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