3D Semantic label benchmark
This table lists the benchmark results for the 3D semantic label scenario.
Method | Info | avg iou | bathtub | bed | bookshelf | cabinet | chair | counter | curtain | desk | door | floor | otherfurniture | picture | refrigerator | shower curtain | sink | sofa | table | toilet | wall | window |
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Virtual MVFusion | 0.746 3 | 0.771 24 | 0.819 3 | 0.848 2 | 0.702 8 | 0.865 3 | 0.397 42 | 0.899 1 | 0.699 1 | 0.664 2 | 0.948 20 | 0.588 2 | 0.330 4 | 0.746 5 | 0.851 7 | 0.764 3 | 0.796 7 | 0.704 1 | 0.935 4 | 0.866 2 | 0.728 1 | |
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020 | ||||||||||||||||||||||
MinkowskiNet | ![]() | 0.736 5 | 0.859 7 | 0.818 4 | 0.832 5 | 0.709 7 | 0.840 5 | 0.521 4 | 0.853 4 | 0.660 4 | 0.643 5 | 0.951 10 | 0.544 5 | 0.286 15 | 0.731 6 | 0.893 2 | 0.675 16 | 0.772 13 | 0.683 4 | 0.874 25 | 0.852 6 | 0.727 2 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 | ||||||||||||||||||||||
OccuSeg+Semantic | 0.764 1 | 0.758 28 | 0.796 7 | 0.839 4 | 0.746 2 | 0.907 1 | 0.562 1 | 0.850 5 | 0.680 2 | 0.672 1 | 0.978 1 | 0.610 1 | 0.335 3 | 0.777 1 | 0.819 13 | 0.847 1 | 0.830 1 | 0.691 3 | 0.972 1 | 0.885 1 | 0.727 2 | |
BPNet | 0.749 2 | 0.909 1 | 0.818 4 | 0.811 8 | 0.752 1 | 0.839 6 | 0.485 12 | 0.842 7 | 0.673 3 | 0.644 4 | 0.957 3 | 0.528 7 | 0.305 9 | 0.773 2 | 0.859 4 | 0.788 2 | 0.818 3 | 0.693 2 | 0.916 6 | 0.856 5 | 0.723 4 | |
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 | ||||||||||||||||||||||
VMNet | 0.746 3 | 0.870 5 | 0.838 1 | 0.858 1 | 0.729 4 | 0.850 4 | 0.501 7 | 0.874 2 | 0.587 19 | 0.658 3 | 0.956 4 | 0.564 4 | 0.299 10 | 0.765 3 | 0.900 1 | 0.716 7 | 0.812 4 | 0.631 10 | 0.939 2 | 0.858 4 | 0.709 5 | |
PointASNL | ![]() | 0.666 17 | 0.703 40 | 0.781 11 | 0.751 23 | 0.655 13 | 0.830 10 | 0.471 15 | 0.769 15 | 0.474 47 | 0.537 20 | 0.951 10 | 0.475 17 | 0.279 17 | 0.635 12 | 0.698 32 | 0.675 16 | 0.751 18 | 0.553 35 | 0.816 45 | 0.806 19 | 0.703 6 |
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 7 | 0.812 16 | 0.812 6 | 0.810 9 | 0.735 3 | 0.834 8 | 0.495 10 | 0.860 3 | 0.572 24 | 0.602 10 | 0.954 6 | 0.512 9 | 0.280 16 | 0.757 4 | 0.845 10 | 0.725 5 | 0.780 10 | 0.606 18 | 0.937 3 | 0.851 7 | 0.700 7 | |
SparseConvNet | 0.725 6 | 0.647 46 | 0.821 2 | 0.846 3 | 0.721 5 | 0.869 2 | 0.533 2 | 0.754 18 | 0.603 16 | 0.614 6 | 0.955 5 | 0.572 3 | 0.325 5 | 0.710 7 | 0.870 3 | 0.724 6 | 0.823 2 | 0.628 11 | 0.934 5 | 0.865 3 | 0.683 8 | |
One Thing One Click | 0.701 9 | 0.825 14 | 0.796 7 | 0.723 24 | 0.716 6 | 0.832 9 | 0.433 32 | 0.816 8 | 0.634 8 | 0.609 8 | 0.969 2 | 0.418 40 | 0.344 2 | 0.559 31 | 0.833 11 | 0.715 8 | 0.808 5 | 0.560 30 | 0.902 9 | 0.847 8 | 0.680 9 | |
PointContrast_LA_SEM | 0.683 14 | 0.757 29 | 0.784 10 | 0.786 13 | 0.639 17 | 0.824 15 | 0.408 36 | 0.775 13 | 0.604 15 | 0.541 19 | 0.934 46 | 0.532 6 | 0.269 20 | 0.552 32 | 0.777 18 | 0.645 28 | 0.793 8 | 0.640 8 | 0.913 7 | 0.824 11 | 0.671 10 | |
VACNN++ | 0.664 19 | 0.891 2 | 0.724 29 | 0.680 36 | 0.636 18 | 0.814 20 | 0.438 31 | 0.629 46 | 0.553 32 | 0.537 20 | 0.950 14 | 0.499 11 | 0.247 26 | 0.626 15 | 0.786 17 | 0.666 19 | 0.700 32 | 0.566 28 | 0.860 35 | 0.816 15 | 0.665 11 | |
FusionNet | 0.688 12 | 0.704 39 | 0.741 23 | 0.754 21 | 0.656 12 | 0.829 11 | 0.501 7 | 0.741 23 | 0.609 13 | 0.548 17 | 0.950 14 | 0.522 8 | 0.371 1 | 0.633 14 | 0.756 20 | 0.715 8 | 0.771 14 | 0.623 12 | 0.861 34 | 0.814 16 | 0.658 12 | |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 | ||||||||||||||||||||||
CU-Hybrid Net | 0.693 11 | 0.596 51 | 0.789 9 | 0.803 11 | 0.677 9 | 0.800 28 | 0.469 16 | 0.846 6 | 0.554 31 | 0.591 12 | 0.948 20 | 0.500 10 | 0.316 7 | 0.609 18 | 0.847 9 | 0.732 4 | 0.808 5 | 0.593 21 | 0.894 14 | 0.839 9 | 0.652 13 | |
JSENet | 0.699 10 | 0.881 4 | 0.762 15 | 0.821 6 | 0.667 11 | 0.800 28 | 0.522 3 | 0.792 11 | 0.613 11 | 0.607 9 | 0.935 42 | 0.492 13 | 0.205 36 | 0.576 27 | 0.853 6 | 0.691 12 | 0.758 16 | 0.652 6 | 0.872 28 | 0.828 10 | 0.649 14 | |
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 | ||||||||||||||||||||||
PointConv | ![]() | 0.666 17 | 0.781 19 | 0.759 17 | 0.699 29 | 0.644 16 | 0.822 16 | 0.475 13 | 0.779 12 | 0.564 27 | 0.504 32 | 0.953 7 | 0.428 35 | 0.203 38 | 0.586 24 | 0.754 21 | 0.661 21 | 0.753 17 | 0.588 22 | 0.902 9 | 0.813 18 | 0.642 15 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 | ||||||||||||||||||||||
SAFNet-seg | ![]() | 0.654 22 | 0.752 30 | 0.734 25 | 0.664 43 | 0.583 30 | 0.815 19 | 0.399 41 | 0.754 18 | 0.639 7 | 0.535 22 | 0.942 33 | 0.470 19 | 0.309 8 | 0.665 8 | 0.539 40 | 0.650 23 | 0.708 28 | 0.635 9 | 0.857 37 | 0.793 28 | 0.642 15 |
DCM-Net | 0.658 20 | 0.778 20 | 0.702 35 | 0.806 10 | 0.619 21 | 0.813 23 | 0.468 17 | 0.693 34 | 0.494 43 | 0.524 26 | 0.941 35 | 0.449 28 | 0.298 11 | 0.510 38 | 0.821 12 | 0.675 16 | 0.727 24 | 0.568 26 | 0.826 42 | 0.803 21 | 0.637 17 | |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] | ||||||||||||||||||||||
KP-FCNN | 0.684 13 | 0.847 9 | 0.758 18 | 0.784 14 | 0.647 15 | 0.814 20 | 0.473 14 | 0.772 14 | 0.605 14 | 0.594 11 | 0.935 42 | 0.450 27 | 0.181 44 | 0.587 22 | 0.805 15 | 0.690 13 | 0.785 9 | 0.614 14 | 0.882 20 | 0.819 14 | 0.632 18 | |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 | ||||||||||||||||||||||
FusionAwareConv | 0.630 37 | 0.604 50 | 0.741 23 | 0.766 18 | 0.590 27 | 0.747 46 | 0.501 7 | 0.734 25 | 0.503 41 | 0.527 24 | 0.919 53 | 0.454 25 | 0.323 6 | 0.550 34 | 0.420 50 | 0.678 15 | 0.688 37 | 0.544 38 | 0.896 13 | 0.795 25 | 0.627 19 | |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 | ||||||||||||||||||||||
RandLA-Net | ![]() | 0.645 23 | 0.778 20 | 0.731 26 | 0.699 29 | 0.577 32 | 0.829 11 | 0.446 24 | 0.736 24 | 0.477 46 | 0.523 28 | 0.945 27 | 0.454 25 | 0.269 20 | 0.484 45 | 0.749 24 | 0.618 35 | 0.738 21 | 0.599 19 | 0.827 41 | 0.792 30 | 0.621 20 |
MCCNN | ![]() | 0.633 32 | 0.866 6 | 0.731 26 | 0.771 15 | 0.576 33 | 0.809 24 | 0.410 35 | 0.684 35 | 0.497 42 | 0.491 35 | 0.949 17 | 0.466 21 | 0.105 58 | 0.581 25 | 0.646 35 | 0.620 33 | 0.680 39 | 0.542 40 | 0.817 44 | 0.795 25 | 0.618 21 |
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 | ||||||||||||||||||||||
Supervoxel-CNN | 0.635 30 | 0.656 44 | 0.711 31 | 0.719 26 | 0.613 22 | 0.757 44 | 0.444 28 | 0.765 16 | 0.534 35 | 0.566 14 | 0.928 47 | 0.478 16 | 0.272 18 | 0.636 11 | 0.531 42 | 0.664 20 | 0.645 46 | 0.508 46 | 0.864 33 | 0.792 30 | 0.611 22 | |
RFCR | 0.702 8 | 0.889 3 | 0.745 20 | 0.813 7 | 0.672 10 | 0.818 18 | 0.493 11 | 0.815 9 | 0.623 10 | 0.610 7 | 0.947 23 | 0.470 19 | 0.249 25 | 0.594 21 | 0.848 8 | 0.705 11 | 0.779 11 | 0.646 7 | 0.892 16 | 0.823 12 | 0.611 22 | |
LAP-D | 0.594 43 | 0.720 36 | 0.692 41 | 0.637 48 | 0.456 51 | 0.773 39 | 0.391 46 | 0.730 26 | 0.587 19 | 0.445 46 | 0.940 37 | 0.381 46 | 0.288 13 | 0.434 49 | 0.453 47 | 0.591 42 | 0.649 44 | 0.581 23 | 0.777 49 | 0.749 49 | 0.610 24 | |
MVPNet | ![]() | 0.641 24 | 0.831 11 | 0.715 30 | 0.671 40 | 0.590 27 | 0.781 37 | 0.394 43 | 0.679 37 | 0.642 6 | 0.553 16 | 0.937 39 | 0.462 22 | 0.256 22 | 0.649 9 | 0.406 51 | 0.626 32 | 0.691 36 | 0.666 5 | 0.877 21 | 0.792 30 | 0.608 25 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 | ||||||||||||||||||||||
ROSMRF3D | 0.673 15 | 0.789 17 | 0.748 19 | 0.763 19 | 0.635 19 | 0.814 20 | 0.407 39 | 0.747 20 | 0.581 22 | 0.573 13 | 0.950 14 | 0.484 14 | 0.271 19 | 0.607 19 | 0.754 21 | 0.649 24 | 0.774 12 | 0.596 20 | 0.883 19 | 0.823 12 | 0.606 26 | |
HPEIN | 0.618 40 | 0.729 35 | 0.668 47 | 0.647 46 | 0.597 25 | 0.766 41 | 0.414 34 | 0.680 36 | 0.520 38 | 0.525 25 | 0.946 25 | 0.432 32 | 0.215 33 | 0.493 44 | 0.599 38 | 0.638 29 | 0.617 51 | 0.570 25 | 0.897 12 | 0.806 19 | 0.605 27 | |
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 | ||||||||||||||||||||||
CCRFNet | 0.589 45 | 0.766 26 | 0.659 51 | 0.683 34 | 0.470 50 | 0.740 48 | 0.387 47 | 0.620 47 | 0.490 44 | 0.476 40 | 0.922 51 | 0.355 51 | 0.245 27 | 0.511 37 | 0.511 43 | 0.571 46 | 0.643 47 | 0.493 49 | 0.872 28 | 0.762 45 | 0.600 28 | |
joint point-based | ![]() | 0.634 31 | 0.614 49 | 0.778 12 | 0.667 42 | 0.633 20 | 0.825 14 | 0.420 33 | 0.804 10 | 0.467 49 | 0.561 15 | 0.951 10 | 0.494 12 | 0.291 12 | 0.566 30 | 0.458 46 | 0.579 45 | 0.764 15 | 0.559 32 | 0.838 40 | 0.814 16 | 0.598 29 |
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 21 | 0.698 41 | 0.743 22 | 0.650 44 | 0.564 37 | 0.820 17 | 0.505 6 | 0.758 17 | 0.631 9 | 0.479 39 | 0.945 27 | 0.480 15 | 0.226 29 | 0.572 28 | 0.774 19 | 0.690 13 | 0.735 23 | 0.614 14 | 0.853 38 | 0.776 40 | 0.597 30 | |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. | ||||||||||||||||||||||
SConv | 0.636 29 | 0.830 12 | 0.697 39 | 0.752 22 | 0.572 36 | 0.780 38 | 0.445 26 | 0.716 29 | 0.529 36 | 0.530 23 | 0.951 10 | 0.446 29 | 0.170 45 | 0.507 40 | 0.666 34 | 0.636 30 | 0.682 38 | 0.541 41 | 0.886 18 | 0.799 22 | 0.594 31 | |
DPC | 0.592 44 | 0.720 36 | 0.700 37 | 0.602 51 | 0.480 48 | 0.762 43 | 0.380 48 | 0.713 31 | 0.585 21 | 0.437 47 | 0.940 37 | 0.369 48 | 0.288 13 | 0.434 49 | 0.509 44 | 0.590 44 | 0.639 49 | 0.567 27 | 0.772 50 | 0.755 47 | 0.592 32 | |
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 34 | 0.742 31 | 0.687 46 | 0.672 38 | 0.557 40 | 0.792 33 | 0.408 36 | 0.665 41 | 0.545 33 | 0.508 30 | 0.952 9 | 0.428 35 | 0.186 42 | 0.634 13 | 0.702 30 | 0.620 33 | 0.706 29 | 0.555 34 | 0.873 27 | 0.798 24 | 0.581 33 | |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL | ||||||||||||||||||||||
PointMTL | 0.632 33 | 0.731 34 | 0.688 44 | 0.675 37 | 0.591 26 | 0.784 36 | 0.444 28 | 0.565 50 | 0.610 12 | 0.492 34 | 0.949 17 | 0.456 24 | 0.254 23 | 0.587 22 | 0.706 29 | 0.599 39 | 0.665 43 | 0.612 17 | 0.868 32 | 0.791 33 | 0.579 34 | |
PointMRNet | 0.640 26 | 0.717 38 | 0.701 36 | 0.692 32 | 0.576 33 | 0.801 26 | 0.467 18 | 0.716 29 | 0.563 28 | 0.459 43 | 0.953 7 | 0.429 34 | 0.169 46 | 0.581 25 | 0.854 5 | 0.605 36 | 0.710 26 | 0.550 36 | 0.894 14 | 0.793 28 | 0.575 35 | |
SIConv | 0.625 38 | 0.830 12 | 0.694 40 | 0.757 20 | 0.563 38 | 0.772 40 | 0.448 22 | 0.647 44 | 0.520 38 | 0.509 29 | 0.949 17 | 0.431 33 | 0.191 41 | 0.496 43 | 0.614 37 | 0.647 27 | 0.672 41 | 0.535 43 | 0.876 22 | 0.783 36 | 0.571 36 | |
SALANet | 0.670 16 | 0.816 15 | 0.770 14 | 0.768 17 | 0.652 14 | 0.807 25 | 0.451 20 | 0.747 20 | 0.659 5 | 0.545 18 | 0.924 49 | 0.473 18 | 0.149 54 | 0.571 29 | 0.811 14 | 0.635 31 | 0.746 19 | 0.623 12 | 0.892 16 | 0.794 27 | 0.570 37 | |
TextureNet | ![]() | 0.566 48 | 0.672 43 | 0.664 49 | 0.671 40 | 0.494 46 | 0.719 49 | 0.445 26 | 0.678 38 | 0.411 55 | 0.396 50 | 0.935 42 | 0.356 50 | 0.225 30 | 0.412 51 | 0.535 41 | 0.565 47 | 0.636 50 | 0.464 52 | 0.794 48 | 0.680 55 | 0.568 38 |
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 27 | 0.785 18 | 0.760 16 | 0.713 28 | 0.603 24 | 0.798 30 | 0.392 44 | 0.534 52 | 0.603 16 | 0.524 26 | 0.948 20 | 0.457 23 | 0.250 24 | 0.538 35 | 0.723 26 | 0.598 40 | 0.696 35 | 0.614 14 | 0.872 28 | 0.799 22 | 0.567 39 |
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 28 | 0.734 33 | 0.692 41 | 0.714 27 | 0.576 33 | 0.797 31 | 0.446 24 | 0.743 22 | 0.598 18 | 0.437 47 | 0.942 33 | 0.403 42 | 0.150 53 | 0.626 15 | 0.800 16 | 0.649 24 | 0.697 34 | 0.557 33 | 0.846 39 | 0.777 39 | 0.563 40 | |
PanopticFusion-label | 0.529 51 | 0.491 58 | 0.688 44 | 0.604 50 | 0.386 54 | 0.632 58 | 0.225 62 | 0.705 33 | 0.434 53 | 0.293 57 | 0.815 61 | 0.348 52 | 0.241 28 | 0.499 42 | 0.669 33 | 0.507 50 | 0.649 44 | 0.442 56 | 0.796 47 | 0.602 61 | 0.561 41 | |
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 24 | 0.776 22 | 0.703 34 | 0.721 25 | 0.557 40 | 0.826 13 | 0.451 20 | 0.672 39 | 0.563 28 | 0.483 38 | 0.943 32 | 0.425 38 | 0.162 48 | 0.644 10 | 0.726 25 | 0.659 22 | 0.709 27 | 0.572 24 | 0.875 23 | 0.786 34 | 0.559 42 | |
PointNet2-SFPN | 0.631 34 | 0.771 24 | 0.692 41 | 0.672 38 | 0.524 43 | 0.837 7 | 0.440 30 | 0.706 32 | 0.538 34 | 0.446 45 | 0.944 29 | 0.421 39 | 0.219 32 | 0.552 32 | 0.751 23 | 0.591 42 | 0.737 22 | 0.543 39 | 0.901 11 | 0.768 43 | 0.557 43 | |
PointMRNet-lite | 0.625 38 | 0.643 47 | 0.711 31 | 0.697 31 | 0.581 31 | 0.801 26 | 0.408 36 | 0.670 40 | 0.558 30 | 0.497 33 | 0.944 29 | 0.436 30 | 0.152 52 | 0.617 17 | 0.708 28 | 0.603 37 | 0.743 20 | 0.532 44 | 0.870 31 | 0.784 35 | 0.545 44 | |
3DMV | 0.484 54 | 0.484 59 | 0.538 60 | 0.643 47 | 0.424 52 | 0.606 61 | 0.310 52 | 0.574 49 | 0.433 54 | 0.378 52 | 0.796 62 | 0.301 54 | 0.214 34 | 0.537 36 | 0.208 60 | 0.472 55 | 0.507 61 | 0.413 59 | 0.693 56 | 0.602 61 | 0.539 45 | |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 | ||||||||||||||||||||||
SPH3D-GCN | ![]() | 0.610 41 | 0.858 8 | 0.772 13 | 0.489 57 | 0.532 42 | 0.792 33 | 0.404 40 | 0.643 45 | 0.570 25 | 0.507 31 | 0.935 42 | 0.414 41 | 0.046 63 | 0.510 38 | 0.702 30 | 0.602 38 | 0.705 30 | 0.549 37 | 0.859 36 | 0.773 41 | 0.534 46 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 | ||||||||||||||||||||||
DVVNet | 0.562 49 | 0.648 45 | 0.700 37 | 0.770 16 | 0.586 29 | 0.687 53 | 0.333 51 | 0.650 42 | 0.514 40 | 0.475 41 | 0.906 57 | 0.359 49 | 0.223 31 | 0.340 54 | 0.442 49 | 0.422 57 | 0.668 42 | 0.501 47 | 0.708 55 | 0.779 37 | 0.534 46 | |
ROSMRF | 0.580 47 | 0.772 23 | 0.707 33 | 0.681 35 | 0.563 38 | 0.764 42 | 0.362 49 | 0.515 53 | 0.465 50 | 0.465 42 | 0.936 40 | 0.427 37 | 0.207 35 | 0.438 47 | 0.577 39 | 0.536 49 | 0.675 40 | 0.486 50 | 0.723 54 | 0.779 37 | 0.524 48 | |
Pointnet++ & Feature | ![]() | 0.557 50 | 0.735 32 | 0.661 50 | 0.686 33 | 0.491 47 | 0.744 47 | 0.392 44 | 0.539 51 | 0.451 51 | 0.375 53 | 0.946 25 | 0.376 47 | 0.205 36 | 0.403 52 | 0.356 53 | 0.553 48 | 0.643 47 | 0.497 48 | 0.824 43 | 0.756 46 | 0.515 49 |
PCNN | 0.498 53 | 0.559 54 | 0.644 54 | 0.560 54 | 0.420 53 | 0.711 51 | 0.229 60 | 0.414 55 | 0.436 52 | 0.352 54 | 0.941 35 | 0.324 53 | 0.155 50 | 0.238 59 | 0.387 52 | 0.493 51 | 0.529 58 | 0.509 45 | 0.813 46 | 0.751 48 | 0.504 50 | |
SegGCN | ![]() | 0.589 45 | 0.833 10 | 0.731 26 | 0.539 55 | 0.514 45 | 0.789 35 | 0.448 22 | 0.467 54 | 0.573 23 | 0.484 37 | 0.936 40 | 0.396 44 | 0.061 62 | 0.501 41 | 0.507 45 | 0.594 41 | 0.700 32 | 0.563 29 | 0.874 25 | 0.771 42 | 0.493 51 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020 | ||||||||||||||||||||||
3DSM_DMMF | 0.631 34 | 0.626 48 | 0.745 20 | 0.801 12 | 0.607 23 | 0.751 45 | 0.506 5 | 0.729 27 | 0.565 26 | 0.491 35 | 0.866 60 | 0.434 31 | 0.197 40 | 0.595 20 | 0.630 36 | 0.709 10 | 0.705 30 | 0.560 30 | 0.875 23 | 0.740 50 | 0.491 52 | |
AttAN | 0.609 42 | 0.760 27 | 0.667 48 | 0.649 45 | 0.521 44 | 0.793 32 | 0.457 19 | 0.648 43 | 0.528 37 | 0.434 49 | 0.947 23 | 0.401 43 | 0.153 51 | 0.454 46 | 0.721 27 | 0.648 26 | 0.717 25 | 0.536 42 | 0.904 8 | 0.765 44 | 0.485 53 | |
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 52 | 0.558 55 | 0.608 57 | 0.424 62 | 0.478 49 | 0.690 52 | 0.246 58 | 0.586 48 | 0.468 48 | 0.450 44 | 0.911 55 | 0.394 45 | 0.160 49 | 0.438 47 | 0.212 59 | 0.432 56 | 0.541 57 | 0.475 51 | 0.742 52 | 0.727 51 | 0.477 54 | |
PointCNN with RGB | ![]() | 0.458 55 | 0.577 53 | 0.611 56 | 0.356 64 | 0.321 60 | 0.715 50 | 0.299 54 | 0.376 58 | 0.328 61 | 0.319 55 | 0.944 29 | 0.285 56 | 0.164 47 | 0.216 62 | 0.229 58 | 0.484 53 | 0.545 56 | 0.456 54 | 0.755 51 | 0.709 52 | 0.475 55 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 | ||||||||||||||||||||||
PNET2 | 0.442 57 | 0.548 56 | 0.548 59 | 0.597 52 | 0.363 57 | 0.628 59 | 0.300 53 | 0.292 59 | 0.374 58 | 0.307 56 | 0.881 59 | 0.268 57 | 0.186 42 | 0.238 59 | 0.204 61 | 0.407 58 | 0.506 62 | 0.449 55 | 0.667 57 | 0.620 59 | 0.462 56 | |
SurfaceConvPF | 0.442 57 | 0.505 57 | 0.622 55 | 0.380 63 | 0.342 59 | 0.654 56 | 0.227 61 | 0.397 57 | 0.367 59 | 0.276 59 | 0.924 49 | 0.240 60 | 0.198 39 | 0.359 53 | 0.262 56 | 0.366 59 | 0.581 53 | 0.435 57 | 0.640 58 | 0.668 56 | 0.398 57 | |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. | ||||||||||||||||||||||
ScanNet+FTSDF | 0.383 62 | 0.297 64 | 0.491 62 | 0.432 61 | 0.358 58 | 0.612 60 | 0.274 56 | 0.116 63 | 0.411 55 | 0.265 60 | 0.904 58 | 0.229 61 | 0.079 60 | 0.250 57 | 0.185 62 | 0.320 62 | 0.510 59 | 0.385 60 | 0.548 61 | 0.597 63 | 0.394 58 | |
Tangent Convolutions | ![]() | 0.438 59 | 0.437 62 | 0.646 53 | 0.474 59 | 0.369 56 | 0.645 57 | 0.353 50 | 0.258 61 | 0.282 63 | 0.279 58 | 0.918 54 | 0.298 55 | 0.147 55 | 0.283 56 | 0.294 55 | 0.487 52 | 0.562 54 | 0.427 58 | 0.619 59 | 0.633 58 | 0.352 59 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 | ||||||||||||||||||||||
subcloud_weak | 0.411 60 | 0.479 60 | 0.650 52 | 0.475 58 | 0.285 63 | 0.519 64 | 0.087 65 | 0.725 28 | 0.396 57 | 0.386 51 | 0.621 65 | 0.250 59 | 0.117 56 | 0.338 55 | 0.443 48 | 0.188 65 | 0.594 52 | 0.369 62 | 0.377 65 | 0.616 60 | 0.306 60 | |
SSC-UNet | ![]() | 0.308 64 | 0.353 63 | 0.290 65 | 0.278 65 | 0.166 65 | 0.553 62 | 0.169 64 | 0.286 60 | 0.147 65 | 0.148 65 | 0.908 56 | 0.182 64 | 0.064 61 | 0.023 65 | 0.018 66 | 0.354 61 | 0.363 63 | 0.345 63 | 0.546 63 | 0.685 54 | 0.278 61 |
SPLAT Net | ![]() | 0.393 61 | 0.472 61 | 0.511 61 | 0.606 49 | 0.311 61 | 0.656 55 | 0.245 59 | 0.405 56 | 0.328 61 | 0.197 63 | 0.927 48 | 0.227 62 | 0.000 66 | 0.001 66 | 0.249 57 | 0.271 64 | 0.510 59 | 0.383 61 | 0.593 60 | 0.699 53 | 0.267 62 |
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 63 | 0.584 52 | 0.478 63 | 0.458 60 | 0.256 64 | 0.360 65 | 0.250 57 | 0.247 62 | 0.278 64 | 0.261 61 | 0.677 64 | 0.183 63 | 0.117 56 | 0.212 63 | 0.145 64 | 0.364 60 | 0.346 65 | 0.232 65 | 0.548 61 | 0.523 64 | 0.252 63 |
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 56 | 0.679 42 | 0.604 58 | 0.578 53 | 0.380 55 | 0.682 54 | 0.291 55 | 0.106 64 | 0.483 45 | 0.258 62 | 0.920 52 | 0.258 58 | 0.025 64 | 0.231 61 | 0.325 54 | 0.480 54 | 0.560 55 | 0.463 53 | 0.725 53 | 0.666 57 | 0.231 64 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 | ||||||||||||||||||||||
ScanNet | ![]() | 0.306 65 | 0.203 65 | 0.366 64 | 0.501 56 | 0.311 61 | 0.524 63 | 0.211 63 | 0.002 66 | 0.342 60 | 0.189 64 | 0.786 63 | 0.145 65 | 0.102 59 | 0.245 58 | 0.152 63 | 0.318 63 | 0.348 64 | 0.300 64 | 0.460 64 | 0.437 65 | 0.182 65 |
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 66 | 0.000 66 | 0.041 66 | 0.172 66 | 0.030 66 | 0.062 66 | 0.001 66 | 0.035 65 | 0.004 66 | 0.051 66 | 0.143 66 | 0.019 66 | 0.003 65 | 0.041 64 | 0.050 65 | 0.003 66 | 0.054 66 | 0.018 66 | 0.005 66 | 0.264 66 | 0.082 66 | |