MSP | | 0.748 10 | 0.623 80 | 0.804 17 | 0.859 3 | 0.745 13 | 0.824 34 | 0.501 24 | 0.912 2 | 0.690 6 | 0.685 3 | 0.956 15 | 0.567 12 | 0.320 14 | 0.768 8 | 0.918 3 | 0.720 23 | 0.802 10 | 0.676 13 | 0.921 19 | 0.881 4 | 0.779 1 |
|
DMF-Net | | 0.752 6 | 0.906 6 | 0.793 24 | 0.802 30 | 0.689 28 | 0.825 32 | 0.556 6 | 0.867 10 | 0.681 8 | 0.602 31 | 0.960 9 | 0.555 17 | 0.365 3 | 0.779 3 | 0.859 17 | 0.747 15 | 0.795 18 | 0.717 4 | 0.917 21 | 0.856 19 | 0.764 2 |
|
OA-CNN-L_ScanNet20 | | 0.756 5 | 0.783 35 | 0.826 5 | 0.858 4 | 0.776 2 | 0.837 20 | 0.548 9 | 0.896 5 | 0.649 17 | 0.675 5 | 0.962 8 | 0.586 8 | 0.335 8 | 0.771 7 | 0.802 36 | 0.770 8 | 0.787 22 | 0.691 8 | 0.936 11 | 0.880 5 | 0.761 3 |
|
EQ-Net | | 0.743 15 | 0.620 81 | 0.799 20 | 0.849 6 | 0.730 17 | 0.822 36 | 0.493 31 | 0.897 4 | 0.664 12 | 0.681 4 | 0.955 19 | 0.562 14 | 0.378 1 | 0.760 12 | 0.903 4 | 0.738 17 | 0.801 11 | 0.673 16 | 0.907 27 | 0.877 6 | 0.745 4 |
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022 |
O-CNN |  | 0.762 4 | 0.924 2 | 0.823 6 | 0.844 10 | 0.770 3 | 0.852 10 | 0.577 2 | 0.847 16 | 0.711 1 | 0.640 17 | 0.958 11 | 0.592 4 | 0.217 59 | 0.762 11 | 0.888 9 | 0.758 12 | 0.813 7 | 0.726 1 | 0.932 16 | 0.868 10 | 0.744 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 |
CU-Hybrid Net | | 0.764 2 | 0.924 2 | 0.819 9 | 0.840 12 | 0.757 7 | 0.853 9 | 0.580 1 | 0.848 15 | 0.709 2 | 0.643 13 | 0.958 11 | 0.587 7 | 0.295 24 | 0.753 15 | 0.884 12 | 0.758 12 | 0.815 6 | 0.725 2 | 0.927 18 | 0.867 11 | 0.743 6 |
|
StratifiedFormer |  | 0.747 11 | 0.901 7 | 0.803 18 | 0.845 9 | 0.757 7 | 0.846 14 | 0.512 20 | 0.825 23 | 0.696 5 | 0.645 11 | 0.956 15 | 0.576 10 | 0.262 45 | 0.744 19 | 0.861 16 | 0.742 16 | 0.770 31 | 0.705 5 | 0.899 34 | 0.860 16 | 0.734 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 |
SAT | | 0.742 16 | 0.860 14 | 0.765 36 | 0.819 17 | 0.769 4 | 0.848 13 | 0.533 13 | 0.829 21 | 0.663 13 | 0.631 19 | 0.955 19 | 0.586 8 | 0.274 35 | 0.753 15 | 0.896 6 | 0.729 18 | 0.760 38 | 0.666 18 | 0.921 19 | 0.855 21 | 0.733 8 |
|
IPCA | | 0.731 21 | 0.890 8 | 0.837 3 | 0.864 2 | 0.726 19 | 0.873 2 | 0.530 16 | 0.824 24 | 0.489 74 | 0.647 10 | 0.978 2 | 0.609 2 | 0.336 7 | 0.624 38 | 0.733 47 | 0.758 12 | 0.776 26 | 0.570 54 | 0.949 2 | 0.877 6 | 0.728 9 |
|
Virtual MVFusion | | 0.746 12 | 0.771 41 | 0.819 9 | 0.848 7 | 0.702 26 | 0.865 5 | 0.397 71 | 0.899 3 | 0.699 3 | 0.664 8 | 0.948 42 | 0.588 6 | 0.330 10 | 0.746 18 | 0.851 24 | 0.764 10 | 0.796 15 | 0.704 6 | 0.935 12 | 0.866 12 | 0.728 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 |
OccuSeg+Semantic | | 0.764 2 | 0.758 47 | 0.796 21 | 0.839 13 | 0.746 12 | 0.907 1 | 0.562 5 | 0.850 14 | 0.680 9 | 0.672 6 | 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 11 |
|
MinkowskiNet |  | 0.736 20 | 0.859 15 | 0.818 11 | 0.832 14 | 0.709 23 | 0.840 18 | 0.521 19 | 0.853 13 | 0.660 15 | 0.643 13 | 0.951 32 | 0.544 20 | 0.286 29 | 0.731 20 | 0.893 7 | 0.675 40 | 0.772 28 | 0.683 10 | 0.874 52 | 0.852 23 | 0.727 11 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |
Mix3D |  | 0.781 1 | 0.964 1 | 0.855 1 | 0.843 11 | 0.781 1 | 0.858 7 | 0.575 3 | 0.831 20 | 0.685 7 | 0.714 1 | 0.979 1 | 0.594 3 | 0.310 18 | 0.801 1 | 0.892 8 | 0.841 2 | 0.819 3 | 0.723 3 | 0.940 8 | 0.887 1 | 0.725 13 |
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral) |
BPNet |  | 0.749 8 | 0.909 4 | 0.818 11 | 0.811 22 | 0.752 9 | 0.839 19 | 0.485 33 | 0.842 17 | 0.673 10 | 0.644 12 | 0.957 14 | 0.528 26 | 0.305 20 | 0.773 6 | 0.859 17 | 0.788 4 | 0.818 5 | 0.693 7 | 0.916 22 | 0.856 19 | 0.723 14 |
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral) |
PointTransformerV2 | | 0.752 6 | 0.742 54 | 0.809 15 | 0.872 1 | 0.758 6 | 0.860 6 | 0.552 7 | 0.891 6 | 0.610 31 | 0.687 2 | 0.960 9 | 0.559 15 | 0.304 21 | 0.766 9 | 0.926 2 | 0.767 9 | 0.797 14 | 0.644 23 | 0.942 6 | 0.876 8 | 0.722 15 |
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022 |
Retro-FPN | | 0.744 14 | 0.842 19 | 0.800 19 | 0.767 42 | 0.740 14 | 0.836 23 | 0.541 11 | 0.914 1 | 0.672 11 | 0.626 20 | 0.958 11 | 0.552 18 | 0.272 37 | 0.777 4 | 0.886 11 | 0.696 33 | 0.801 11 | 0.674 15 | 0.941 7 | 0.858 17 | 0.717 16 |
|
LRPNet | | 0.742 16 | 0.816 27 | 0.806 16 | 0.807 25 | 0.752 9 | 0.828 30 | 0.575 3 | 0.839 19 | 0.699 3 | 0.637 18 | 0.954 24 | 0.520 28 | 0.320 14 | 0.755 14 | 0.834 28 | 0.760 11 | 0.772 28 | 0.676 13 | 0.915 23 | 0.862 14 | 0.717 16 |
|
PointConvFormer | | 0.749 8 | 0.793 32 | 0.790 25 | 0.807 25 | 0.750 11 | 0.856 8 | 0.524 17 | 0.881 8 | 0.588 42 | 0.642 16 | 0.977 4 | 0.591 5 | 0.274 35 | 0.781 2 | 0.929 1 | 0.804 3 | 0.796 15 | 0.642 24 | 0.947 3 | 0.885 2 | 0.715 18 |
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution. |
VMNet |  | 0.746 12 | 0.870 12 | 0.838 2 | 0.858 4 | 0.729 18 | 0.850 12 | 0.501 24 | 0.874 9 | 0.587 43 | 0.658 9 | 0.956 15 | 0.564 13 | 0.299 22 | 0.765 10 | 0.900 5 | 0.716 26 | 0.812 8 | 0.631 29 | 0.939 9 | 0.858 17 | 0.709 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) |
PointTransformer++ | | 0.725 22 | 0.727 61 | 0.811 14 | 0.819 17 | 0.765 5 | 0.841 17 | 0.502 23 | 0.814 30 | 0.621 26 | 0.623 21 | 0.955 19 | 0.556 16 | 0.284 30 | 0.620 39 | 0.866 14 | 0.781 5 | 0.757 41 | 0.648 21 | 0.932 16 | 0.862 14 | 0.709 19 |
|
PointASNL |  | 0.666 44 | 0.703 68 | 0.781 30 | 0.751 48 | 0.655 36 | 0.830 27 | 0.471 37 | 0.769 40 | 0.474 77 | 0.537 48 | 0.951 32 | 0.475 41 | 0.279 33 | 0.635 33 | 0.698 56 | 0.675 40 | 0.751 43 | 0.553 63 | 0.816 74 | 0.806 46 | 0.703 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 |
MatchingNet | | 0.724 24 | 0.812 29 | 0.812 13 | 0.810 23 | 0.735 16 | 0.834 25 | 0.495 30 | 0.860 12 | 0.572 49 | 0.602 31 | 0.954 24 | 0.512 30 | 0.280 32 | 0.757 13 | 0.845 26 | 0.725 20 | 0.780 24 | 0.606 40 | 0.937 10 | 0.851 24 | 0.700 22 |
|
PicassoNet-II |  | 0.696 31 | 0.704 66 | 0.790 25 | 0.787 34 | 0.709 23 | 0.837 20 | 0.459 43 | 0.815 28 | 0.543 58 | 0.615 23 | 0.956 15 | 0.529 24 | 0.250 48 | 0.551 60 | 0.790 39 | 0.703 31 | 0.799 13 | 0.619 35 | 0.908 26 | 0.848 25 | 0.700 22 |
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes. |
TXC | | 0.740 18 | 0.842 19 | 0.832 4 | 0.805 29 | 0.715 22 | 0.846 14 | 0.473 35 | 0.885 7 | 0.615 27 | 0.671 7 | 0.971 6 | 0.547 19 | 0.320 14 | 0.697 23 | 0.799 38 | 0.777 6 | 0.819 3 | 0.682 11 | 0.946 4 | 0.871 9 | 0.696 24 |
|
LargeKernel3D | | 0.739 19 | 0.909 4 | 0.820 8 | 0.806 27 | 0.740 14 | 0.852 10 | 0.545 10 | 0.826 22 | 0.594 41 | 0.643 13 | 0.955 19 | 0.541 21 | 0.263 44 | 0.723 21 | 0.858 19 | 0.775 7 | 0.767 32 | 0.678 12 | 0.933 14 | 0.848 25 | 0.694 25 |
|
contrastBoundary |  | 0.705 27 | 0.769 44 | 0.775 32 | 0.809 24 | 0.687 29 | 0.820 39 | 0.439 59 | 0.812 31 | 0.661 14 | 0.591 37 | 0.945 50 | 0.515 29 | 0.171 77 | 0.633 35 | 0.856 20 | 0.720 23 | 0.796 15 | 0.668 17 | 0.889 41 | 0.847 27 | 0.689 26 |
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022 |
DGNet | | 0.684 36 | 0.712 65 | 0.784 28 | 0.782 38 | 0.658 34 | 0.835 24 | 0.499 28 | 0.823 25 | 0.641 19 | 0.597 34 | 0.950 36 | 0.487 36 | 0.281 31 | 0.575 49 | 0.619 65 | 0.647 53 | 0.764 34 | 0.620 34 | 0.871 58 | 0.846 29 | 0.688 27 |
|
SparseConvNet | | 0.725 22 | 0.647 77 | 0.821 7 | 0.846 8 | 0.721 20 | 0.869 3 | 0.533 13 | 0.754 44 | 0.603 37 | 0.614 24 | 0.955 19 | 0.572 11 | 0.325 12 | 0.710 22 | 0.870 13 | 0.724 21 | 0.823 2 | 0.628 30 | 0.934 13 | 0.865 13 | 0.683 28 |
|
One Thing One Click | | 0.701 29 | 0.825 25 | 0.796 21 | 0.723 49 | 0.716 21 | 0.832 26 | 0.433 61 | 0.816 26 | 0.634 22 | 0.609 26 | 0.969 7 | 0.418 68 | 0.344 5 | 0.559 55 | 0.833 29 | 0.715 27 | 0.808 9 | 0.560 58 | 0.902 31 | 0.847 27 | 0.680 29 |
|
PointMetaBase | | 0.714 26 | 0.835 21 | 0.785 27 | 0.821 15 | 0.684 30 | 0.846 14 | 0.531 15 | 0.865 11 | 0.614 28 | 0.596 35 | 0.953 27 | 0.500 33 | 0.246 51 | 0.674 24 | 0.888 9 | 0.692 34 | 0.764 34 | 0.624 31 | 0.849 67 | 0.844 31 | 0.675 30 |
|
PointContrast_LA_SEM | | 0.683 39 | 0.757 48 | 0.784 28 | 0.786 35 | 0.639 43 | 0.824 34 | 0.408 66 | 0.775 38 | 0.604 36 | 0.541 46 | 0.934 74 | 0.532 23 | 0.269 40 | 0.552 58 | 0.777 40 | 0.645 56 | 0.793 19 | 0.640 25 | 0.913 24 | 0.824 36 | 0.671 31 |
|
VI-PointConv | | 0.676 41 | 0.770 43 | 0.754 43 | 0.783 37 | 0.621 47 | 0.814 46 | 0.552 7 | 0.758 42 | 0.571 51 | 0.557 42 | 0.954 24 | 0.529 24 | 0.268 42 | 0.530 64 | 0.682 57 | 0.675 40 | 0.719 53 | 0.603 41 | 0.888 42 | 0.833 32 | 0.665 32 |
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions. |
INS-Conv-semantic | | 0.717 25 | 0.751 50 | 0.759 39 | 0.812 21 | 0.704 25 | 0.868 4 | 0.537 12 | 0.842 17 | 0.609 33 | 0.608 27 | 0.953 27 | 0.534 22 | 0.293 25 | 0.616 40 | 0.864 15 | 0.719 25 | 0.793 19 | 0.640 25 | 0.933 14 | 0.845 30 | 0.663 33 |
|
FusionNet | | 0.688 34 | 0.704 66 | 0.741 52 | 0.754 46 | 0.656 35 | 0.829 28 | 0.501 24 | 0.741 49 | 0.609 33 | 0.548 44 | 0.950 36 | 0.522 27 | 0.371 2 | 0.633 35 | 0.756 42 | 0.715 27 | 0.771 30 | 0.623 32 | 0.861 63 | 0.814 42 | 0.658 34 |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 |
PPCNN++ |  | 0.663 46 | 0.746 51 | 0.708 60 | 0.722 50 | 0.638 44 | 0.820 39 | 0.451 45 | 0.566 81 | 0.599 39 | 0.541 46 | 0.950 36 | 0.510 31 | 0.313 17 | 0.648 30 | 0.819 32 | 0.616 64 | 0.682 69 | 0.590 48 | 0.869 59 | 0.810 45 | 0.656 35 |
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access |
VACNN++ | | 0.684 36 | 0.728 60 | 0.757 42 | 0.776 39 | 0.690 27 | 0.804 53 | 0.464 41 | 0.816 26 | 0.577 48 | 0.587 38 | 0.945 50 | 0.508 32 | 0.276 34 | 0.671 25 | 0.710 52 | 0.663 45 | 0.750 44 | 0.589 49 | 0.881 46 | 0.832 33 | 0.653 36 |
|
JSENet |  | 0.699 30 | 0.881 11 | 0.762 37 | 0.821 15 | 0.667 33 | 0.800 56 | 0.522 18 | 0.792 36 | 0.613 29 | 0.607 28 | 0.935 70 | 0.492 35 | 0.205 64 | 0.576 48 | 0.853 22 | 0.691 35 | 0.758 40 | 0.652 20 | 0.872 55 | 0.828 34 | 0.649 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 |
One-Thing-One-Click | | 0.693 32 | 0.743 53 | 0.794 23 | 0.655 73 | 0.684 30 | 0.822 36 | 0.497 29 | 0.719 54 | 0.622 25 | 0.617 22 | 0.977 4 | 0.447 55 | 0.339 6 | 0.750 17 | 0.664 62 | 0.703 31 | 0.790 21 | 0.596 44 | 0.946 4 | 0.855 21 | 0.647 38 |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 |
PointConv |  | 0.666 44 | 0.781 36 | 0.759 39 | 0.699 58 | 0.644 42 | 0.822 36 | 0.475 34 | 0.779 37 | 0.564 54 | 0.504 62 | 0.953 27 | 0.428 62 | 0.203 66 | 0.586 46 | 0.754 43 | 0.661 46 | 0.753 42 | 0.588 50 | 0.902 31 | 0.813 44 | 0.642 39 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 |
SAFNet-seg |  | 0.654 49 | 0.752 49 | 0.734 54 | 0.664 71 | 0.583 60 | 0.815 45 | 0.399 70 | 0.754 44 | 0.639 20 | 0.535 50 | 0.942 59 | 0.470 43 | 0.309 19 | 0.665 26 | 0.539 71 | 0.650 49 | 0.708 58 | 0.635 27 | 0.857 65 | 0.793 56 | 0.642 39 |
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021 |
DCM-Net | | 0.658 47 | 0.778 37 | 0.702 63 | 0.806 27 | 0.619 48 | 0.813 49 | 0.468 38 | 0.693 62 | 0.494 69 | 0.524 54 | 0.941 61 | 0.449 54 | 0.298 23 | 0.510 68 | 0.821 31 | 0.675 40 | 0.727 52 | 0.568 56 | 0.826 72 | 0.803 48 | 0.637 41 |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] |
DenSeR | | 0.628 65 | 0.800 30 | 0.625 86 | 0.719 52 | 0.545 71 | 0.806 51 | 0.445 52 | 0.597 76 | 0.448 83 | 0.519 57 | 0.938 66 | 0.481 38 | 0.328 11 | 0.489 74 | 0.499 78 | 0.657 48 | 0.759 39 | 0.592 47 | 0.881 46 | 0.797 52 | 0.634 42 |
|
KP-FCNN | | 0.684 36 | 0.847 18 | 0.758 41 | 0.784 36 | 0.647 39 | 0.814 46 | 0.473 35 | 0.772 39 | 0.605 35 | 0.594 36 | 0.935 70 | 0.450 53 | 0.181 75 | 0.587 44 | 0.805 35 | 0.690 36 | 0.785 23 | 0.614 36 | 0.882 45 | 0.819 41 | 0.632 43 |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 |
Superpoint Network | | 0.683 39 | 0.851 17 | 0.728 56 | 0.800 32 | 0.653 37 | 0.806 51 | 0.468 38 | 0.804 32 | 0.572 49 | 0.602 31 | 0.946 47 | 0.453 52 | 0.239 54 | 0.519 66 | 0.822 30 | 0.689 38 | 0.762 37 | 0.595 46 | 0.895 37 | 0.827 35 | 0.630 44 |
|
FusionAwareConv | | 0.630 64 | 0.604 84 | 0.741 52 | 0.766 43 | 0.590 56 | 0.747 78 | 0.501 24 | 0.734 51 | 0.503 68 | 0.527 52 | 0.919 84 | 0.454 50 | 0.323 13 | 0.550 61 | 0.420 83 | 0.678 39 | 0.688 67 | 0.544 66 | 0.896 36 | 0.795 53 | 0.627 45 |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 |
RandLA-Net |  | 0.645 50 | 0.778 37 | 0.731 55 | 0.699 58 | 0.577 61 | 0.829 28 | 0.446 50 | 0.736 50 | 0.477 76 | 0.523 56 | 0.945 50 | 0.454 50 | 0.269 40 | 0.484 75 | 0.749 46 | 0.618 62 | 0.738 46 | 0.599 43 | 0.827 71 | 0.792 59 | 0.621 46 |
|
RFCR | | 0.702 28 | 0.889 9 | 0.745 48 | 0.813 20 | 0.672 32 | 0.818 43 | 0.493 31 | 0.815 28 | 0.623 24 | 0.610 25 | 0.947 44 | 0.470 43 | 0.249 50 | 0.594 43 | 0.848 25 | 0.705 30 | 0.779 25 | 0.646 22 | 0.892 39 | 0.823 37 | 0.611 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 |
Supervoxel-CNN | | 0.635 58 | 0.656 75 | 0.711 59 | 0.719 52 | 0.613 49 | 0.757 76 | 0.444 55 | 0.765 41 | 0.534 60 | 0.566 40 | 0.928 78 | 0.478 40 | 0.272 37 | 0.636 32 | 0.531 73 | 0.664 44 | 0.645 79 | 0.508 77 | 0.864 62 | 0.792 59 | 0.611 47 |
|
LAP-D | | 0.594 73 | 0.720 62 | 0.692 71 | 0.637 80 | 0.456 83 | 0.773 70 | 0.391 75 | 0.730 52 | 0.587 43 | 0.445 76 | 0.940 64 | 0.381 77 | 0.288 27 | 0.434 80 | 0.453 81 | 0.591 72 | 0.649 77 | 0.581 52 | 0.777 78 | 0.749 78 | 0.610 49 |
|
MVPNet |  | 0.641 51 | 0.831 22 | 0.715 58 | 0.671 68 | 0.590 56 | 0.781 65 | 0.394 72 | 0.679 64 | 0.642 18 | 0.553 43 | 0.937 67 | 0.462 46 | 0.256 46 | 0.649 29 | 0.406 84 | 0.626 60 | 0.691 66 | 0.666 18 | 0.877 48 | 0.792 59 | 0.608 50 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 |
Feature_GeometricNet |  | 0.690 33 | 0.884 10 | 0.754 43 | 0.795 33 | 0.647 39 | 0.818 43 | 0.422 63 | 0.802 34 | 0.612 30 | 0.604 29 | 0.945 50 | 0.462 46 | 0.189 72 | 0.563 54 | 0.853 22 | 0.726 19 | 0.765 33 | 0.632 28 | 0.904 29 | 0.821 40 | 0.606 51 |
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint |
ROSMRF3D | | 0.673 42 | 0.789 33 | 0.748 45 | 0.763 44 | 0.635 45 | 0.814 46 | 0.407 68 | 0.747 46 | 0.581 47 | 0.573 39 | 0.950 36 | 0.484 37 | 0.271 39 | 0.607 41 | 0.754 43 | 0.649 50 | 0.774 27 | 0.596 44 | 0.883 44 | 0.823 37 | 0.606 51 |
|
HPEIN | | 0.618 68 | 0.729 59 | 0.668 77 | 0.647 76 | 0.597 54 | 0.766 72 | 0.414 65 | 0.680 63 | 0.520 63 | 0.525 53 | 0.946 47 | 0.432 58 | 0.215 60 | 0.493 73 | 0.599 67 | 0.638 57 | 0.617 84 | 0.570 54 | 0.897 35 | 0.806 46 | 0.605 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 |
Feature-Geometry Net |  | 0.685 35 | 0.866 13 | 0.748 45 | 0.819 17 | 0.645 41 | 0.794 59 | 0.450 48 | 0.802 34 | 0.587 43 | 0.604 29 | 0.945 50 | 0.464 45 | 0.201 67 | 0.554 57 | 0.840 27 | 0.723 22 | 0.732 50 | 0.602 42 | 0.907 27 | 0.822 39 | 0.603 54 |
|
CCRFNet | | 0.589 75 | 0.766 45 | 0.659 81 | 0.683 63 | 0.470 82 | 0.740 80 | 0.387 76 | 0.620 75 | 0.490 72 | 0.476 68 | 0.922 82 | 0.355 82 | 0.245 52 | 0.511 67 | 0.511 76 | 0.571 77 | 0.643 80 | 0.493 81 | 0.872 55 | 0.762 73 | 0.600 55 |
|
joint point-based |  | 0.634 59 | 0.614 82 | 0.778 31 | 0.667 70 | 0.633 46 | 0.825 32 | 0.420 64 | 0.804 32 | 0.467 79 | 0.561 41 | 0.951 32 | 0.494 34 | 0.291 26 | 0.566 52 | 0.458 79 | 0.579 76 | 0.764 34 | 0.559 60 | 0.838 69 | 0.814 42 | 0.598 56 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 |
HPGCNN | | 0.656 48 | 0.698 69 | 0.743 50 | 0.650 74 | 0.564 65 | 0.820 39 | 0.505 22 | 0.758 42 | 0.631 23 | 0.479 67 | 0.945 50 | 0.480 39 | 0.226 55 | 0.572 50 | 0.774 41 | 0.690 36 | 0.735 48 | 0.614 36 | 0.853 66 | 0.776 69 | 0.597 57 |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. |
SConv | | 0.636 57 | 0.830 23 | 0.697 67 | 0.752 47 | 0.572 64 | 0.780 67 | 0.445 52 | 0.716 55 | 0.529 61 | 0.530 51 | 0.951 32 | 0.446 56 | 0.170 78 | 0.507 70 | 0.666 61 | 0.636 58 | 0.682 69 | 0.541 69 | 0.886 43 | 0.799 49 | 0.594 58 |
|
DPC | | 0.592 74 | 0.720 62 | 0.700 65 | 0.602 84 | 0.480 79 | 0.762 75 | 0.380 78 | 0.713 58 | 0.585 46 | 0.437 77 | 0.940 64 | 0.369 79 | 0.288 27 | 0.434 80 | 0.509 77 | 0.590 74 | 0.639 82 | 0.567 57 | 0.772 79 | 0.755 76 | 0.592 59 |
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020 |
APCF-Net | | 0.631 61 | 0.742 54 | 0.687 76 | 0.672 66 | 0.557 68 | 0.792 62 | 0.408 66 | 0.665 67 | 0.545 57 | 0.508 59 | 0.952 31 | 0.428 62 | 0.186 73 | 0.634 34 | 0.702 54 | 0.620 61 | 0.706 59 | 0.555 62 | 0.873 53 | 0.798 51 | 0.581 60 |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL |
PointMTL | | 0.632 60 | 0.731 58 | 0.688 74 | 0.675 65 | 0.591 55 | 0.784 64 | 0.444 55 | 0.565 82 | 0.610 31 | 0.492 64 | 0.949 40 | 0.456 49 | 0.254 47 | 0.587 44 | 0.706 53 | 0.599 68 | 0.665 75 | 0.612 39 | 0.868 60 | 0.791 63 | 0.579 61 |
|
PointMRNet | | 0.640 53 | 0.717 64 | 0.701 64 | 0.692 61 | 0.576 62 | 0.801 55 | 0.467 40 | 0.716 55 | 0.563 55 | 0.459 72 | 0.953 27 | 0.429 61 | 0.169 79 | 0.581 47 | 0.854 21 | 0.605 65 | 0.710 55 | 0.550 64 | 0.894 38 | 0.793 56 | 0.575 62 |
|
SIConv | | 0.625 67 | 0.830 23 | 0.694 69 | 0.757 45 | 0.563 66 | 0.772 71 | 0.448 49 | 0.647 70 | 0.520 63 | 0.509 58 | 0.949 40 | 0.431 60 | 0.191 71 | 0.496 72 | 0.614 66 | 0.647 53 | 0.672 73 | 0.535 72 | 0.876 49 | 0.783 65 | 0.571 63 |
|
SALANet | | 0.670 43 | 0.816 27 | 0.770 34 | 0.768 41 | 0.652 38 | 0.807 50 | 0.451 45 | 0.747 46 | 0.659 16 | 0.545 45 | 0.924 80 | 0.473 42 | 0.149 87 | 0.571 51 | 0.811 34 | 0.635 59 | 0.746 45 | 0.623 32 | 0.892 39 | 0.794 54 | 0.570 64 |
|
TextureNet |  | 0.566 79 | 0.672 74 | 0.664 79 | 0.671 68 | 0.494 77 | 0.719 81 | 0.445 52 | 0.678 65 | 0.411 89 | 0.396 82 | 0.935 70 | 0.356 81 | 0.225 56 | 0.412 83 | 0.535 72 | 0.565 78 | 0.636 83 | 0.464 85 | 0.794 77 | 0.680 89 | 0.568 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 |
FPConv |  | 0.639 54 | 0.785 34 | 0.760 38 | 0.713 56 | 0.603 51 | 0.798 57 | 0.392 73 | 0.534 86 | 0.603 37 | 0.524 54 | 0.948 42 | 0.457 48 | 0.250 48 | 0.538 62 | 0.723 50 | 0.598 69 | 0.696 64 | 0.614 36 | 0.872 55 | 0.799 49 | 0.567 66 |
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020 |
PointSPNet | | 0.637 56 | 0.734 57 | 0.692 71 | 0.714 55 | 0.576 62 | 0.797 58 | 0.446 50 | 0.743 48 | 0.598 40 | 0.437 77 | 0.942 59 | 0.403 72 | 0.150 86 | 0.626 37 | 0.800 37 | 0.649 50 | 0.697 63 | 0.557 61 | 0.846 68 | 0.777 68 | 0.563 67 |
|
PanopticFusion-label | | 0.529 83 | 0.491 93 | 0.688 74 | 0.604 83 | 0.386 88 | 0.632 92 | 0.225 97 | 0.705 61 | 0.434 86 | 0.293 92 | 0.815 95 | 0.348 83 | 0.241 53 | 0.499 71 | 0.669 59 | 0.507 82 | 0.649 77 | 0.442 91 | 0.796 76 | 0.602 95 | 0.561 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) |
PointConv-SFPN | | 0.641 51 | 0.776 39 | 0.703 62 | 0.721 51 | 0.557 68 | 0.826 31 | 0.451 45 | 0.672 66 | 0.563 55 | 0.483 66 | 0.943 58 | 0.425 65 | 0.162 82 | 0.644 31 | 0.726 48 | 0.659 47 | 0.709 57 | 0.572 53 | 0.875 50 | 0.786 64 | 0.559 69 |
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PointNet2-SFPN | | 0.631 61 | 0.771 41 | 0.692 71 | 0.672 66 | 0.524 73 | 0.837 20 | 0.440 58 | 0.706 60 | 0.538 59 | 0.446 74 | 0.944 56 | 0.421 67 | 0.219 58 | 0.552 58 | 0.751 45 | 0.591 72 | 0.737 47 | 0.543 68 | 0.901 33 | 0.768 71 | 0.557 70 |
|
SQN_0.1% | | 0.569 78 | 0.676 72 | 0.696 68 | 0.657 72 | 0.497 76 | 0.779 68 | 0.424 62 | 0.548 83 | 0.515 65 | 0.376 84 | 0.902 91 | 0.422 66 | 0.357 4 | 0.379 85 | 0.456 80 | 0.596 71 | 0.659 76 | 0.544 66 | 0.685 88 | 0.665 92 | 0.556 71 |
|
GMLPs | | 0.538 82 | 0.495 92 | 0.693 70 | 0.647 76 | 0.471 81 | 0.793 60 | 0.300 87 | 0.477 88 | 0.505 67 | 0.358 86 | 0.903 90 | 0.327 85 | 0.081 93 | 0.472 76 | 0.529 74 | 0.448 89 | 0.710 55 | 0.509 75 | 0.746 81 | 0.737 80 | 0.554 72 |
|
SegGroup_sem |  | 0.627 66 | 0.818 26 | 0.747 47 | 0.701 57 | 0.602 52 | 0.764 73 | 0.385 77 | 0.629 73 | 0.490 72 | 0.508 59 | 0.931 77 | 0.409 70 | 0.201 67 | 0.564 53 | 0.725 49 | 0.618 62 | 0.692 65 | 0.539 70 | 0.873 53 | 0.794 54 | 0.548 73 |
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022 |
3DMV | | 0.484 88 | 0.484 94 | 0.538 95 | 0.643 78 | 0.424 86 | 0.606 96 | 0.310 85 | 0.574 80 | 0.433 87 | 0.378 83 | 0.796 96 | 0.301 88 | 0.214 61 | 0.537 63 | 0.208 95 | 0.472 88 | 0.507 96 | 0.413 94 | 0.693 87 | 0.602 95 | 0.539 74 |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 |
SD-DETR | | 0.576 77 | 0.746 51 | 0.609 90 | 0.445 95 | 0.517 75 | 0.643 91 | 0.366 79 | 0.714 57 | 0.456 81 | 0.468 70 | 0.870 93 | 0.432 58 | 0.264 43 | 0.558 56 | 0.674 58 | 0.586 75 | 0.688 67 | 0.482 83 | 0.739 83 | 0.733 81 | 0.537 75 |
|
DVVNet | | 0.562 80 | 0.648 76 | 0.700 65 | 0.770 40 | 0.586 59 | 0.687 85 | 0.333 84 | 0.650 68 | 0.514 66 | 0.475 69 | 0.906 88 | 0.359 80 | 0.223 57 | 0.340 87 | 0.442 82 | 0.422 91 | 0.668 74 | 0.501 78 | 0.708 86 | 0.779 66 | 0.534 76 |
|
SPH3D-GCN |  | 0.610 69 | 0.858 16 | 0.772 33 | 0.489 91 | 0.532 72 | 0.792 62 | 0.404 69 | 0.643 72 | 0.570 52 | 0.507 61 | 0.935 70 | 0.414 69 | 0.046 96 | 0.510 68 | 0.702 54 | 0.602 67 | 0.705 60 | 0.549 65 | 0.859 64 | 0.773 70 | 0.534 76 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 |
ROSMRF | | 0.580 76 | 0.772 40 | 0.707 61 | 0.681 64 | 0.563 66 | 0.764 73 | 0.362 80 | 0.515 87 | 0.465 80 | 0.465 71 | 0.936 69 | 0.427 64 | 0.207 62 | 0.438 78 | 0.577 68 | 0.536 80 | 0.675 72 | 0.486 82 | 0.723 85 | 0.779 66 | 0.524 78 |
|
dtc_net | | 0.596 72 | 0.683 70 | 0.725 57 | 0.715 54 | 0.549 70 | 0.803 54 | 0.444 55 | 0.647 70 | 0.493 70 | 0.495 63 | 0.941 61 | 0.409 70 | 0.000 100 | 0.424 82 | 0.544 70 | 0.598 69 | 0.703 62 | 0.522 74 | 0.912 25 | 0.792 59 | 0.520 79 |
|
Pointnet++ & Feature |  | 0.557 81 | 0.735 56 | 0.661 80 | 0.686 62 | 0.491 78 | 0.744 79 | 0.392 73 | 0.539 84 | 0.451 82 | 0.375 85 | 0.946 47 | 0.376 78 | 0.205 64 | 0.403 84 | 0.356 87 | 0.553 79 | 0.643 80 | 0.497 79 | 0.824 73 | 0.756 75 | 0.515 80 |
|
Online SegFusion | | 0.515 85 | 0.607 83 | 0.644 84 | 0.579 86 | 0.434 85 | 0.630 93 | 0.353 81 | 0.628 74 | 0.440 84 | 0.410 80 | 0.762 98 | 0.307 87 | 0.167 80 | 0.520 65 | 0.403 85 | 0.516 81 | 0.565 87 | 0.447 89 | 0.678 89 | 0.701 86 | 0.514 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 |
PCNN | | 0.498 87 | 0.559 87 | 0.644 84 | 0.560 88 | 0.420 87 | 0.711 83 | 0.229 95 | 0.414 89 | 0.436 85 | 0.352 88 | 0.941 61 | 0.324 86 | 0.155 84 | 0.238 94 | 0.387 86 | 0.493 83 | 0.529 93 | 0.509 75 | 0.813 75 | 0.751 77 | 0.504 82 |
|
3DSM_DMMF | | 0.631 61 | 0.626 79 | 0.745 48 | 0.801 31 | 0.607 50 | 0.751 77 | 0.506 21 | 0.729 53 | 0.565 53 | 0.491 65 | 0.866 94 | 0.434 57 | 0.197 70 | 0.595 42 | 0.630 64 | 0.709 29 | 0.705 60 | 0.560 58 | 0.875 50 | 0.740 79 | 0.491 83 |
|
AttAN | | 0.609 70 | 0.760 46 | 0.667 78 | 0.649 75 | 0.521 74 | 0.793 60 | 0.457 44 | 0.648 69 | 0.528 62 | 0.434 79 | 0.947 44 | 0.401 73 | 0.153 85 | 0.454 77 | 0.721 51 | 0.648 52 | 0.717 54 | 0.536 71 | 0.904 29 | 0.765 72 | 0.485 84 |
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020 |
wsss-transformer | | 0.600 71 | 0.634 78 | 0.743 50 | 0.697 60 | 0.601 53 | 0.781 65 | 0.437 60 | 0.585 79 | 0.493 70 | 0.446 74 | 0.933 75 | 0.394 74 | 0.011 98 | 0.654 28 | 0.661 63 | 0.603 66 | 0.733 49 | 0.526 73 | 0.832 70 | 0.761 74 | 0.480 85 |
|
3DMV, FTSDF | | 0.501 86 | 0.558 88 | 0.608 91 | 0.424 97 | 0.478 80 | 0.690 84 | 0.246 93 | 0.586 78 | 0.468 78 | 0.450 73 | 0.911 86 | 0.394 74 | 0.160 83 | 0.438 78 | 0.212 94 | 0.432 90 | 0.541 92 | 0.475 84 | 0.742 82 | 0.727 82 | 0.477 86 |
|
PointCNN with RGB |  | 0.458 89 | 0.577 86 | 0.611 89 | 0.356 99 | 0.321 96 | 0.715 82 | 0.299 89 | 0.376 93 | 0.328 96 | 0.319 90 | 0.944 56 | 0.285 90 | 0.164 81 | 0.216 97 | 0.229 92 | 0.484 85 | 0.545 91 | 0.456 87 | 0.755 80 | 0.709 85 | 0.475 87 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 |
PD-Net | | 0.638 55 | 0.797 31 | 0.769 35 | 0.641 79 | 0.590 56 | 0.820 39 | 0.461 42 | 0.537 85 | 0.637 21 | 0.536 49 | 0.947 44 | 0.388 76 | 0.206 63 | 0.656 27 | 0.668 60 | 0.647 53 | 0.732 50 | 0.585 51 | 0.868 60 | 0.793 56 | 0.473 88 |
|
PNET2 | | 0.442 92 | 0.548 89 | 0.548 94 | 0.597 85 | 0.363 92 | 0.628 94 | 0.300 87 | 0.292 94 | 0.374 91 | 0.307 91 | 0.881 92 | 0.268 93 | 0.186 73 | 0.238 94 | 0.204 96 | 0.407 92 | 0.506 97 | 0.449 88 | 0.667 90 | 0.620 94 | 0.462 89 |
|
SurfaceConvPF | | 0.442 92 | 0.505 91 | 0.622 88 | 0.380 98 | 0.342 94 | 0.654 88 | 0.227 96 | 0.397 91 | 0.367 92 | 0.276 94 | 0.924 80 | 0.240 95 | 0.198 69 | 0.359 86 | 0.262 90 | 0.366 93 | 0.581 85 | 0.435 92 | 0.640 91 | 0.668 90 | 0.398 90 |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. |
ScanNet+FTSDF | | 0.383 97 | 0.297 99 | 0.491 97 | 0.432 96 | 0.358 93 | 0.612 95 | 0.274 91 | 0.116 98 | 0.411 89 | 0.265 95 | 0.904 89 | 0.229 96 | 0.079 94 | 0.250 92 | 0.185 97 | 0.320 97 | 0.510 94 | 0.385 96 | 0.548 96 | 0.597 98 | 0.394 91 |
|
DGCNN_reproduce |  | 0.446 91 | 0.474 95 | 0.623 87 | 0.463 93 | 0.366 91 | 0.651 89 | 0.310 85 | 0.389 92 | 0.349 94 | 0.330 89 | 0.937 67 | 0.271 92 | 0.126 89 | 0.285 90 | 0.224 93 | 0.350 96 | 0.577 86 | 0.445 90 | 0.625 92 | 0.723 83 | 0.394 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 |
3DWSSS | | 0.425 95 | 0.525 90 | 0.647 82 | 0.522 89 | 0.324 95 | 0.488 99 | 0.077 100 | 0.712 59 | 0.353 93 | 0.401 81 | 0.636 100 | 0.281 91 | 0.176 76 | 0.340 87 | 0.565 69 | 0.175 100 | 0.551 90 | 0.398 95 | 0.370 100 | 0.602 95 | 0.361 93 |
|
Tangent Convolutions |  | 0.438 94 | 0.437 97 | 0.646 83 | 0.474 92 | 0.369 90 | 0.645 90 | 0.353 81 | 0.258 96 | 0.282 98 | 0.279 93 | 0.918 85 | 0.298 89 | 0.147 88 | 0.283 91 | 0.294 89 | 0.487 84 | 0.562 88 | 0.427 93 | 0.619 93 | 0.633 93 | 0.352 94 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 |
subcloud_weak | | 0.516 84 | 0.676 72 | 0.591 93 | 0.609 81 | 0.442 84 | 0.774 69 | 0.335 83 | 0.597 76 | 0.422 88 | 0.357 87 | 0.932 76 | 0.341 84 | 0.094 92 | 0.298 89 | 0.528 75 | 0.473 87 | 0.676 71 | 0.495 80 | 0.602 94 | 0.721 84 | 0.349 95 |
|
SSC-UNet |  | 0.308 99 | 0.353 98 | 0.290 100 | 0.278 100 | 0.166 100 | 0.553 97 | 0.169 99 | 0.286 95 | 0.147 100 | 0.148 100 | 0.908 87 | 0.182 99 | 0.064 95 | 0.023 100 | 0.018 101 | 0.354 95 | 0.363 98 | 0.345 98 | 0.546 98 | 0.685 88 | 0.278 96 |
|
SPLAT Net |  | 0.393 96 | 0.472 96 | 0.511 96 | 0.606 82 | 0.311 97 | 0.656 87 | 0.245 94 | 0.405 90 | 0.328 96 | 0.197 98 | 0.927 79 | 0.227 97 | 0.000 100 | 0.001 101 | 0.249 91 | 0.271 99 | 0.510 94 | 0.383 97 | 0.593 95 | 0.699 87 | 0.267 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 |
PointNet++ |  | 0.339 98 | 0.584 85 | 0.478 98 | 0.458 94 | 0.256 99 | 0.360 100 | 0.250 92 | 0.247 97 | 0.278 99 | 0.261 96 | 0.677 99 | 0.183 98 | 0.117 90 | 0.212 98 | 0.145 99 | 0.364 94 | 0.346 100 | 0.232 100 | 0.548 96 | 0.523 99 | 0.252 98 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. |
FCPN |  | 0.447 90 | 0.679 71 | 0.604 92 | 0.578 87 | 0.380 89 | 0.682 86 | 0.291 90 | 0.106 99 | 0.483 75 | 0.258 97 | 0.920 83 | 0.258 94 | 0.025 97 | 0.231 96 | 0.325 88 | 0.480 86 | 0.560 89 | 0.463 86 | 0.725 84 | 0.666 91 | 0.231 99 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 |
ScanNet |  | 0.306 100 | 0.203 100 | 0.366 99 | 0.501 90 | 0.311 97 | 0.524 98 | 0.211 98 | 0.002 101 | 0.342 95 | 0.189 99 | 0.786 97 | 0.145 100 | 0.102 91 | 0.245 93 | 0.152 98 | 0.318 98 | 0.348 99 | 0.300 99 | 0.460 99 | 0.437 100 | 0.182 100 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 |
ERROR | | 0.054 101 | 0.000 101 | 0.041 101 | 0.172 101 | 0.030 101 | 0.062 101 | 0.001 101 | 0.035 100 | 0.004 101 | 0.051 101 | 0.143 101 | 0.019 101 | 0.003 99 | 0.041 99 | 0.050 100 | 0.003 101 | 0.054 101 | 0.018 101 | 0.005 101 | 0.264 101 | 0.082 101 |
|