN |  | 0.610 66 | 0.858 16 | 0.772 31 | 0.489 88 | 0.532 69 | 0.792 59 | 0.404 66 | 0.643 68 | 0.570 49 | 0.507 59 | 0.935 67 | 0.414 66 | 0.046 94 | 0.510 65 | 0.702 52 | 0.602 64 | 0.705 58 | 0.549 63 | 0.859 60 | 0.773 67 | 0.534 74 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 |
SConv | | 0.636 54 | 0.830 22 | 0.697 64 | 0.752 45 | 0.572 62 | 0.780 64 | 0.445 49 | 0.716 52 | 0.529 58 | 0.530 49 | 0.951 30 | 0.446 53 | 0.170 76 | 0.507 67 | 0.666 59 | 0.636 55 | 0.682 66 | 0.541 67 | 0.886 40 | 0.799 47 | 0.594 56 |
|
PanopticFusion-label | | 0.529 79 | 0.491 89 | 0.688 71 | 0.604 80 | 0.386 85 | 0.632 89 | 0.225 94 | 0.705 58 | 0.434 82 | 0.293 89 | 0.815 92 | 0.348 79 | 0.241 51 | 0.499 68 | 0.669 57 | 0.507 78 | 0.649 74 | 0.442 88 | 0.796 72 | 0.602 92 | 0.561 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) |
SIConv | | 0.625 64 | 0.830 22 | 0.694 66 | 0.757 43 | 0.563 64 | 0.772 68 | 0.448 46 | 0.647 67 | 0.520 60 | 0.509 56 | 0.949 37 | 0.431 57 | 0.191 69 | 0.496 69 | 0.614 63 | 0.647 51 | 0.672 70 | 0.535 70 | 0.876 46 | 0.783 62 | 0.571 61 |
|
HPEIN | | 0.618 65 | 0.729 57 | 0.668 74 | 0.647 73 | 0.597 52 | 0.766 69 | 0.414 61 | 0.680 60 | 0.520 60 | 0.525 51 | 0.946 45 | 0.432 55 | 0.215 58 | 0.493 70 | 0.599 64 | 0.638 54 | 0.617 81 | 0.570 52 | 0.897 32 | 0.806 44 | 0.605 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 |
DenSeR | | 0.628 62 | 0.800 29 | 0.625 83 | 0.719 50 | 0.545 68 | 0.806 49 | 0.445 49 | 0.597 72 | 0.448 79 | 0.519 55 | 0.938 63 | 0.481 35 | 0.328 10 | 0.489 71 | 0.499 74 | 0.657 46 | 0.759 37 | 0.592 45 | 0.881 43 | 0.797 50 | 0.634 39 |
|
RandLA-Net |  | 0.645 47 | 0.778 35 | 0.731 53 | 0.699 55 | 0.577 59 | 0.829 26 | 0.446 47 | 0.736 47 | 0.477 72 | 0.523 54 | 0.945 48 | 0.454 47 | 0.269 38 | 0.484 72 | 0.749 44 | 0.618 59 | 0.738 44 | 0.599 41 | 0.827 67 | 0.792 57 | 0.621 43 |
|
GMLPs | | 0.538 78 | 0.495 88 | 0.693 67 | 0.647 73 | 0.471 78 | 0.793 57 | 0.300 84 | 0.477 84 | 0.505 64 | 0.358 83 | 0.903 87 | 0.327 81 | 0.081 91 | 0.472 73 | 0.529 70 | 0.448 85 | 0.710 53 | 0.509 72 | 0.746 77 | 0.737 77 | 0.554 70 |
|
AttAN | | 0.609 67 | 0.760 44 | 0.667 75 | 0.649 72 | 0.521 71 | 0.793 57 | 0.457 41 | 0.648 66 | 0.528 59 | 0.434 76 | 0.947 41 | 0.401 69 | 0.153 83 | 0.454 74 | 0.721 49 | 0.648 50 | 0.717 52 | 0.536 69 | 0.904 26 | 0.765 69 | 0.485 81 |
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020 |
ROSMRF | | 0.580 72 | 0.772 38 | 0.707 58 | 0.681 61 | 0.563 64 | 0.764 70 | 0.362 77 | 0.515 83 | 0.465 76 | 0.465 68 | 0.936 66 | 0.427 61 | 0.207 60 | 0.438 75 | 0.577 65 | 0.536 76 | 0.675 69 | 0.486 79 | 0.723 81 | 0.779 63 | 0.524 76 |
|
3DMV, FTSDF | | 0.501 82 | 0.558 84 | 0.608 88 | 0.424 94 | 0.478 77 | 0.690 81 | 0.246 90 | 0.586 74 | 0.468 74 | 0.450 70 | 0.911 83 | 0.394 70 | 0.160 81 | 0.438 75 | 0.212 90 | 0.432 86 | 0.541 89 | 0.475 81 | 0.742 78 | 0.727 79 | 0.477 83 |
|
DPC | | 0.592 70 | 0.720 60 | 0.700 62 | 0.602 81 | 0.480 76 | 0.762 72 | 0.380 75 | 0.713 55 | 0.585 43 | 0.437 74 | 0.940 61 | 0.369 75 | 0.288 25 | 0.434 77 | 0.509 73 | 0.590 70 | 0.639 79 | 0.567 55 | 0.772 75 | 0.755 73 | 0.592 57 |
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020 |
LAP-D | | 0.594 69 | 0.720 60 | 0.692 68 | 0.637 77 | 0.456 80 | 0.773 67 | 0.391 72 | 0.730 49 | 0.587 40 | 0.445 73 | 0.940 61 | 0.381 73 | 0.288 25 | 0.434 77 | 0.453 77 | 0.591 68 | 0.649 74 | 0.581 50 | 0.777 74 | 0.749 75 | 0.610 47 |
|
TextureNet |  | 0.566 75 | 0.672 70 | 0.664 76 | 0.671 65 | 0.494 74 | 0.719 78 | 0.445 49 | 0.678 62 | 0.411 85 | 0.396 79 | 0.935 67 | 0.356 77 | 0.225 54 | 0.412 79 | 0.535 68 | 0.565 74 | 0.636 80 | 0.464 82 | 0.794 73 | 0.680 86 | 0.568 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++ & Feature |  | 0.557 77 | 0.735 54 | 0.661 77 | 0.686 59 | 0.491 75 | 0.744 76 | 0.392 70 | 0.539 80 | 0.451 78 | 0.375 82 | 0.946 45 | 0.376 74 | 0.205 62 | 0.403 80 | 0.356 83 | 0.553 75 | 0.643 77 | 0.497 76 | 0.824 69 | 0.756 72 | 0.515 77 |
|
SQN_0.1% | | 0.569 74 | 0.676 68 | 0.696 65 | 0.657 69 | 0.497 73 | 0.779 65 | 0.424 58 | 0.548 79 | 0.515 62 | 0.376 81 | 0.902 88 | 0.422 63 | 0.357 4 | 0.379 81 | 0.456 76 | 0.596 67 | 0.659 73 | 0.544 64 | 0.685 84 | 0.665 89 | 0.556 69 |
|
SurfaceConvPF | | 0.442 88 | 0.505 87 | 0.622 85 | 0.380 95 | 0.342 91 | 0.654 85 | 0.227 93 | 0.397 87 | 0.367 88 | 0.276 91 | 0.924 77 | 0.240 91 | 0.198 67 | 0.359 82 | 0.262 86 | 0.366 89 | 0.581 82 | 0.435 89 | 0.640 87 | 0.668 87 | 0.398 87 |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. |
3DWSSS | | 0.425 91 | 0.525 86 | 0.647 79 | 0.522 86 | 0.324 92 | 0.488 96 | 0.077 97 | 0.712 56 | 0.353 89 | 0.401 78 | 0.636 97 | 0.281 87 | 0.176 74 | 0.340 83 | 0.565 66 | 0.175 96 | 0.551 87 | 0.398 92 | 0.370 96 | 0.602 92 | 0.361 90 |
|
DVVNet | | 0.562 76 | 0.648 72 | 0.700 62 | 0.770 38 | 0.586 57 | 0.687 82 | 0.333 81 | 0.650 65 | 0.514 63 | 0.475 66 | 0.906 85 | 0.359 76 | 0.223 55 | 0.340 83 | 0.442 78 | 0.422 87 | 0.668 71 | 0.501 75 | 0.708 82 | 0.779 63 | 0.534 74 |
|
subcloud_weak | | 0.516 80 | 0.676 68 | 0.591 90 | 0.609 78 | 0.442 81 | 0.774 66 | 0.335 80 | 0.597 72 | 0.422 84 | 0.357 84 | 0.932 73 | 0.341 80 | 0.094 90 | 0.298 85 | 0.528 71 | 0.473 83 | 0.676 68 | 0.495 77 | 0.602 90 | 0.721 81 | 0.349 92 |
|
DGCNN_reproduce |  | 0.446 87 | 0.474 91 | 0.623 84 | 0.463 90 | 0.366 88 | 0.651 86 | 0.310 82 | 0.389 88 | 0.349 90 | 0.330 86 | 0.937 64 | 0.271 88 | 0.126 87 | 0.285 86 | 0.224 89 | 0.350 92 | 0.577 83 | 0.445 87 | 0.625 88 | 0.723 80 | 0.394 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 |
Tangent Convolutions |  | 0.438 90 | 0.437 93 | 0.646 80 | 0.474 89 | 0.369 87 | 0.645 87 | 0.353 78 | 0.258 92 | 0.282 94 | 0.279 90 | 0.918 82 | 0.298 85 | 0.147 86 | 0.283 87 | 0.294 85 | 0.487 80 | 0.562 85 | 0.427 90 | 0.619 89 | 0.633 90 | 0.352 91 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 |
ScanNet+FTSDF | | 0.383 94 | 0.297 95 | 0.491 94 | 0.432 93 | 0.358 90 | 0.612 92 | 0.274 88 | 0.116 94 | 0.411 85 | 0.265 92 | 0.904 86 | 0.229 92 | 0.079 92 | 0.250 88 | 0.185 93 | 0.320 93 | 0.510 91 | 0.385 93 | 0.548 92 | 0.597 95 | 0.394 88 |
|
ScanNet |  | 0.306 97 | 0.203 96 | 0.366 96 | 0.501 87 | 0.311 94 | 0.524 95 | 0.211 95 | 0.002 97 | 0.342 91 | 0.189 96 | 0.786 94 | 0.145 96 | 0.102 89 | 0.245 89 | 0.152 94 | 0.318 94 | 0.348 96 | 0.300 96 | 0.460 95 | 0.437 97 | 0.182 97 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 |
PCNN | | 0.498 83 | 0.559 83 | 0.644 81 | 0.560 85 | 0.420 84 | 0.711 80 | 0.229 92 | 0.414 85 | 0.436 81 | 0.352 85 | 0.941 59 | 0.324 82 | 0.155 82 | 0.238 90 | 0.387 82 | 0.493 79 | 0.529 90 | 0.509 72 | 0.813 71 | 0.751 74 | 0.504 79 |
|
PNET2 | | 0.442 88 | 0.548 85 | 0.548 91 | 0.597 82 | 0.363 89 | 0.628 91 | 0.300 84 | 0.292 90 | 0.374 87 | 0.307 88 | 0.881 89 | 0.268 89 | 0.186 71 | 0.238 90 | 0.204 92 | 0.407 88 | 0.506 94 | 0.449 85 | 0.667 86 | 0.620 91 | 0.462 86 |
|
FCPN |  | 0.447 86 | 0.679 67 | 0.604 89 | 0.578 84 | 0.380 86 | 0.682 83 | 0.291 87 | 0.106 95 | 0.483 71 | 0.258 94 | 0.920 80 | 0.258 90 | 0.025 95 | 0.231 92 | 0.325 84 | 0.480 82 | 0.560 86 | 0.463 83 | 0.725 80 | 0.666 88 | 0.231 96 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 |
PointCNN with RGB |  | 0.458 85 | 0.577 82 | 0.611 86 | 0.356 96 | 0.321 93 | 0.715 79 | 0.299 86 | 0.376 89 | 0.328 92 | 0.319 87 | 0.944 54 | 0.285 86 | 0.164 79 | 0.216 93 | 0.229 88 | 0.484 81 | 0.545 88 | 0.456 84 | 0.755 76 | 0.709 82 | 0.475 84 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 |
PointNet++ |  | 0.339 95 | 0.584 81 | 0.478 95 | 0.458 91 | 0.256 96 | 0.360 97 | 0.250 89 | 0.247 93 | 0.278 95 | 0.261 93 | 0.677 96 | 0.183 94 | 0.117 88 | 0.212 94 | 0.145 95 | 0.364 90 | 0.346 97 | 0.232 97 | 0.548 92 | 0.523 96 | 0.252 95 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. |
ERROR | | 0.054 98 | 0.000 97 | 0.041 98 | 0.172 98 | 0.030 98 | 0.062 98 | 0.001 98 | 0.035 96 | 0.004 97 | 0.051 98 | 0.143 98 | 0.019 97 | 0.003 97 | 0.041 95 | 0.050 96 | 0.003 97 | 0.054 98 | 0.018 98 | 0.005 97 | 0.264 98 | 0.082 98 |
|
SSC-UNet |  | 0.308 96 | 0.353 94 | 0.290 97 | 0.278 97 | 0.166 97 | 0.553 94 | 0.169 96 | 0.286 91 | 0.147 96 | 0.148 97 | 0.908 84 | 0.182 95 | 0.064 93 | 0.023 96 | 0.018 97 | 0.354 91 | 0.363 95 | 0.345 95 | 0.546 94 | 0.685 85 | 0.278 93 |
|
SPLAT Net |  | 0.393 93 | 0.472 92 | 0.511 93 | 0.606 79 | 0.311 94 | 0.656 84 | 0.245 91 | 0.405 86 | 0.328 92 | 0.197 95 | 0.927 76 | 0.227 93 | 0.000 98 | 0.001 97 | 0.249 87 | 0.271 95 | 0.510 91 | 0.383 94 | 0.593 91 | 0.699 84 | 0.267 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 |
SimConv | | 0.410 92 | 0.000 97 | 0.782 27 | 0.772 37 | 0.722 19 | 0.838 19 | 0.407 64 | 0.000 98 | 0.000 98 | 0.595 33 | 0.947 41 | 0.000 98 | 0.270 37 | 0.000 98 | 0.000 98 | 0.000 98 | 0.786 21 | 0.621 32 | 0.000 98 | 0.841 29 | 0.621 43 |
|