Mix3D |  | 0.781 1 | 0.964 1 | 0.855 1 | 0.843 10 | 0.781 1 | 0.858 7 | 0.575 2 | 0.831 15 | 0.685 5 | 0.714 1 | 0.979 1 | 0.594 3 | 0.310 15 | 0.801 1 | 0.892 7 | 0.841 2 | 0.819 3 | 0.723 2 | 0.940 7 | 0.887 1 | 0.725 9 |
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral) |
IPCA | | 0.731 14 | 0.890 5 | 0.837 3 | 0.864 2 | 0.726 13 | 0.873 2 | 0.530 9 | 0.824 17 | 0.489 65 | 0.647 8 | 0.978 2 | 0.609 2 | 0.336 6 | 0.624 30 | 0.733 39 | 0.758 7 | 0.776 23 | 0.570 45 | 0.949 2 | 0.877 5 | 0.728 5 |
|
OccuSeg+Semantic | | 0.764 2 | 0.758 40 | 0.796 15 | 0.839 11 | 0.746 7 | 0.907 1 | 0.562 3 | 0.850 10 | 0.680 6 | 0.672 5 | 0.978 2 | 0.610 1 | 0.335 7 | 0.777 3 | 0.819 26 | 0.847 1 | 0.830 1 | 0.691 6 | 0.972 1 | 0.885 2 | 0.727 7 |
|
One-Thing-One-Click | | 0.693 23 | 0.743 46 | 0.794 17 | 0.655 65 | 0.684 22 | 0.822 26 | 0.497 20 | 0.719 45 | 0.622 19 | 0.617 15 | 0.977 4 | 0.447 47 | 0.339 5 | 0.750 12 | 0.664 54 | 0.703 24 | 0.790 19 | 0.596 34 | 0.946 4 | 0.855 15 | 0.647 29 |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 |
PointConvFormer | | 0.749 5 | 0.793 26 | 0.790 18 | 0.807 20 | 0.750 6 | 0.856 8 | 0.524 10 | 0.881 6 | 0.588 32 | 0.642 12 | 0.977 4 | 0.591 5 | 0.274 29 | 0.781 2 | 0.929 1 | 0.804 3 | 0.796 14 | 0.642 16 | 0.947 3 | 0.885 2 | 0.715 13 |
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution. |
One Thing One Click | | 0.701 20 | 0.825 20 | 0.796 15 | 0.723 41 | 0.716 15 | 0.832 18 | 0.433 51 | 0.816 18 | 0.634 16 | 0.609 19 | 0.969 6 | 0.418 60 | 0.344 4 | 0.559 47 | 0.833 23 | 0.715 20 | 0.808 7 | 0.560 49 | 0.902 22 | 0.847 19 | 0.680 20 |
|
PointFormerV2 | | 0.752 4 | 0.742 47 | 0.809 10 | 0.872 1 | 0.758 3 | 0.860 6 | 0.552 4 | 0.891 5 | 0.610 22 | 0.687 2 | 0.960 7 | 0.559 12 | 0.304 18 | 0.766 7 | 0.926 2 | 0.767 5 | 0.797 13 | 0.644 15 | 0.942 5 | 0.876 7 | 0.722 11 |
|
Retro-FPN | | 0.744 11 | 0.842 16 | 0.800 13 | 0.767 34 | 0.740 9 | 0.836 16 | 0.541 6 | 0.914 1 | 0.672 8 | 0.626 14 | 0.958 8 | 0.552 13 | 0.272 30 | 0.777 3 | 0.886 9 | 0.696 26 | 0.801 10 | 0.674 9 | 0.941 6 | 0.858 12 | 0.717 12 |
|
O-CNN |  | 0.762 3 | 0.924 2 | 0.823 4 | 0.844 9 | 0.770 2 | 0.852 9 | 0.577 1 | 0.847 11 | 0.711 1 | 0.640 13 | 0.958 8 | 0.592 4 | 0.217 50 | 0.762 9 | 0.888 8 | 0.758 7 | 0.813 5 | 0.726 1 | 0.932 13 | 0.868 8 | 0.744 3 |
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 |
BPNet |  | 0.749 5 | 0.909 3 | 0.818 7 | 0.811 17 | 0.752 5 | 0.839 13 | 0.485 24 | 0.842 13 | 0.673 7 | 0.644 10 | 0.957 10 | 0.528 19 | 0.305 17 | 0.773 5 | 0.859 13 | 0.788 4 | 0.818 4 | 0.693 5 | 0.916 15 | 0.856 14 | 0.723 10 |
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral) |
PicassoNet-II |  | 0.696 22 | 0.704 57 | 0.790 18 | 0.787 26 | 0.709 16 | 0.837 14 | 0.459 34 | 0.815 20 | 0.543 49 | 0.615 16 | 0.956 11 | 0.529 17 | 0.250 40 | 0.551 52 | 0.790 31 | 0.703 24 | 0.799 12 | 0.619 25 | 0.908 17 | 0.848 18 | 0.700 16 |
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes. |
MSP | | 0.748 7 | 0.623 71 | 0.804 11 | 0.859 3 | 0.745 8 | 0.824 24 | 0.501 16 | 0.912 2 | 0.690 4 | 0.685 3 | 0.956 11 | 0.567 9 | 0.320 12 | 0.768 6 | 0.918 3 | 0.720 16 | 0.802 9 | 0.676 8 | 0.921 14 | 0.881 4 | 0.779 1 |
|
StratifiedFormer |  | 0.747 8 | 0.901 4 | 0.803 12 | 0.845 8 | 0.757 4 | 0.846 11 | 0.512 13 | 0.825 16 | 0.696 3 | 0.645 9 | 0.956 11 | 0.576 7 | 0.262 37 | 0.744 14 | 0.861 12 | 0.742 9 | 0.770 27 | 0.705 3 | 0.899 25 | 0.860 11 | 0.734 4 |
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 |
VMNet |  | 0.746 9 | 0.870 9 | 0.838 2 | 0.858 4 | 0.729 12 | 0.850 10 | 0.501 16 | 0.874 7 | 0.587 33 | 0.658 7 | 0.956 11 | 0.564 10 | 0.299 19 | 0.765 8 | 0.900 5 | 0.716 19 | 0.812 6 | 0.631 21 | 0.939 8 | 0.858 12 | 0.709 14 |
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) |
SparseConvNet | | 0.725 15 | 0.647 67 | 0.821 5 | 0.846 7 | 0.721 14 | 0.869 3 | 0.533 8 | 0.754 35 | 0.603 28 | 0.614 17 | 0.955 15 | 0.572 8 | 0.325 10 | 0.710 16 | 0.870 10 | 0.724 14 | 0.823 2 | 0.628 22 | 0.934 11 | 0.865 10 | 0.683 19 |
|
EQ-Net | | 0.743 12 | 0.620 72 | 0.799 14 | 0.849 5 | 0.730 11 | 0.822 26 | 0.493 22 | 0.897 4 | 0.664 9 | 0.681 4 | 0.955 15 | 0.562 11 | 0.378 1 | 0.760 10 | 0.903 4 | 0.738 10 | 0.801 10 | 0.673 10 | 0.907 18 | 0.877 5 | 0.745 2 |
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022 |
VI-PointConv | | 0.676 32 | 0.770 36 | 0.754 34 | 0.783 29 | 0.621 38 | 0.814 36 | 0.552 4 | 0.758 33 | 0.571 41 | 0.557 33 | 0.954 17 | 0.529 17 | 0.268 35 | 0.530 56 | 0.682 49 | 0.675 32 | 0.719 44 | 0.603 31 | 0.888 34 | 0.833 23 | 0.665 22 |
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions. |
MatchingNet | | 0.724 16 | 0.812 23 | 0.812 9 | 0.810 18 | 0.735 10 | 0.834 17 | 0.495 21 | 0.860 8 | 0.572 39 | 0.602 24 | 0.954 17 | 0.512 22 | 0.280 26 | 0.757 11 | 0.845 21 | 0.725 13 | 0.780 21 | 0.606 30 | 0.937 9 | 0.851 17 | 0.700 16 |
|
INS-Conv-semantic | | 0.717 17 | 0.751 43 | 0.759 30 | 0.812 16 | 0.704 18 | 0.868 4 | 0.537 7 | 0.842 13 | 0.609 24 | 0.608 20 | 0.953 19 | 0.534 15 | 0.293 21 | 0.616 31 | 0.864 11 | 0.719 18 | 0.793 17 | 0.640 17 | 0.933 12 | 0.845 21 | 0.663 23 |
|
PointConv |  | 0.666 35 | 0.781 29 | 0.759 30 | 0.699 49 | 0.644 33 | 0.822 26 | 0.475 25 | 0.779 28 | 0.564 44 | 0.504 53 | 0.953 19 | 0.428 54 | 0.203 57 | 0.586 38 | 0.754 35 | 0.661 38 | 0.753 33 | 0.588 41 | 0.902 22 | 0.813 35 | 0.642 30 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 |
PointMRNet | | 0.640 44 | 0.717 56 | 0.701 55 | 0.692 52 | 0.576 53 | 0.801 45 | 0.467 31 | 0.716 46 | 0.563 45 | 0.459 63 | 0.953 19 | 0.429 53 | 0.169 70 | 0.581 39 | 0.854 15 | 0.605 57 | 0.710 47 | 0.550 55 | 0.894 29 | 0.793 48 | 0.575 54 |
|
APCF-Net | | 0.631 53 | 0.742 47 | 0.687 67 | 0.672 57 | 0.557 60 | 0.792 54 | 0.408 57 | 0.665 59 | 0.545 48 | 0.508 50 | 0.952 22 | 0.428 54 | 0.186 64 | 0.634 26 | 0.702 46 | 0.620 52 | 0.706 51 | 0.555 53 | 0.873 46 | 0.798 42 | 0.581 52 |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL |
PointASNL |  | 0.666 35 | 0.703 59 | 0.781 22 | 0.751 40 | 0.655 27 | 0.830 19 | 0.471 27 | 0.769 31 | 0.474 68 | 0.537 39 | 0.951 23 | 0.475 32 | 0.279 27 | 0.635 25 | 0.698 48 | 0.675 32 | 0.751 34 | 0.553 54 | 0.816 66 | 0.806 37 | 0.703 15 |
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020 |
SConv | | 0.636 48 | 0.830 18 | 0.697 58 | 0.752 39 | 0.572 56 | 0.780 59 | 0.445 43 | 0.716 46 | 0.529 52 | 0.530 42 | 0.951 23 | 0.446 48 | 0.170 69 | 0.507 62 | 0.666 53 | 0.636 49 | 0.682 60 | 0.541 61 | 0.886 35 | 0.799 40 | 0.594 50 |
|
MinkowskiNet |  | 0.736 13 | 0.859 12 | 0.818 7 | 0.832 12 | 0.709 16 | 0.840 12 | 0.521 12 | 0.853 9 | 0.660 11 | 0.643 11 | 0.951 23 | 0.544 14 | 0.286 25 | 0.731 15 | 0.893 6 | 0.675 32 | 0.772 25 | 0.683 7 | 0.874 45 | 0.852 16 | 0.727 7 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |
joint point-based |  | 0.634 50 | 0.614 73 | 0.778 23 | 0.667 61 | 0.633 37 | 0.825 23 | 0.420 54 | 0.804 23 | 0.467 70 | 0.561 32 | 0.951 23 | 0.494 26 | 0.291 22 | 0.566 44 | 0.458 70 | 0.579 67 | 0.764 29 | 0.559 51 | 0.838 60 | 0.814 33 | 0.598 48 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 |
ROSMRF3D | | 0.673 33 | 0.789 27 | 0.748 36 | 0.763 36 | 0.635 36 | 0.814 36 | 0.407 59 | 0.747 37 | 0.581 37 | 0.573 30 | 0.950 27 | 0.484 28 | 0.271 32 | 0.607 33 | 0.754 35 | 0.649 42 | 0.774 24 | 0.596 34 | 0.883 36 | 0.823 28 | 0.606 43 |
|
FusionNet | | 0.688 26 | 0.704 57 | 0.741 43 | 0.754 38 | 0.656 26 | 0.829 20 | 0.501 16 | 0.741 40 | 0.609 24 | 0.548 35 | 0.950 27 | 0.522 20 | 0.371 2 | 0.633 27 | 0.756 34 | 0.715 20 | 0.771 26 | 0.623 23 | 0.861 55 | 0.814 33 | 0.658 24 |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 |
PPCNN++ |  | 0.663 37 | 0.746 44 | 0.708 51 | 0.722 42 | 0.638 35 | 0.820 29 | 0.451 36 | 0.566 73 | 0.599 30 | 0.541 37 | 0.950 27 | 0.510 23 | 0.313 14 | 0.648 22 | 0.819 26 | 0.616 56 | 0.682 60 | 0.590 39 | 0.869 51 | 0.810 36 | 0.656 25 |
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access |
SIConv | | 0.625 59 | 0.830 18 | 0.694 60 | 0.757 37 | 0.563 58 | 0.772 63 | 0.448 40 | 0.647 62 | 0.520 54 | 0.509 49 | 0.949 30 | 0.431 52 | 0.191 62 | 0.496 64 | 0.614 58 | 0.647 45 | 0.672 65 | 0.535 64 | 0.876 42 | 0.783 56 | 0.571 55 |
|
MCCNN |  | 0.633 51 | 0.866 10 | 0.731 46 | 0.771 31 | 0.576 53 | 0.809 40 | 0.410 56 | 0.684 54 | 0.497 60 | 0.491 55 | 0.949 30 | 0.466 36 | 0.105 83 | 0.581 39 | 0.646 56 | 0.620 52 | 0.680 62 | 0.542 60 | 0.817 65 | 0.795 44 | 0.618 38 |
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018 |
PointMTL | | 0.632 52 | 0.731 51 | 0.688 65 | 0.675 56 | 0.591 46 | 0.784 56 | 0.444 46 | 0.565 74 | 0.610 22 | 0.492 54 | 0.949 30 | 0.456 41 | 0.254 39 | 0.587 36 | 0.706 45 | 0.599 60 | 0.665 67 | 0.612 29 | 0.868 52 | 0.791 54 | 0.579 53 |
|
CU-Hybrid Net | | 0.693 23 | 0.596 76 | 0.789 20 | 0.803 22 | 0.677 23 | 0.800 46 | 0.469 28 | 0.846 12 | 0.554 47 | 0.591 27 | 0.948 33 | 0.500 25 | 0.316 13 | 0.609 32 | 0.847 20 | 0.732 11 | 0.808 7 | 0.593 37 | 0.894 29 | 0.839 22 | 0.652 27 |
|
FPConv |  | 0.639 45 | 0.785 28 | 0.760 29 | 0.713 47 | 0.603 42 | 0.798 49 | 0.392 64 | 0.534 78 | 0.603 28 | 0.524 45 | 0.948 33 | 0.457 40 | 0.250 40 | 0.538 54 | 0.723 42 | 0.598 61 | 0.696 55 | 0.614 26 | 0.872 48 | 0.799 40 | 0.567 58 |
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020 |
Virtual MVFusion | | 0.746 9 | 0.771 34 | 0.819 6 | 0.848 6 | 0.702 19 | 0.865 5 | 0.397 62 | 0.899 3 | 0.699 2 | 0.664 6 | 0.948 33 | 0.588 6 | 0.330 8 | 0.746 13 | 0.851 18 | 0.764 6 | 0.796 14 | 0.704 4 | 0.935 10 | 0.866 9 | 0.728 5 |
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020 |
AttAN | | 0.609 62 | 0.760 39 | 0.667 69 | 0.649 67 | 0.521 65 | 0.793 52 | 0.457 35 | 0.648 61 | 0.528 53 | 0.434 70 | 0.947 36 | 0.401 64 | 0.153 76 | 0.454 69 | 0.721 43 | 0.648 44 | 0.717 45 | 0.536 63 | 0.904 20 | 0.765 63 | 0.485 75 |
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020 |
RFCR | | 0.702 19 | 0.889 6 | 0.745 39 | 0.813 15 | 0.672 24 | 0.818 33 | 0.493 22 | 0.815 20 | 0.623 18 | 0.610 18 | 0.947 36 | 0.470 34 | 0.249 42 | 0.594 35 | 0.848 19 | 0.705 23 | 0.779 22 | 0.646 14 | 0.892 31 | 0.823 28 | 0.611 39 |
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 |
PD-Net | | 0.638 46 | 0.797 25 | 0.769 27 | 0.641 71 | 0.590 47 | 0.820 29 | 0.461 33 | 0.537 77 | 0.637 15 | 0.536 40 | 0.947 36 | 0.388 67 | 0.206 54 | 0.656 19 | 0.668 52 | 0.647 45 | 0.732 41 | 0.585 42 | 0.868 52 | 0.793 48 | 0.473 79 |
|
Pointnet++ & Feature |  | 0.557 72 | 0.735 49 | 0.661 71 | 0.686 53 | 0.491 69 | 0.744 71 | 0.392 64 | 0.539 76 | 0.451 74 | 0.375 76 | 0.946 39 | 0.376 69 | 0.205 55 | 0.403 75 | 0.356 79 | 0.553 71 | 0.643 72 | 0.497 70 | 0.824 64 | 0.756 66 | 0.515 71 |
|
Superpoint Network | | 0.683 30 | 0.851 14 | 0.728 48 | 0.800 24 | 0.653 28 | 0.806 42 | 0.468 29 | 0.804 23 | 0.572 39 | 0.602 24 | 0.946 39 | 0.453 44 | 0.239 45 | 0.519 58 | 0.822 24 | 0.689 30 | 0.762 30 | 0.595 36 | 0.895 28 | 0.827 26 | 0.630 35 |
|
HPEIN | | 0.618 60 | 0.729 52 | 0.668 68 | 0.647 68 | 0.597 45 | 0.766 64 | 0.414 55 | 0.680 55 | 0.520 54 | 0.525 44 | 0.946 39 | 0.432 50 | 0.215 51 | 0.493 65 | 0.599 59 | 0.638 48 | 0.617 76 | 0.570 45 | 0.897 26 | 0.806 37 | 0.605 45 |
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_GeometricNet |  | 0.690 25 | 0.884 7 | 0.754 34 | 0.795 25 | 0.647 30 | 0.818 33 | 0.422 53 | 0.802 25 | 0.612 21 | 0.604 22 | 0.945 42 | 0.462 38 | 0.189 63 | 0.563 46 | 0.853 16 | 0.726 12 | 0.765 28 | 0.632 20 | 0.904 20 | 0.821 31 | 0.606 43 |
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint |
VACNN++ | | 0.684 28 | 0.728 53 | 0.757 33 | 0.776 30 | 0.690 20 | 0.804 44 | 0.464 32 | 0.816 18 | 0.577 38 | 0.587 29 | 0.945 42 | 0.508 24 | 0.276 28 | 0.671 17 | 0.710 44 | 0.663 37 | 0.750 35 | 0.589 40 | 0.881 39 | 0.832 24 | 0.653 26 |
|
contrastBoundary |  | 0.705 18 | 0.769 37 | 0.775 24 | 0.809 19 | 0.687 21 | 0.820 29 | 0.439 49 | 0.812 22 | 0.661 10 | 0.591 27 | 0.945 42 | 0.515 21 | 0.171 68 | 0.633 27 | 0.856 14 | 0.720 16 | 0.796 14 | 0.668 11 | 0.889 33 | 0.847 19 | 0.689 18 |
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022 |
RandLA-Net |  | 0.645 41 | 0.778 30 | 0.731 46 | 0.699 49 | 0.577 52 | 0.829 20 | 0.446 41 | 0.736 41 | 0.477 67 | 0.523 47 | 0.945 42 | 0.454 42 | 0.269 33 | 0.484 67 | 0.749 38 | 0.618 54 | 0.738 37 | 0.599 33 | 0.827 62 | 0.792 51 | 0.621 37 |
|
Feature-Geometry Net |  | 0.685 27 | 0.866 10 | 0.748 36 | 0.819 14 | 0.645 32 | 0.794 51 | 0.450 39 | 0.802 25 | 0.587 33 | 0.604 22 | 0.945 42 | 0.464 37 | 0.201 58 | 0.554 49 | 0.840 22 | 0.723 15 | 0.732 41 | 0.602 32 | 0.907 18 | 0.822 30 | 0.603 46 |
|
HPGCNN | | 0.656 39 | 0.698 60 | 0.743 41 | 0.650 66 | 0.564 57 | 0.820 29 | 0.505 15 | 0.758 33 | 0.631 17 | 0.479 58 | 0.945 42 | 0.480 30 | 0.226 46 | 0.572 42 | 0.774 33 | 0.690 28 | 0.735 39 | 0.614 26 | 0.853 58 | 0.776 60 | 0.597 49 |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. |
PointNet2-SFPN | | 0.631 53 | 0.771 34 | 0.692 62 | 0.672 57 | 0.524 64 | 0.837 14 | 0.440 48 | 0.706 51 | 0.538 50 | 0.446 65 | 0.944 48 | 0.421 59 | 0.219 49 | 0.552 50 | 0.751 37 | 0.591 63 | 0.737 38 | 0.543 59 | 0.901 24 | 0.768 62 | 0.557 62 |
|
PointCNN with RGB |  | 0.458 81 | 0.577 78 | 0.611 81 | 0.356 91 | 0.321 88 | 0.715 74 | 0.299 81 | 0.376 85 | 0.328 88 | 0.319 82 | 0.944 48 | 0.285 82 | 0.164 72 | 0.216 89 | 0.229 84 | 0.484 77 | 0.545 83 | 0.456 79 | 0.755 72 | 0.709 77 | 0.475 78 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 |
PointConv-SFPN | | 0.641 42 | 0.776 32 | 0.703 53 | 0.721 43 | 0.557 60 | 0.826 22 | 0.451 36 | 0.672 58 | 0.563 45 | 0.483 57 | 0.943 50 | 0.425 57 | 0.162 73 | 0.644 23 | 0.726 40 | 0.659 39 | 0.709 49 | 0.572 44 | 0.875 43 | 0.786 55 | 0.559 61 |
|
SAFNet-seg |  | 0.654 40 | 0.752 42 | 0.734 45 | 0.664 62 | 0.583 51 | 0.815 35 | 0.399 61 | 0.754 35 | 0.639 14 | 0.535 41 | 0.942 51 | 0.470 34 | 0.309 16 | 0.665 18 | 0.539 62 | 0.650 41 | 0.708 50 | 0.635 19 | 0.857 57 | 0.793 48 | 0.642 30 |
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021 |
PointSPNet | | 0.637 47 | 0.734 50 | 0.692 62 | 0.714 46 | 0.576 53 | 0.797 50 | 0.446 41 | 0.743 39 | 0.598 31 | 0.437 68 | 0.942 51 | 0.403 63 | 0.150 77 | 0.626 29 | 0.800 30 | 0.649 42 | 0.697 54 | 0.557 52 | 0.846 59 | 0.777 59 | 0.563 59 |
|
DCM-Net | | 0.658 38 | 0.778 30 | 0.702 54 | 0.806 21 | 0.619 39 | 0.813 39 | 0.468 29 | 0.693 53 | 0.494 61 | 0.524 45 | 0.941 53 | 0.449 46 | 0.298 20 | 0.510 60 | 0.821 25 | 0.675 32 | 0.727 43 | 0.568 47 | 0.826 63 | 0.803 39 | 0.637 32 |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] |
PCNN | | 0.498 79 | 0.559 79 | 0.644 76 | 0.560 80 | 0.420 79 | 0.711 75 | 0.229 87 | 0.414 81 | 0.436 77 | 0.352 80 | 0.941 53 | 0.324 78 | 0.155 75 | 0.238 86 | 0.387 78 | 0.493 75 | 0.529 85 | 0.509 66 | 0.813 67 | 0.751 68 | 0.504 73 |
|
DPC | | 0.592 65 | 0.720 54 | 0.700 56 | 0.602 76 | 0.480 70 | 0.762 67 | 0.380 70 | 0.713 49 | 0.585 36 | 0.437 68 | 0.940 55 | 0.369 70 | 0.288 23 | 0.434 72 | 0.509 68 | 0.590 65 | 0.639 74 | 0.567 48 | 0.772 71 | 0.755 67 | 0.592 51 |
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 64 | 0.720 54 | 0.692 62 | 0.637 72 | 0.456 74 | 0.773 62 | 0.391 66 | 0.730 43 | 0.587 33 | 0.445 67 | 0.940 55 | 0.381 68 | 0.288 23 | 0.434 72 | 0.453 72 | 0.591 63 | 0.649 69 | 0.581 43 | 0.777 70 | 0.749 69 | 0.610 41 |
|
PointMRNet-lite | | 0.553 73 | 0.633 69 | 0.648 73 | 0.659 63 | 0.430 77 | 0.800 46 | 0.390 67 | 0.592 69 | 0.454 73 | 0.371 77 | 0.939 57 | 0.368 71 | 0.136 80 | 0.368 77 | 0.448 73 | 0.560 70 | 0.715 46 | 0.486 73 | 0.882 37 | 0.720 76 | 0.462 80 |
|
DenSeR | | 0.628 57 | 0.800 24 | 0.625 78 | 0.719 44 | 0.545 62 | 0.806 42 | 0.445 43 | 0.597 67 | 0.448 75 | 0.519 48 | 0.938 58 | 0.481 29 | 0.328 9 | 0.489 66 | 0.499 69 | 0.657 40 | 0.759 31 | 0.592 38 | 0.881 39 | 0.797 43 | 0.634 33 |
|
DGCNN_reproduce |  | 0.446 83 | 0.474 87 | 0.623 79 | 0.463 85 | 0.366 83 | 0.651 81 | 0.310 77 | 0.389 84 | 0.349 86 | 0.330 81 | 0.937 59 | 0.271 84 | 0.126 81 | 0.285 82 | 0.224 85 | 0.350 88 | 0.577 78 | 0.445 82 | 0.625 84 | 0.723 74 | 0.394 83 |
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 |
MVPNet |  | 0.641 42 | 0.831 17 | 0.715 49 | 0.671 59 | 0.590 47 | 0.781 57 | 0.394 63 | 0.679 56 | 0.642 13 | 0.553 34 | 0.937 59 | 0.462 38 | 0.256 38 | 0.649 21 | 0.406 76 | 0.626 51 | 0.691 57 | 0.666 12 | 0.877 41 | 0.792 51 | 0.608 42 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 |
ROSMRF | | 0.580 67 | 0.772 33 | 0.707 52 | 0.681 55 | 0.563 58 | 0.764 65 | 0.362 72 | 0.515 79 | 0.465 71 | 0.465 62 | 0.936 61 | 0.427 56 | 0.207 53 | 0.438 70 | 0.577 60 | 0.536 72 | 0.675 64 | 0.486 73 | 0.723 77 | 0.779 57 | 0.524 70 |
|
JSENet |  | 0.699 21 | 0.881 8 | 0.762 28 | 0.821 13 | 0.667 25 | 0.800 46 | 0.522 11 | 0.792 27 | 0.613 20 | 0.607 21 | 0.935 62 | 0.492 27 | 0.205 55 | 0.576 41 | 0.853 16 | 0.691 27 | 0.758 32 | 0.652 13 | 0.872 48 | 0.828 25 | 0.649 28 |
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 |
SPH3D-GCN |  | 0.610 61 | 0.858 13 | 0.772 25 | 0.489 83 | 0.532 63 | 0.792 54 | 0.404 60 | 0.643 63 | 0.570 42 | 0.507 52 | 0.935 62 | 0.414 61 | 0.046 89 | 0.510 60 | 0.702 46 | 0.602 59 | 0.705 52 | 0.549 56 | 0.859 56 | 0.773 61 | 0.534 68 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 |
TextureNet |  | 0.566 70 | 0.672 64 | 0.664 70 | 0.671 59 | 0.494 68 | 0.719 73 | 0.445 43 | 0.678 57 | 0.411 81 | 0.396 73 | 0.935 62 | 0.356 73 | 0.225 47 | 0.412 74 | 0.535 63 | 0.565 69 | 0.636 75 | 0.464 77 | 0.794 69 | 0.680 81 | 0.568 57 |
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 |
KP-FCNN | | 0.684 28 | 0.847 15 | 0.758 32 | 0.784 28 | 0.647 30 | 0.814 36 | 0.473 26 | 0.772 30 | 0.605 26 | 0.594 26 | 0.935 62 | 0.450 45 | 0.181 66 | 0.587 36 | 0.805 29 | 0.690 28 | 0.785 20 | 0.614 26 | 0.882 37 | 0.819 32 | 0.632 34 |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 |
PointContrast_LA_SEM | | 0.683 30 | 0.757 41 | 0.784 21 | 0.786 27 | 0.639 34 | 0.824 24 | 0.408 57 | 0.775 29 | 0.604 27 | 0.541 37 | 0.934 66 | 0.532 16 | 0.269 33 | 0.552 50 | 0.777 32 | 0.645 47 | 0.793 17 | 0.640 17 | 0.913 16 | 0.824 27 | 0.671 21 |
|
wsss-transformer | | 0.600 63 | 0.634 68 | 0.743 41 | 0.697 51 | 0.601 44 | 0.781 57 | 0.437 50 | 0.585 71 | 0.493 62 | 0.446 65 | 0.933 67 | 0.394 65 | 0.011 91 | 0.654 20 | 0.661 55 | 0.603 58 | 0.733 40 | 0.526 65 | 0.832 61 | 0.761 65 | 0.480 76 |
|
subcloud_weak | | 0.516 76 | 0.676 62 | 0.591 85 | 0.609 73 | 0.442 75 | 0.774 61 | 0.335 75 | 0.597 67 | 0.422 80 | 0.357 79 | 0.932 68 | 0.341 76 | 0.094 85 | 0.298 81 | 0.528 66 | 0.473 79 | 0.676 63 | 0.495 71 | 0.602 86 | 0.721 75 | 0.349 87 |
|
SegGroup_sem |  | 0.627 58 | 0.818 21 | 0.747 38 | 0.701 48 | 0.602 43 | 0.764 65 | 0.385 69 | 0.629 64 | 0.490 63 | 0.508 50 | 0.931 69 | 0.409 62 | 0.201 58 | 0.564 45 | 0.725 41 | 0.618 54 | 0.692 56 | 0.539 62 | 0.873 46 | 0.794 46 | 0.548 65 |
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022 |
Supervoxel-CNN | | 0.635 49 | 0.656 65 | 0.711 50 | 0.719 44 | 0.613 40 | 0.757 68 | 0.444 46 | 0.765 32 | 0.534 51 | 0.566 31 | 0.928 70 | 0.478 31 | 0.272 30 | 0.636 24 | 0.531 64 | 0.664 36 | 0.645 71 | 0.508 68 | 0.864 54 | 0.792 51 | 0.611 39 |
|
SPLAT Net |  | 0.393 88 | 0.472 88 | 0.511 88 | 0.606 74 | 0.311 89 | 0.656 79 | 0.245 86 | 0.405 82 | 0.328 88 | 0.197 90 | 0.927 71 | 0.227 89 | 0.000 93 | 0.001 93 | 0.249 83 | 0.271 91 | 0.510 86 | 0.383 89 | 0.593 87 | 0.699 79 | 0.267 89 |
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 |
SurfaceConvPF | | 0.442 84 | 0.505 83 | 0.622 80 | 0.380 90 | 0.342 86 | 0.654 80 | 0.227 88 | 0.397 83 | 0.367 84 | 0.276 86 | 0.924 72 | 0.240 87 | 0.198 60 | 0.359 78 | 0.262 82 | 0.366 85 | 0.581 77 | 0.435 84 | 0.640 83 | 0.668 82 | 0.398 82 |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. |
SALANet | | 0.670 34 | 0.816 22 | 0.770 26 | 0.768 33 | 0.652 29 | 0.807 41 | 0.451 36 | 0.747 37 | 0.659 12 | 0.545 36 | 0.924 72 | 0.473 33 | 0.149 78 | 0.571 43 | 0.811 28 | 0.635 50 | 0.746 36 | 0.623 23 | 0.892 31 | 0.794 46 | 0.570 56 |
|
CCRFNet | | 0.589 66 | 0.766 38 | 0.659 72 | 0.683 54 | 0.470 73 | 0.740 72 | 0.387 68 | 0.620 66 | 0.490 63 | 0.476 59 | 0.922 74 | 0.355 74 | 0.245 43 | 0.511 59 | 0.511 67 | 0.571 68 | 0.643 72 | 0.493 72 | 0.872 48 | 0.762 64 | 0.600 47 |
|
FCPN |  | 0.447 82 | 0.679 61 | 0.604 84 | 0.578 79 | 0.380 81 | 0.682 78 | 0.291 82 | 0.106 91 | 0.483 66 | 0.258 89 | 0.920 75 | 0.258 86 | 0.025 90 | 0.231 88 | 0.325 80 | 0.480 78 | 0.560 81 | 0.463 78 | 0.725 76 | 0.666 83 | 0.231 91 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 |
FusionAwareConv | | 0.630 56 | 0.604 75 | 0.741 43 | 0.766 35 | 0.590 47 | 0.747 70 | 0.501 16 | 0.734 42 | 0.503 59 | 0.527 43 | 0.919 76 | 0.454 42 | 0.323 11 | 0.550 53 | 0.420 75 | 0.678 31 | 0.688 58 | 0.544 57 | 0.896 27 | 0.795 44 | 0.627 36 |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 |
Tangent Convolutions |  | 0.438 86 | 0.437 89 | 0.646 75 | 0.474 84 | 0.369 82 | 0.645 82 | 0.353 73 | 0.258 88 | 0.282 90 | 0.279 85 | 0.918 77 | 0.298 81 | 0.147 79 | 0.283 83 | 0.294 81 | 0.487 76 | 0.562 80 | 0.427 85 | 0.619 85 | 0.633 85 | 0.352 86 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 |
3DMV, FTSDF | | 0.501 78 | 0.558 80 | 0.608 83 | 0.424 89 | 0.478 71 | 0.690 76 | 0.246 85 | 0.586 70 | 0.468 69 | 0.450 64 | 0.911 78 | 0.394 65 | 0.160 74 | 0.438 70 | 0.212 86 | 0.432 82 | 0.541 84 | 0.475 76 | 0.742 74 | 0.727 73 | 0.477 77 |
|
SSC-UNet |  | 0.308 91 | 0.353 90 | 0.290 92 | 0.278 92 | 0.166 92 | 0.553 89 | 0.169 91 | 0.286 87 | 0.147 92 | 0.148 92 | 0.908 79 | 0.182 91 | 0.064 88 | 0.023 92 | 0.018 93 | 0.354 87 | 0.363 90 | 0.345 90 | 0.546 90 | 0.685 80 | 0.278 88 |
|
DVVNet | | 0.562 71 | 0.648 66 | 0.700 56 | 0.770 32 | 0.586 50 | 0.687 77 | 0.333 76 | 0.650 60 | 0.514 57 | 0.475 60 | 0.906 80 | 0.359 72 | 0.223 48 | 0.340 79 | 0.442 74 | 0.422 83 | 0.668 66 | 0.501 69 | 0.708 78 | 0.779 57 | 0.534 68 |
|
ScanNet+FTSDF | | 0.383 89 | 0.297 91 | 0.491 89 | 0.432 88 | 0.358 85 | 0.612 87 | 0.274 83 | 0.116 90 | 0.411 81 | 0.265 87 | 0.904 81 | 0.229 88 | 0.079 87 | 0.250 84 | 0.185 89 | 0.320 89 | 0.510 86 | 0.385 88 | 0.548 88 | 0.597 90 | 0.394 83 |
|
GMLPs | | 0.538 74 | 0.495 84 | 0.693 61 | 0.647 68 | 0.471 72 | 0.793 52 | 0.300 79 | 0.477 80 | 0.505 58 | 0.358 78 | 0.903 82 | 0.327 77 | 0.081 86 | 0.472 68 | 0.529 65 | 0.448 81 | 0.710 47 | 0.509 66 | 0.746 73 | 0.737 71 | 0.554 64 |
|
SQN_0.1% | | 0.569 69 | 0.676 62 | 0.696 59 | 0.657 64 | 0.497 67 | 0.779 60 | 0.424 52 | 0.548 75 | 0.515 56 | 0.376 75 | 0.902 83 | 0.422 58 | 0.357 3 | 0.379 76 | 0.456 71 | 0.596 62 | 0.659 68 | 0.544 57 | 0.685 80 | 0.665 84 | 0.556 63 |
|
PNET2 | | 0.442 84 | 0.548 81 | 0.548 86 | 0.597 77 | 0.363 84 | 0.628 86 | 0.300 79 | 0.292 86 | 0.374 83 | 0.307 83 | 0.881 84 | 0.268 85 | 0.186 64 | 0.238 86 | 0.204 88 | 0.407 84 | 0.506 89 | 0.449 80 | 0.667 82 | 0.620 86 | 0.462 80 |
|
SD-DETR | | 0.576 68 | 0.746 44 | 0.609 82 | 0.445 87 | 0.517 66 | 0.643 83 | 0.366 71 | 0.714 48 | 0.456 72 | 0.468 61 | 0.870 85 | 0.432 50 | 0.264 36 | 0.558 48 | 0.674 50 | 0.586 66 | 0.688 58 | 0.482 75 | 0.739 75 | 0.733 72 | 0.537 67 |
|
3DSM_DMMF | | 0.631 53 | 0.626 70 | 0.745 39 | 0.801 23 | 0.607 41 | 0.751 69 | 0.506 14 | 0.729 44 | 0.565 43 | 0.491 55 | 0.866 86 | 0.434 49 | 0.197 61 | 0.595 34 | 0.630 57 | 0.709 22 | 0.705 52 | 0.560 49 | 0.875 43 | 0.740 70 | 0.491 74 |
|
PanopticFusion-label | | 0.529 75 | 0.491 85 | 0.688 65 | 0.604 75 | 0.386 80 | 0.632 84 | 0.225 89 | 0.705 52 | 0.434 78 | 0.293 84 | 0.815 87 | 0.348 75 | 0.241 44 | 0.499 63 | 0.669 51 | 0.507 74 | 0.649 69 | 0.442 83 | 0.796 68 | 0.602 87 | 0.561 60 |
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear) |
3DMV | | 0.484 80 | 0.484 86 | 0.538 87 | 0.643 70 | 0.424 78 | 0.606 88 | 0.310 77 | 0.574 72 | 0.433 79 | 0.378 74 | 0.796 88 | 0.301 80 | 0.214 52 | 0.537 55 | 0.208 87 | 0.472 80 | 0.507 88 | 0.413 86 | 0.693 79 | 0.602 87 | 0.539 66 |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 |
ScanNet |  | 0.306 92 | 0.203 92 | 0.366 91 | 0.501 82 | 0.311 89 | 0.524 90 | 0.211 90 | 0.002 93 | 0.342 87 | 0.189 91 | 0.786 89 | 0.145 92 | 0.102 84 | 0.245 85 | 0.152 90 | 0.318 90 | 0.348 91 | 0.300 91 | 0.460 91 | 0.437 92 | 0.182 92 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 |
Online SegFusion | | 0.515 77 | 0.607 74 | 0.644 76 | 0.579 78 | 0.434 76 | 0.630 85 | 0.353 73 | 0.628 65 | 0.440 76 | 0.410 71 | 0.762 90 | 0.307 79 | 0.167 71 | 0.520 57 | 0.403 77 | 0.516 73 | 0.565 79 | 0.447 81 | 0.678 81 | 0.701 78 | 0.514 72 |
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 |
PointNet++ |  | 0.339 90 | 0.584 77 | 0.478 90 | 0.458 86 | 0.256 91 | 0.360 92 | 0.250 84 | 0.247 89 | 0.278 91 | 0.261 88 | 0.677 91 | 0.183 90 | 0.117 82 | 0.212 90 | 0.145 91 | 0.364 86 | 0.346 92 | 0.232 92 | 0.548 88 | 0.523 91 | 0.252 90 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. |
3DWSSS | | 0.425 87 | 0.525 82 | 0.647 74 | 0.522 81 | 0.324 87 | 0.488 91 | 0.077 92 | 0.712 50 | 0.353 85 | 0.401 72 | 0.636 92 | 0.281 83 | 0.176 67 | 0.340 79 | 0.565 61 | 0.175 92 | 0.551 82 | 0.398 87 | 0.370 92 | 0.602 87 | 0.361 85 |
|
ERROR | | 0.054 93 | 0.000 93 | 0.041 93 | 0.172 93 | 0.030 93 | 0.062 93 | 0.001 93 | 0.035 92 | 0.004 93 | 0.051 93 | 0.143 93 | 0.019 93 | 0.003 92 | 0.041 91 | 0.050 92 | 0.003 93 | 0.054 93 | 0.018 93 | 0.005 93 | 0.264 93 | 0.082 93 |
|