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 20 | 0.819 2 | 0.848 1 | 0.702 6 | 0.865 3 | 0.397 36 | 0.899 1 | 0.699 1 | 0.664 2 | 0.948 17 | 0.588 2 | 0.330 3 | 0.746 4 | 0.851 6 | 0.764 3 | 0.796 5 | 0.704 1 | 0.935 3 | 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 4 | 0.859 5 | 0.818 3 | 0.832 4 | 0.709 5 | 0.840 4 | 0.521 4 | 0.853 3 | 0.660 4 | 0.643 4 | 0.951 8 | 0.544 4 | 0.286 12 | 0.731 5 | 0.893 1 | 0.675 14 | 0.772 9 | 0.683 4 | 0.874 20 | 0.852 5 | 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 23 | 0.796 6 | 0.839 3 | 0.746 2 | 0.907 1 | 0.562 1 | 0.850 4 | 0.680 2 | 0.672 1 | 0.978 1 | 0.610 1 | 0.335 2 | 0.777 1 | 0.819 11 | 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 3 | 0.811 7 | 0.752 1 | 0.839 5 | 0.485 11 | 0.842 6 | 0.673 3 | 0.644 3 | 0.957 2 | 0.528 5 | 0.305 7 | 0.773 2 | 0.859 3 | 0.788 2 | 0.818 3 | 0.693 2 | 0.916 5 | 0.856 4 | 0.723 4 | |
PointASNL | ![]() | 0.666 13 | 0.703 33 | 0.781 8 | 0.751 20 | 0.655 11 | 0.830 7 | 0.471 14 | 0.769 12 | 0.474 41 | 0.537 16 | 0.951 8 | 0.475 13 | 0.279 14 | 0.635 10 | 0.698 27 | 0.675 14 | 0.751 14 | 0.553 30 | 0.816 38 | 0.806 14 | 0.703 5 |
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 6 | 0.812 14 | 0.812 5 | 0.810 8 | 0.735 3 | 0.834 6 | 0.495 9 | 0.860 2 | 0.572 19 | 0.602 8 | 0.954 4 | 0.512 7 | 0.280 13 | 0.757 3 | 0.845 9 | 0.725 5 | 0.780 7 | 0.606 15 | 0.937 2 | 0.851 6 | 0.700 6 | |
SparseConvNet | 0.725 5 | 0.647 39 | 0.821 1 | 0.846 2 | 0.721 4 | 0.869 2 | 0.533 2 | 0.754 15 | 0.603 13 | 0.614 5 | 0.955 3 | 0.572 3 | 0.325 4 | 0.710 6 | 0.870 2 | 0.724 6 | 0.823 2 | 0.628 8 | 0.934 4 | 0.865 3 | 0.683 7 | |
VACNN++ | 0.638 22 | 0.820 12 | 0.701 29 | 0.687 29 | 0.594 21 | 0.791 28 | 0.430 29 | 0.587 41 | 0.569 21 | 0.529 18 | 0.950 12 | 0.467 16 | 0.253 19 | 0.524 30 | 0.722 21 | 0.618 29 | 0.694 29 | 0.570 21 | 0.793 42 | 0.802 17 | 0.659 8 | |
FusionNet | 0.688 10 | 0.704 32 | 0.741 19 | 0.754 18 | 0.656 10 | 0.829 8 | 0.501 7 | 0.741 18 | 0.609 11 | 0.548 14 | 0.950 12 | 0.522 6 | 0.371 1 | 0.633 12 | 0.756 16 | 0.715 7 | 0.771 10 | 0.623 9 | 0.861 29 | 0.814 11 | 0.658 9 | |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 | ||||||||||||||||||||||
CU-Hybrid Net | 0.693 9 | 0.596 44 | 0.789 7 | 0.803 10 | 0.677 7 | 0.800 21 | 0.469 15 | 0.846 5 | 0.554 27 | 0.591 10 | 0.948 17 | 0.500 8 | 0.316 6 | 0.609 15 | 0.847 8 | 0.732 4 | 0.808 4 | 0.593 17 | 0.894 10 | 0.839 7 | 0.652 10 | |
JSENet | 0.699 8 | 0.881 3 | 0.762 12 | 0.821 5 | 0.667 9 | 0.800 21 | 0.522 3 | 0.792 9 | 0.613 9 | 0.607 7 | 0.935 36 | 0.492 10 | 0.205 29 | 0.576 23 | 0.853 5 | 0.691 10 | 0.758 12 | 0.652 6 | 0.872 23 | 0.828 8 | 0.649 11 | |
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 13 | 0.781 16 | 0.759 14 | 0.699 25 | 0.644 14 | 0.822 12 | 0.475 12 | 0.779 10 | 0.564 23 | 0.504 27 | 0.953 5 | 0.428 31 | 0.203 31 | 0.586 20 | 0.754 17 | 0.661 18 | 0.753 13 | 0.588 18 | 0.902 7 | 0.813 13 | 0.642 12 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 | ||||||||||||||||||||||
DCM-Net | 0.658 15 | 0.778 17 | 0.702 28 | 0.806 9 | 0.619 16 | 0.813 16 | 0.468 16 | 0.693 28 | 0.494 37 | 0.524 21 | 0.941 30 | 0.449 24 | 0.298 8 | 0.510 32 | 0.821 10 | 0.675 14 | 0.727 19 | 0.568 23 | 0.826 35 | 0.803 16 | 0.637 13 | |
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 11 | 0.847 7 | 0.758 15 | 0.784 12 | 0.647 13 | 0.814 15 | 0.473 13 | 0.772 11 | 0.605 12 | 0.594 9 | 0.935 36 | 0.450 23 | 0.181 37 | 0.587 18 | 0.805 13 | 0.690 11 | 0.785 6 | 0.614 11 | 0.882 15 | 0.819 10 | 0.632 14 | |
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 31 | 0.604 43 | 0.741 19 | 0.766 16 | 0.590 23 | 0.747 39 | 0.501 7 | 0.734 20 | 0.503 35 | 0.527 19 | 0.919 46 | 0.454 21 | 0.323 5 | 0.550 27 | 0.420 43 | 0.678 13 | 0.688 31 | 0.544 33 | 0.896 9 | 0.795 21 | 0.627 15 | |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 | ||||||||||||||||||||||
RandLA-Net | ![]() | 0.645 17 | 0.778 17 | 0.731 21 | 0.699 25 | 0.577 27 | 0.829 8 | 0.446 23 | 0.736 19 | 0.477 40 | 0.523 23 | 0.945 24 | 0.454 21 | 0.269 16 | 0.484 39 | 0.749 18 | 0.618 29 | 0.738 17 | 0.599 16 | 0.827 34 | 0.792 25 | 0.621 16 |
MCCNN | ![]() | 0.633 27 | 0.866 4 | 0.731 21 | 0.771 13 | 0.576 28 | 0.809 17 | 0.410 32 | 0.684 29 | 0.497 36 | 0.491 30 | 0.949 14 | 0.466 17 | 0.105 51 | 0.581 21 | 0.646 30 | 0.620 27 | 0.680 33 | 0.542 34 | 0.817 37 | 0.795 21 | 0.618 17 |
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 25 | 0.656 37 | 0.711 25 | 0.719 22 | 0.613 17 | 0.757 37 | 0.444 27 | 0.765 13 | 0.534 29 | 0.566 11 | 0.928 40 | 0.478 12 | 0.272 15 | 0.636 9 | 0.531 35 | 0.664 17 | 0.645 39 | 0.508 40 | 0.864 28 | 0.792 25 | 0.611 18 | |
RFCR | 0.702 7 | 0.889 2 | 0.745 16 | 0.813 6 | 0.672 8 | 0.818 14 | 0.493 10 | 0.815 7 | 0.623 8 | 0.610 6 | 0.947 20 | 0.470 15 | 0.249 21 | 0.594 17 | 0.848 7 | 0.705 9 | 0.779 8 | 0.646 7 | 0.892 12 | 0.823 9 | 0.611 18 | |
LAP-D | 0.594 37 | 0.720 29 | 0.692 35 | 0.637 41 | 0.456 44 | 0.773 33 | 0.391 40 | 0.730 21 | 0.587 16 | 0.445 39 | 0.940 32 | 0.381 39 | 0.288 10 | 0.434 42 | 0.453 40 | 0.591 37 | 0.649 37 | 0.581 19 | 0.777 43 | 0.749 42 | 0.610 20 | |
MVPNet | ![]() | 0.641 18 | 0.831 9 | 0.715 24 | 0.671 34 | 0.590 23 | 0.781 31 | 0.394 37 | 0.679 31 | 0.642 6 | 0.553 13 | 0.937 34 | 0.462 18 | 0.256 17 | 0.649 7 | 0.406 44 | 0.626 26 | 0.691 30 | 0.666 5 | 0.877 16 | 0.792 25 | 0.608 21 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 | ||||||||||||||||||||||
HPEIN | 0.618 34 | 0.729 28 | 0.668 40 | 0.647 39 | 0.597 20 | 0.766 35 | 0.414 31 | 0.680 30 | 0.520 32 | 0.525 20 | 0.946 22 | 0.432 28 | 0.215 27 | 0.493 38 | 0.599 33 | 0.638 23 | 0.617 44 | 0.570 21 | 0.897 8 | 0.806 14 | 0.605 22 | |
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 39 | 0.766 21 | 0.659 44 | 0.683 31 | 0.470 43 | 0.740 41 | 0.387 41 | 0.620 40 | 0.490 38 | 0.476 35 | 0.922 44 | 0.355 44 | 0.245 22 | 0.511 31 | 0.511 36 | 0.571 40 | 0.643 40 | 0.493 43 | 0.872 23 | 0.762 38 | 0.600 23 | |
joint point-based | ![]() | 0.634 26 | 0.614 42 | 0.778 9 | 0.667 36 | 0.633 15 | 0.825 11 | 0.420 30 | 0.804 8 | 0.467 43 | 0.561 12 | 0.951 8 | 0.494 9 | 0.291 9 | 0.566 26 | 0.458 39 | 0.579 39 | 0.764 11 | 0.559 27 | 0.838 33 | 0.814 11 | 0.598 24 |
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 16 | 0.698 34 | 0.743 18 | 0.650 37 | 0.564 32 | 0.820 13 | 0.505 6 | 0.758 14 | 0.631 7 | 0.479 34 | 0.945 24 | 0.480 11 | 0.226 24 | 0.572 24 | 0.774 15 | 0.690 11 | 0.735 18 | 0.614 11 | 0.853 31 | 0.776 34 | 0.597 25 | |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. | ||||||||||||||||||||||
SConv | 0.636 24 | 0.830 10 | 0.697 33 | 0.752 19 | 0.572 31 | 0.780 32 | 0.445 25 | 0.716 24 | 0.529 30 | 0.530 17 | 0.951 8 | 0.446 25 | 0.170 38 | 0.507 34 | 0.666 29 | 0.636 24 | 0.682 32 | 0.541 35 | 0.886 14 | 0.799 18 | 0.594 26 | |
DPC | 0.592 38 | 0.720 29 | 0.700 31 | 0.602 44 | 0.480 41 | 0.762 36 | 0.380 42 | 0.713 26 | 0.585 17 | 0.437 40 | 0.940 32 | 0.369 41 | 0.288 10 | 0.434 42 | 0.509 37 | 0.590 38 | 0.639 42 | 0.567 24 | 0.772 44 | 0.755 40 | 0.592 27 | |
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 29 | 0.742 24 | 0.687 39 | 0.672 33 | 0.557 34 | 0.792 26 | 0.408 33 | 0.665 35 | 0.545 28 | 0.508 25 | 0.952 7 | 0.428 31 | 0.186 35 | 0.634 11 | 0.702 25 | 0.620 27 | 0.706 23 | 0.555 29 | 0.873 22 | 0.798 20 | 0.581 28 | |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL | ||||||||||||||||||||||
PointMTL | 0.632 28 | 0.731 27 | 0.688 37 | 0.675 32 | 0.591 22 | 0.784 30 | 0.444 27 | 0.565 44 | 0.610 10 | 0.492 29 | 0.949 14 | 0.456 20 | 0.254 18 | 0.587 18 | 0.706 24 | 0.599 34 | 0.665 36 | 0.612 14 | 0.868 27 | 0.791 28 | 0.579 29 | |
PointMRNet | 0.640 20 | 0.717 31 | 0.701 29 | 0.692 28 | 0.576 28 | 0.801 19 | 0.467 17 | 0.716 24 | 0.563 24 | 0.459 37 | 0.953 5 | 0.429 30 | 0.169 39 | 0.581 21 | 0.854 4 | 0.605 31 | 0.710 21 | 0.550 31 | 0.894 10 | 0.793 24 | 0.575 30 | |
SIConv | 0.625 32 | 0.830 10 | 0.694 34 | 0.757 17 | 0.563 33 | 0.772 34 | 0.448 21 | 0.647 38 | 0.520 32 | 0.509 24 | 0.949 14 | 0.431 29 | 0.191 34 | 0.496 37 | 0.614 32 | 0.647 22 | 0.672 34 | 0.535 37 | 0.876 17 | 0.783 31 | 0.571 31 | |
SALANet | 0.670 12 | 0.816 13 | 0.770 11 | 0.768 15 | 0.652 12 | 0.807 18 | 0.451 19 | 0.747 16 | 0.659 5 | 0.545 15 | 0.924 42 | 0.473 14 | 0.149 47 | 0.571 25 | 0.811 12 | 0.635 25 | 0.746 15 | 0.623 9 | 0.892 12 | 0.794 23 | 0.570 32 | |
TextureNet | ![]() | 0.566 41 | 0.672 36 | 0.664 42 | 0.671 34 | 0.494 39 | 0.719 42 | 0.445 25 | 0.678 32 | 0.411 48 | 0.396 43 | 0.935 36 | 0.356 43 | 0.225 25 | 0.412 44 | 0.535 34 | 0.565 41 | 0.636 43 | 0.464 45 | 0.794 41 | 0.680 48 | 0.568 33 |
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 21 | 0.785 15 | 0.760 13 | 0.713 24 | 0.603 19 | 0.798 23 | 0.392 38 | 0.534 46 | 0.603 13 | 0.524 21 | 0.948 17 | 0.457 19 | 0.250 20 | 0.538 28 | 0.723 20 | 0.598 35 | 0.696 28 | 0.614 11 | 0.872 23 | 0.799 18 | 0.567 34 |
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 23 | 0.734 26 | 0.692 35 | 0.714 23 | 0.576 28 | 0.797 24 | 0.446 23 | 0.743 17 | 0.598 15 | 0.437 40 | 0.942 29 | 0.403 35 | 0.150 46 | 0.626 13 | 0.800 14 | 0.649 20 | 0.697 27 | 0.557 28 | 0.846 32 | 0.777 33 | 0.563 35 | |
PanopticFusion-label | 0.529 44 | 0.491 51 | 0.688 37 | 0.604 43 | 0.386 47 | 0.632 51 | 0.225 55 | 0.705 27 | 0.434 46 | 0.293 50 | 0.815 54 | 0.348 45 | 0.241 23 | 0.499 36 | 0.669 28 | 0.507 43 | 0.649 37 | 0.442 49 | 0.796 40 | 0.602 54 | 0.561 36 | |
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 18 | 0.776 19 | 0.703 27 | 0.721 21 | 0.557 34 | 0.826 10 | 0.451 19 | 0.672 33 | 0.563 24 | 0.483 33 | 0.943 28 | 0.425 33 | 0.162 41 | 0.644 8 | 0.726 19 | 0.659 19 | 0.709 22 | 0.572 20 | 0.875 18 | 0.786 29 | 0.559 37 | |
PointMRNet-lite | 0.625 32 | 0.643 40 | 0.711 25 | 0.697 27 | 0.581 26 | 0.801 19 | 0.408 33 | 0.670 34 | 0.558 26 | 0.497 28 | 0.944 26 | 0.436 26 | 0.152 45 | 0.617 14 | 0.708 23 | 0.603 32 | 0.743 16 | 0.532 38 | 0.870 26 | 0.784 30 | 0.545 38 | |
3DMV | 0.484 47 | 0.484 52 | 0.538 53 | 0.643 40 | 0.424 45 | 0.606 54 | 0.310 45 | 0.574 43 | 0.433 47 | 0.378 45 | 0.796 55 | 0.301 47 | 0.214 28 | 0.537 29 | 0.208 53 | 0.472 48 | 0.507 54 | 0.413 52 | 0.693 49 | 0.602 54 | 0.539 39 | |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 | ||||||||||||||||||||||
DVVNet | 0.562 42 | 0.648 38 | 0.700 31 | 0.770 14 | 0.586 25 | 0.687 46 | 0.333 44 | 0.650 36 | 0.514 34 | 0.475 36 | 0.906 50 | 0.359 42 | 0.223 26 | 0.340 47 | 0.442 42 | 0.422 50 | 0.668 35 | 0.501 41 | 0.708 48 | 0.779 32 | 0.534 40 | |
SPH3D-GCN | ![]() | 0.610 35 | 0.858 6 | 0.772 10 | 0.489 50 | 0.532 36 | 0.792 26 | 0.404 35 | 0.643 39 | 0.570 20 | 0.507 26 | 0.935 36 | 0.414 34 | 0.046 56 | 0.510 32 | 0.702 25 | 0.602 33 | 0.705 24 | 0.549 32 | 0.859 30 | 0.773 35 | 0.534 40 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 | ||||||||||||||||||||||
Pointnet++ & Feature | ![]() | 0.557 43 | 0.735 25 | 0.661 43 | 0.686 30 | 0.491 40 | 0.744 40 | 0.392 38 | 0.539 45 | 0.451 44 | 0.375 46 | 0.946 22 | 0.376 40 | 0.205 29 | 0.403 45 | 0.356 46 | 0.553 42 | 0.643 40 | 0.497 42 | 0.824 36 | 0.756 39 | 0.515 42 |
PCNN | 0.498 46 | 0.559 47 | 0.644 47 | 0.560 47 | 0.420 46 | 0.711 44 | 0.229 53 | 0.414 48 | 0.436 45 | 0.352 47 | 0.941 30 | 0.324 46 | 0.155 43 | 0.238 52 | 0.387 45 | 0.493 44 | 0.529 51 | 0.509 39 | 0.813 39 | 0.751 41 | 0.504 43 | |
SegGCN | ![]() | 0.589 39 | 0.833 8 | 0.731 21 | 0.539 48 | 0.514 38 | 0.789 29 | 0.448 21 | 0.467 47 | 0.573 18 | 0.484 32 | 0.936 35 | 0.396 37 | 0.061 55 | 0.501 35 | 0.507 38 | 0.594 36 | 0.700 26 | 0.563 25 | 0.874 20 | 0.771 36 | 0.493 44 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020 | ||||||||||||||||||||||
3DSM_DMMF | 0.631 29 | 0.626 41 | 0.745 16 | 0.801 11 | 0.607 18 | 0.751 38 | 0.506 5 | 0.729 22 | 0.565 22 | 0.491 30 | 0.866 53 | 0.434 27 | 0.197 33 | 0.595 16 | 0.630 31 | 0.709 8 | 0.705 24 | 0.560 26 | 0.875 18 | 0.740 43 | 0.491 45 | |
AttAN | 0.609 36 | 0.760 22 | 0.667 41 | 0.649 38 | 0.521 37 | 0.793 25 | 0.457 18 | 0.648 37 | 0.528 31 | 0.434 42 | 0.947 20 | 0.401 36 | 0.153 44 | 0.454 40 | 0.721 22 | 0.648 21 | 0.717 20 | 0.536 36 | 0.904 6 | 0.765 37 | 0.485 46 | |
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 45 | 0.558 48 | 0.608 50 | 0.424 55 | 0.478 42 | 0.690 45 | 0.246 51 | 0.586 42 | 0.468 42 | 0.450 38 | 0.911 48 | 0.394 38 | 0.160 42 | 0.438 41 | 0.212 52 | 0.432 49 | 0.541 50 | 0.475 44 | 0.742 46 | 0.727 44 | 0.477 47 | |
PointCNN with RGB | ![]() | 0.458 48 | 0.577 46 | 0.611 49 | 0.356 57 | 0.321 53 | 0.715 43 | 0.299 47 | 0.376 51 | 0.328 54 | 0.319 48 | 0.944 26 | 0.285 49 | 0.164 40 | 0.216 55 | 0.229 51 | 0.484 46 | 0.545 49 | 0.456 47 | 0.755 45 | 0.709 45 | 0.475 48 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 | ||||||||||||||||||||||
PNET2 | 0.442 50 | 0.548 49 | 0.548 52 | 0.597 45 | 0.363 50 | 0.628 52 | 0.300 46 | 0.292 52 | 0.374 51 | 0.307 49 | 0.881 52 | 0.268 50 | 0.186 35 | 0.238 52 | 0.204 54 | 0.407 51 | 0.506 55 | 0.449 48 | 0.667 50 | 0.620 52 | 0.462 49 | |
SurfaceConvPF | 0.442 50 | 0.505 50 | 0.622 48 | 0.380 56 | 0.342 52 | 0.654 49 | 0.227 54 | 0.397 50 | 0.367 52 | 0.276 52 | 0.924 42 | 0.240 53 | 0.198 32 | 0.359 46 | 0.262 49 | 0.366 52 | 0.581 46 | 0.435 50 | 0.640 51 | 0.668 49 | 0.398 50 | |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. | ||||||||||||||||||||||
ScanNet+FTSDF | 0.383 55 | 0.297 57 | 0.491 55 | 0.432 54 | 0.358 51 | 0.612 53 | 0.274 49 | 0.116 56 | 0.411 48 | 0.265 53 | 0.904 51 | 0.229 54 | 0.079 53 | 0.250 50 | 0.185 55 | 0.320 55 | 0.510 52 | 0.385 53 | 0.548 54 | 0.597 56 | 0.394 51 | |
Tangent Convolutions | ![]() | 0.438 52 | 0.437 55 | 0.646 46 | 0.474 52 | 0.369 49 | 0.645 50 | 0.353 43 | 0.258 54 | 0.282 56 | 0.279 51 | 0.918 47 | 0.298 48 | 0.147 48 | 0.283 49 | 0.294 48 | 0.487 45 | 0.562 47 | 0.427 51 | 0.619 52 | 0.633 51 | 0.352 52 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 | ||||||||||||||||||||||
subcloud_weak | 0.411 53 | 0.479 53 | 0.650 45 | 0.475 51 | 0.285 56 | 0.519 57 | 0.087 58 | 0.725 23 | 0.396 50 | 0.386 44 | 0.621 58 | 0.250 52 | 0.117 49 | 0.338 48 | 0.443 41 | 0.188 58 | 0.594 45 | 0.369 55 | 0.377 58 | 0.616 53 | 0.306 53 | |
SSC-UNet | ![]() | 0.308 57 | 0.353 56 | 0.290 58 | 0.278 58 | 0.166 58 | 0.553 55 | 0.169 57 | 0.286 53 | 0.147 58 | 0.148 58 | 0.908 49 | 0.182 57 | 0.064 54 | 0.023 58 | 0.018 59 | 0.354 54 | 0.363 56 | 0.345 56 | 0.546 56 | 0.685 47 | 0.278 54 |
SPLAT Net | ![]() | 0.393 54 | 0.472 54 | 0.511 54 | 0.606 42 | 0.311 54 | 0.656 48 | 0.245 52 | 0.405 49 | 0.328 54 | 0.197 56 | 0.927 41 | 0.227 55 | 0.000 59 | 0.001 59 | 0.249 50 | 0.271 57 | 0.510 52 | 0.383 54 | 0.593 53 | 0.699 46 | 0.267 55 |
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 56 | 0.584 45 | 0.478 56 | 0.458 53 | 0.256 57 | 0.360 58 | 0.250 50 | 0.247 55 | 0.278 57 | 0.261 54 | 0.677 57 | 0.183 56 | 0.117 49 | 0.212 56 | 0.145 57 | 0.364 53 | 0.346 58 | 0.232 58 | 0.548 54 | 0.523 57 | 0.252 56 |
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 49 | 0.679 35 | 0.604 51 | 0.578 46 | 0.380 48 | 0.682 47 | 0.291 48 | 0.106 57 | 0.483 39 | 0.258 55 | 0.920 45 | 0.258 51 | 0.025 57 | 0.231 54 | 0.325 47 | 0.480 47 | 0.560 48 | 0.463 46 | 0.725 47 | 0.666 50 | 0.231 57 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 | ||||||||||||||||||||||
ScanNet | ![]() | 0.306 58 | 0.203 58 | 0.366 57 | 0.501 49 | 0.311 54 | 0.524 56 | 0.211 56 | 0.002 59 | 0.342 53 | 0.189 57 | 0.786 56 | 0.145 58 | 0.102 52 | 0.245 51 | 0.152 56 | 0.318 56 | 0.348 57 | 0.300 57 | 0.460 57 | 0.437 58 | 0.182 58 |
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 59 | 0.000 59 | 0.041 59 | 0.172 59 | 0.030 59 | 0.062 59 | 0.001 59 | 0.035 58 | 0.004 59 | 0.051 59 | 0.143 59 | 0.019 59 | 0.003 58 | 0.041 57 | 0.050 58 | 0.003 59 | 0.054 59 | 0.018 59 | 0.005 59 | 0.264 59 | 0.082 59 | |