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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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 2 | 0.528 6 | 0.305 8 | 0.773 2 | 0.859 4 | 0.788 2 | 0.818 3 | 0.693 2 | 0.916 6 | 0.856 5 | 0.723 4 | |
OccuSeg+Semantic | 0.764 1 | 0.758 26 | 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 2 | 0.777 1 | 0.819 12 | 0.847 1 | 0.830 1 | 0.691 3 | 0.972 1 | 0.885 1 | 0.727 2 | |
MatchingNet | 0.724 7 | 0.812 15 | 0.812 6 | 0.810 9 | 0.735 3 | 0.834 8 | 0.495 10 | 0.860 3 | 0.572 21 | 0.602 9 | 0.954 5 | 0.512 8 | 0.280 15 | 0.757 4 | 0.845 10 | 0.725 5 | 0.780 8 | 0.606 17 | 0.937 3 | 0.851 7 | 0.700 7 | |
VMNet | 0.746 3 | 0.870 4 | 0.838 1 | 0.858 1 | 0.729 4 | 0.850 4 | 0.501 7 | 0.874 2 | 0.587 17 | 0.658 3 | 0.956 3 | 0.564 4 | 0.299 9 | 0.765 3 | 0.900 1 | 0.716 7 | 0.812 4 | 0.631 9 | 0.939 2 | 0.858 4 | 0.709 5 | |
SparseConvNet | 0.725 6 | 0.647 43 | 0.821 2 | 0.846 3 | 0.721 5 | 0.869 2 | 0.533 2 | 0.754 16 | 0.603 14 | 0.614 6 | 0.955 4 | 0.572 3 | 0.325 4 | 0.710 7 | 0.870 3 | 0.724 6 | 0.823 2 | 0.628 10 | 0.934 5 | 0.865 3 | 0.683 8 | |
MinkowskiNet | ![]() | 0.736 5 | 0.859 6 | 0.818 4 | 0.832 5 | 0.709 6 | 0.840 5 | 0.521 4 | 0.853 4 | 0.660 4 | 0.643 5 | 0.951 9 | 0.544 5 | 0.286 14 | 0.731 6 | 0.893 2 | 0.675 15 | 0.772 10 | 0.683 4 | 0.874 22 | 0.852 6 | 0.727 2 |
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 | ||||||||||||||||||||||
Virtual MVFusion | 0.746 3 | 0.771 22 | 0.819 3 | 0.848 2 | 0.702 7 | 0.865 3 | 0.397 39 | 0.899 1 | 0.699 1 | 0.664 2 | 0.948 18 | 0.588 2 | 0.330 3 | 0.746 5 | 0.851 7 | 0.764 3 | 0.796 6 | 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 | ||||||||||||||||||||||
CU-Hybrid Net | 0.693 10 | 0.596 48 | 0.789 8 | 0.803 11 | 0.677 8 | 0.800 24 | 0.469 16 | 0.846 6 | 0.554 29 | 0.591 11 | 0.948 18 | 0.500 9 | 0.316 6 | 0.609 17 | 0.847 9 | 0.732 4 | 0.808 5 | 0.593 19 | 0.894 12 | 0.839 8 | 0.652 11 | |
RFCR | 0.702 8 | 0.889 2 | 0.745 17 | 0.813 7 | 0.672 9 | 0.818 16 | 0.493 11 | 0.815 8 | 0.623 9 | 0.610 7 | 0.947 21 | 0.470 16 | 0.249 23 | 0.594 19 | 0.848 8 | 0.705 10 | 0.779 9 | 0.646 7 | 0.892 14 | 0.823 10 | 0.611 20 | |
JSENet | 0.699 9 | 0.881 3 | 0.762 13 | 0.821 6 | 0.667 10 | 0.800 24 | 0.522 3 | 0.792 10 | 0.613 10 | 0.607 8 | 0.935 40 | 0.492 11 | 0.205 33 | 0.576 25 | 0.853 6 | 0.691 11 | 0.758 13 | 0.652 6 | 0.872 25 | 0.828 9 | 0.649 12 | |
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 | ||||||||||||||||||||||
FusionNet | 0.688 11 | 0.704 36 | 0.741 20 | 0.754 19 | 0.656 11 | 0.829 10 | 0.501 7 | 0.741 20 | 0.609 12 | 0.548 15 | 0.950 13 | 0.522 7 | 0.371 1 | 0.633 14 | 0.756 17 | 0.715 8 | 0.771 11 | 0.623 11 | 0.861 31 | 0.814 12 | 0.658 10 | |
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020 | ||||||||||||||||||||||
PointASNL | ![]() | 0.666 14 | 0.703 37 | 0.781 9 | 0.751 21 | 0.655 12 | 0.830 9 | 0.471 15 | 0.769 13 | 0.474 44 | 0.537 17 | 0.951 9 | 0.475 14 | 0.279 16 | 0.635 12 | 0.698 29 | 0.675 15 | 0.751 15 | 0.553 32 | 0.816 41 | 0.806 15 | 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 | ||||||||||||||||||||||
SALANet | 0.670 13 | 0.816 14 | 0.770 12 | 0.768 16 | 0.652 13 | 0.807 21 | 0.451 20 | 0.747 18 | 0.659 5 | 0.545 16 | 0.924 46 | 0.473 15 | 0.149 51 | 0.571 27 | 0.811 13 | 0.635 27 | 0.746 16 | 0.623 11 | 0.892 14 | 0.794 24 | 0.570 34 | |
KP-FCNN | 0.684 12 | 0.847 8 | 0.758 16 | 0.784 13 | 0.647 14 | 0.814 18 | 0.473 14 | 0.772 12 | 0.605 13 | 0.594 10 | 0.935 40 | 0.450 25 | 0.181 41 | 0.587 20 | 0.805 14 | 0.690 12 | 0.785 7 | 0.614 13 | 0.882 17 | 0.819 11 | 0.632 16 | |
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019 | ||||||||||||||||||||||
PointConv | ![]() | 0.666 14 | 0.781 17 | 0.759 15 | 0.699 26 | 0.644 15 | 0.822 14 | 0.475 13 | 0.779 11 | 0.564 25 | 0.504 29 | 0.953 6 | 0.428 33 | 0.203 35 | 0.586 22 | 0.754 18 | 0.661 19 | 0.753 14 | 0.588 20 | 0.902 8 | 0.813 14 | 0.642 13 |
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 | ||||||||||||||||||||||
joint point-based | ![]() | 0.634 28 | 0.614 46 | 0.778 10 | 0.667 39 | 0.633 16 | 0.825 13 | 0.420 32 | 0.804 9 | 0.467 46 | 0.561 13 | 0.951 9 | 0.494 10 | 0.291 11 | 0.566 28 | 0.458 43 | 0.579 42 | 0.764 12 | 0.559 29 | 0.838 36 | 0.814 12 | 0.598 26 |
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019 | ||||||||||||||||||||||
DCM-Net | 0.658 16 | 0.778 18 | 0.702 31 | 0.806 10 | 0.619 17 | 0.813 19 | 0.468 17 | 0.693 31 | 0.494 40 | 0.524 23 | 0.941 33 | 0.449 26 | 0.298 10 | 0.510 35 | 0.821 11 | 0.675 15 | 0.727 21 | 0.568 25 | 0.826 38 | 0.803 17 | 0.637 15 | |
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral] | ||||||||||||||||||||||
Supervoxel-CNN | 0.635 27 | 0.656 41 | 0.711 27 | 0.719 23 | 0.613 18 | 0.757 41 | 0.444 28 | 0.765 14 | 0.534 32 | 0.566 12 | 0.928 44 | 0.478 13 | 0.272 17 | 0.636 11 | 0.531 39 | 0.664 18 | 0.645 43 | 0.508 43 | 0.864 30 | 0.792 27 | 0.611 20 | |
3DSM_DMMF | 0.631 31 | 0.626 45 | 0.745 17 | 0.801 12 | 0.607 19 | 0.751 42 | 0.506 5 | 0.729 24 | 0.565 24 | 0.491 32 | 0.866 57 | 0.434 29 | 0.197 37 | 0.595 18 | 0.630 33 | 0.709 9 | 0.705 27 | 0.560 28 | 0.875 20 | 0.740 47 | 0.491 49 | |
FPConv | ![]() | 0.639 23 | 0.785 16 | 0.760 14 | 0.713 25 | 0.603 20 | 0.798 26 | 0.392 41 | 0.534 49 | 0.603 14 | 0.524 23 | 0.948 18 | 0.457 21 | 0.250 22 | 0.538 31 | 0.723 22 | 0.598 37 | 0.696 31 | 0.614 13 | 0.872 25 | 0.799 19 | 0.567 36 |
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020 | ||||||||||||||||||||||
HPEIN | 0.618 37 | 0.729 32 | 0.668 44 | 0.647 43 | 0.597 21 | 0.766 38 | 0.414 33 | 0.680 33 | 0.520 35 | 0.525 22 | 0.946 23 | 0.432 30 | 0.215 30 | 0.493 41 | 0.599 35 | 0.638 25 | 0.617 48 | 0.570 23 | 0.897 10 | 0.806 15 | 0.605 24 | |
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 | ||||||||||||||||||||||
VACNN++ | 0.638 24 | 0.820 13 | 0.701 32 | 0.687 30 | 0.594 22 | 0.791 31 | 0.430 31 | 0.587 44 | 0.569 23 | 0.529 20 | 0.950 13 | 0.467 18 | 0.253 21 | 0.524 33 | 0.722 23 | 0.618 31 | 0.694 32 | 0.570 23 | 0.793 45 | 0.802 18 | 0.659 9 | |
PointMTL | 0.632 30 | 0.731 31 | 0.688 41 | 0.675 34 | 0.591 23 | 0.784 33 | 0.444 28 | 0.565 47 | 0.610 11 | 0.492 31 | 0.949 15 | 0.456 22 | 0.254 20 | 0.587 20 | 0.706 26 | 0.599 36 | 0.665 40 | 0.612 16 | 0.868 29 | 0.791 30 | 0.579 31 | |
MVPNet | ![]() | 0.641 20 | 0.831 10 | 0.715 26 | 0.671 37 | 0.590 24 | 0.781 34 | 0.394 40 | 0.679 34 | 0.642 6 | 0.553 14 | 0.937 37 | 0.462 20 | 0.256 19 | 0.649 9 | 0.406 48 | 0.626 28 | 0.691 33 | 0.666 5 | 0.877 18 | 0.792 27 | 0.608 23 |
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019 | ||||||||||||||||||||||
FusionAwareConv | 0.630 34 | 0.604 47 | 0.741 20 | 0.766 17 | 0.590 24 | 0.747 43 | 0.501 7 | 0.734 22 | 0.503 38 | 0.527 21 | 0.919 50 | 0.454 23 | 0.323 5 | 0.550 30 | 0.420 47 | 0.678 14 | 0.688 34 | 0.544 35 | 0.896 11 | 0.795 22 | 0.627 17 | |
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020 | ||||||||||||||||||||||
DVVNet | 0.562 46 | 0.648 42 | 0.700 34 | 0.770 15 | 0.586 26 | 0.687 50 | 0.333 48 | 0.650 39 | 0.514 37 | 0.475 38 | 0.906 54 | 0.359 46 | 0.223 28 | 0.340 51 | 0.442 46 | 0.422 54 | 0.668 39 | 0.501 44 | 0.708 52 | 0.779 34 | 0.534 43 | |
SAFNet-seg | ![]() | 0.654 18 | 0.752 27 | 0.734 22 | 0.664 40 | 0.583 27 | 0.815 17 | 0.399 38 | 0.754 16 | 0.639 7 | 0.535 18 | 0.942 31 | 0.470 16 | 0.309 7 | 0.665 8 | 0.539 37 | 0.650 21 | 0.708 25 | 0.635 8 | 0.857 33 | 0.793 25 | 0.642 13 |
PointMRNet-lite | 0.625 35 | 0.643 44 | 0.711 27 | 0.697 28 | 0.581 28 | 0.801 22 | 0.408 35 | 0.670 37 | 0.558 28 | 0.497 30 | 0.944 27 | 0.436 28 | 0.152 49 | 0.617 16 | 0.708 25 | 0.603 34 | 0.743 17 | 0.532 41 | 0.870 28 | 0.784 32 | 0.545 41 | |
RandLA-Net | ![]() | 0.645 19 | 0.778 18 | 0.731 23 | 0.699 26 | 0.577 29 | 0.829 10 | 0.446 24 | 0.736 21 | 0.477 43 | 0.523 25 | 0.945 25 | 0.454 23 | 0.269 18 | 0.484 42 | 0.749 20 | 0.618 31 | 0.738 18 | 0.599 18 | 0.827 37 | 0.792 27 | 0.621 18 |
MCCNN | ![]() | 0.633 29 | 0.866 5 | 0.731 23 | 0.771 14 | 0.576 30 | 0.809 20 | 0.410 34 | 0.684 32 | 0.497 39 | 0.491 32 | 0.949 15 | 0.466 19 | 0.105 55 | 0.581 23 | 0.646 32 | 0.620 29 | 0.680 36 | 0.542 37 | 0.817 40 | 0.795 22 | 0.618 19 |
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 | ||||||||||||||||||||||
PointSPNet | 0.637 25 | 0.734 30 | 0.692 38 | 0.714 24 | 0.576 30 | 0.797 27 | 0.446 24 | 0.743 19 | 0.598 16 | 0.437 44 | 0.942 31 | 0.403 39 | 0.150 50 | 0.626 15 | 0.800 15 | 0.649 22 | 0.697 30 | 0.557 30 | 0.846 35 | 0.777 36 | 0.563 37 | |
PointMRNet | 0.640 22 | 0.717 35 | 0.701 32 | 0.692 29 | 0.576 30 | 0.801 22 | 0.467 18 | 0.716 26 | 0.563 26 | 0.459 40 | 0.953 6 | 0.429 32 | 0.169 43 | 0.581 23 | 0.854 5 | 0.605 33 | 0.710 23 | 0.550 33 | 0.894 12 | 0.793 25 | 0.575 32 | |
SConv | 0.636 26 | 0.830 11 | 0.697 36 | 0.752 20 | 0.572 33 | 0.780 35 | 0.445 26 | 0.716 26 | 0.529 33 | 0.530 19 | 0.951 9 | 0.446 27 | 0.170 42 | 0.507 37 | 0.666 31 | 0.636 26 | 0.682 35 | 0.541 38 | 0.886 16 | 0.799 19 | 0.594 28 | |
HPGCNN | 0.656 17 | 0.698 38 | 0.743 19 | 0.650 41 | 0.564 34 | 0.820 15 | 0.505 6 | 0.758 15 | 0.631 8 | 0.479 36 | 0.945 25 | 0.480 12 | 0.226 26 | 0.572 26 | 0.774 16 | 0.690 12 | 0.735 20 | 0.614 13 | 0.853 34 | 0.776 37 | 0.597 27 | |
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN. | ||||||||||||||||||||||
ROSMRF | 0.580 44 | 0.772 21 | 0.707 29 | 0.681 33 | 0.563 35 | 0.764 39 | 0.362 46 | 0.515 50 | 0.465 47 | 0.465 39 | 0.936 38 | 0.427 35 | 0.207 32 | 0.438 44 | 0.577 36 | 0.536 46 | 0.675 37 | 0.486 47 | 0.723 51 | 0.779 34 | 0.524 45 | |
SIConv | 0.625 35 | 0.830 11 | 0.694 37 | 0.757 18 | 0.563 35 | 0.772 37 | 0.448 22 | 0.647 41 | 0.520 35 | 0.509 26 | 0.949 15 | 0.431 31 | 0.191 38 | 0.496 40 | 0.614 34 | 0.647 24 | 0.672 38 | 0.535 40 | 0.876 19 | 0.783 33 | 0.571 33 | |
PointConv-SFPN | 0.641 20 | 0.776 20 | 0.703 30 | 0.721 22 | 0.557 37 | 0.826 12 | 0.451 20 | 0.672 36 | 0.563 26 | 0.483 35 | 0.943 30 | 0.425 36 | 0.162 45 | 0.644 10 | 0.726 21 | 0.659 20 | 0.709 24 | 0.572 22 | 0.875 20 | 0.786 31 | 0.559 39 | |
APCF-Net | 0.631 31 | 0.742 28 | 0.687 43 | 0.672 35 | 0.557 37 | 0.792 29 | 0.408 35 | 0.665 38 | 0.545 30 | 0.508 27 | 0.952 8 | 0.428 33 | 0.186 39 | 0.634 13 | 0.702 27 | 0.620 29 | 0.706 26 | 0.555 31 | 0.873 24 | 0.798 21 | 0.581 30 | |
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL | ||||||||||||||||||||||
SPH3D-GCN | ![]() | 0.610 38 | 0.858 7 | 0.772 11 | 0.489 54 | 0.532 39 | 0.792 29 | 0.404 37 | 0.643 42 | 0.570 22 | 0.507 28 | 0.935 40 | 0.414 38 | 0.046 60 | 0.510 35 | 0.702 27 | 0.602 35 | 0.705 27 | 0.549 34 | 0.859 32 | 0.773 38 | 0.534 43 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020 | ||||||||||||||||||||||
PointNet2-SFPN | 0.631 31 | 0.771 22 | 0.692 38 | 0.672 35 | 0.524 40 | 0.837 7 | 0.440 30 | 0.706 29 | 0.538 31 | 0.446 42 | 0.944 27 | 0.421 37 | 0.219 29 | 0.552 29 | 0.751 19 | 0.591 39 | 0.737 19 | 0.543 36 | 0.901 9 | 0.768 40 | 0.557 40 | |
AttAN | 0.609 39 | 0.760 25 | 0.667 45 | 0.649 42 | 0.521 41 | 0.793 28 | 0.457 19 | 0.648 40 | 0.528 34 | 0.434 46 | 0.947 21 | 0.401 40 | 0.153 48 | 0.454 43 | 0.721 24 | 0.648 23 | 0.717 22 | 0.536 39 | 0.904 7 | 0.765 41 | 0.485 50 | |
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020 | ||||||||||||||||||||||
SegGCN | ![]() | 0.589 42 | 0.833 9 | 0.731 23 | 0.539 52 | 0.514 42 | 0.789 32 | 0.448 22 | 0.467 51 | 0.573 20 | 0.484 34 | 0.936 38 | 0.396 41 | 0.061 59 | 0.501 38 | 0.507 42 | 0.594 38 | 0.700 29 | 0.563 27 | 0.874 22 | 0.771 39 | 0.493 48 |
Huan Lei, Naveed Akhtar, and Ajmal Mian: SegGCN: Efficient 3D Point Cloud Segmentation with Fuzzy Spherical Kernel. CVPR 2020 | ||||||||||||||||||||||
TextureNet | ![]() | 0.566 45 | 0.672 40 | 0.664 46 | 0.671 37 | 0.494 43 | 0.719 46 | 0.445 26 | 0.678 35 | 0.411 52 | 0.396 47 | 0.935 40 | 0.356 47 | 0.225 27 | 0.412 48 | 0.535 38 | 0.565 44 | 0.636 47 | 0.464 49 | 0.794 44 | 0.680 52 | 0.568 35 |
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 | ||||||||||||||||||||||
Pointnet++ & Feature | ![]() | 0.557 47 | 0.735 29 | 0.661 47 | 0.686 31 | 0.491 44 | 0.744 44 | 0.392 41 | 0.539 48 | 0.451 48 | 0.375 50 | 0.946 23 | 0.376 44 | 0.205 33 | 0.403 49 | 0.356 50 | 0.553 45 | 0.643 44 | 0.497 45 | 0.824 39 | 0.756 43 | 0.515 46 |
DPC | 0.592 41 | 0.720 33 | 0.700 34 | 0.602 48 | 0.480 45 | 0.762 40 | 0.380 45 | 0.713 28 | 0.585 19 | 0.437 44 | 0.940 35 | 0.369 45 | 0.288 12 | 0.434 46 | 0.509 41 | 0.590 41 | 0.639 46 | 0.567 26 | 0.772 47 | 0.755 44 | 0.592 29 | |
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020 | ||||||||||||||||||||||
3DMV, FTSDF | 0.501 49 | 0.558 52 | 0.608 54 | 0.424 59 | 0.478 46 | 0.690 49 | 0.246 55 | 0.586 45 | 0.468 45 | 0.450 41 | 0.911 52 | 0.394 42 | 0.160 46 | 0.438 44 | 0.212 56 | 0.432 53 | 0.541 54 | 0.475 48 | 0.742 49 | 0.727 48 | 0.477 51 | |
CCRFNet | 0.589 42 | 0.766 24 | 0.659 48 | 0.683 32 | 0.470 47 | 0.740 45 | 0.387 44 | 0.620 43 | 0.490 41 | 0.476 37 | 0.922 48 | 0.355 48 | 0.245 24 | 0.511 34 | 0.511 40 | 0.571 43 | 0.643 44 | 0.493 46 | 0.872 25 | 0.762 42 | 0.600 25 | |
LAP-D | 0.594 40 | 0.720 33 | 0.692 38 | 0.637 45 | 0.456 48 | 0.773 36 | 0.391 43 | 0.730 23 | 0.587 17 | 0.445 43 | 0.940 35 | 0.381 43 | 0.288 12 | 0.434 46 | 0.453 44 | 0.591 39 | 0.649 41 | 0.581 21 | 0.777 46 | 0.749 46 | 0.610 22 | |
3DMV | 0.484 51 | 0.484 56 | 0.538 57 | 0.643 44 | 0.424 49 | 0.606 58 | 0.310 49 | 0.574 46 | 0.433 51 | 0.378 49 | 0.796 59 | 0.301 51 | 0.214 31 | 0.537 32 | 0.208 57 | 0.472 52 | 0.507 58 | 0.413 56 | 0.693 53 | 0.602 58 | 0.539 42 | |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 | ||||||||||||||||||||||
PCNN | 0.498 50 | 0.559 51 | 0.644 51 | 0.560 51 | 0.420 50 | 0.711 48 | 0.229 57 | 0.414 52 | 0.436 49 | 0.352 51 | 0.941 33 | 0.324 50 | 0.155 47 | 0.238 56 | 0.387 49 | 0.493 48 | 0.529 55 | 0.509 42 | 0.813 42 | 0.751 45 | 0.504 47 | |
PanopticFusion-label | 0.529 48 | 0.491 55 | 0.688 41 | 0.604 47 | 0.386 51 | 0.632 55 | 0.225 59 | 0.705 30 | 0.434 50 | 0.293 54 | 0.815 58 | 0.348 49 | 0.241 25 | 0.499 39 | 0.669 30 | 0.507 47 | 0.649 41 | 0.442 53 | 0.796 43 | 0.602 58 | 0.561 38 | |
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear) | ||||||||||||||||||||||
FCPN | ![]() | 0.447 53 | 0.679 39 | 0.604 55 | 0.578 50 | 0.380 52 | 0.682 51 | 0.291 52 | 0.106 61 | 0.483 42 | 0.258 59 | 0.920 49 | 0.258 55 | 0.025 61 | 0.231 58 | 0.325 51 | 0.480 51 | 0.560 52 | 0.463 50 | 0.725 50 | 0.666 54 | 0.231 61 |
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018 | ||||||||||||||||||||||
Tangent Convolutions | ![]() | 0.438 56 | 0.437 59 | 0.646 50 | 0.474 56 | 0.369 53 | 0.645 54 | 0.353 47 | 0.258 58 | 0.282 60 | 0.279 55 | 0.918 51 | 0.298 52 | 0.147 52 | 0.283 53 | 0.294 52 | 0.487 49 | 0.562 51 | 0.427 55 | 0.619 56 | 0.633 55 | 0.352 56 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 | ||||||||||||||||||||||
PNET2 | 0.442 54 | 0.548 53 | 0.548 56 | 0.597 49 | 0.363 54 | 0.628 56 | 0.300 50 | 0.292 56 | 0.374 55 | 0.307 53 | 0.881 56 | 0.268 54 | 0.186 39 | 0.238 56 | 0.204 58 | 0.407 55 | 0.506 59 | 0.449 52 | 0.667 54 | 0.620 56 | 0.462 53 | |
ScanNet+FTSDF | 0.383 59 | 0.297 61 | 0.491 59 | 0.432 58 | 0.358 55 | 0.612 57 | 0.274 53 | 0.116 60 | 0.411 52 | 0.265 57 | 0.904 55 | 0.229 58 | 0.079 57 | 0.250 54 | 0.185 59 | 0.320 59 | 0.510 56 | 0.385 57 | 0.548 58 | 0.597 60 | 0.394 55 | |
SurfaceConvPF | 0.442 54 | 0.505 54 | 0.622 52 | 0.380 60 | 0.342 56 | 0.654 53 | 0.227 58 | 0.397 54 | 0.367 56 | 0.276 56 | 0.924 46 | 0.240 57 | 0.198 36 | 0.359 50 | 0.262 53 | 0.366 56 | 0.581 50 | 0.435 54 | 0.640 55 | 0.668 53 | 0.398 54 | |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. | ||||||||||||||||||||||
PointCNN with RGB | ![]() | 0.458 52 | 0.577 50 | 0.611 53 | 0.356 61 | 0.321 57 | 0.715 47 | 0.299 51 | 0.376 55 | 0.328 58 | 0.319 52 | 0.944 27 | 0.285 53 | 0.164 44 | 0.216 59 | 0.229 55 | 0.484 50 | 0.545 53 | 0.456 51 | 0.755 48 | 0.709 49 | 0.475 52 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 | ||||||||||||||||||||||
SPLAT Net | ![]() | 0.393 58 | 0.472 58 | 0.511 58 | 0.606 46 | 0.311 58 | 0.656 52 | 0.245 56 | 0.405 53 | 0.328 58 | 0.197 60 | 0.927 45 | 0.227 59 | 0.000 63 | 0.001 63 | 0.249 54 | 0.271 61 | 0.510 56 | 0.383 58 | 0.593 57 | 0.699 50 | 0.267 59 |
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 | ||||||||||||||||||||||
ScanNet | ![]() | 0.306 62 | 0.203 62 | 0.366 61 | 0.501 53 | 0.311 58 | 0.524 60 | 0.211 60 | 0.002 63 | 0.342 57 | 0.189 61 | 0.786 60 | 0.145 62 | 0.102 56 | 0.245 55 | 0.152 60 | 0.318 60 | 0.348 61 | 0.300 61 | 0.460 61 | 0.437 62 | 0.182 62 |
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17 | ||||||||||||||||||||||
subcloud_weak | 0.411 57 | 0.479 57 | 0.650 49 | 0.475 55 | 0.285 60 | 0.519 61 | 0.087 62 | 0.725 25 | 0.396 54 | 0.386 48 | 0.621 62 | 0.250 56 | 0.117 53 | 0.338 52 | 0.443 45 | 0.188 62 | 0.594 49 | 0.369 59 | 0.377 62 | 0.616 57 | 0.306 57 | |
PointNet++ | ![]() | 0.339 60 | 0.584 49 | 0.478 60 | 0.458 57 | 0.256 61 | 0.360 62 | 0.250 54 | 0.247 59 | 0.278 61 | 0.261 58 | 0.677 61 | 0.183 60 | 0.117 53 | 0.212 60 | 0.145 61 | 0.364 57 | 0.346 62 | 0.232 62 | 0.548 58 | 0.523 61 | 0.252 60 |
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 61 | 0.353 60 | 0.290 62 | 0.278 62 | 0.166 62 | 0.553 59 | 0.169 61 | 0.286 57 | 0.147 62 | 0.148 62 | 0.908 53 | 0.182 61 | 0.064 58 | 0.023 62 | 0.018 63 | 0.354 58 | 0.363 60 | 0.345 60 | 0.546 60 | 0.685 51 | 0.278 58 |
ERROR | 0.054 63 | 0.000 63 | 0.041 63 | 0.172 63 | 0.030 63 | 0.062 63 | 0.001 63 | 0.035 62 | 0.004 63 | 0.051 63 | 0.143 63 | 0.019 63 | 0.003 62 | 0.041 61 | 0.050 62 | 0.003 63 | 0.054 63 | 0.018 63 | 0.005 63 | 0.264 63 | 0.082 63 | |