The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).



This table lists the benchmark results for the 3D semantic label scenario.


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PonderV20.785 20.978 10.800 230.833 200.788 30.853 140.545 140.910 40.713 10.705 40.979 10.596 50.390 10.769 100.832 380.821 30.792 270.730 10.975 10.897 30.785 3
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm.
PTv3 ScanNet0.794 10.941 30.813 150.851 60.782 50.890 20.597 10.916 10.696 70.713 30.979 10.635 10.384 20.793 20.907 60.821 30.790 280.696 100.967 30.903 10.805 1
Mix3Dpermissive0.781 30.964 20.855 10.843 140.781 60.858 100.575 50.831 280.685 110.714 20.979 10.594 60.310 230.801 10.892 130.841 20.819 30.723 40.940 110.887 50.725 20
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
IPCA0.731 280.890 130.837 30.864 20.726 280.873 40.530 220.824 320.489 810.647 170.978 40.609 40.336 130.624 450.733 540.758 160.776 340.570 600.949 60.877 110.728 16
PNE0.755 110.786 390.835 40.834 190.758 120.849 190.570 70.836 270.648 230.668 140.978 40.581 140.367 50.683 300.856 260.804 50.801 170.678 160.961 40.889 40.716 25
P. Hermosilla: Point Neighborhood Embeddings.
OccuSeg+Semantic0.764 70.758 550.796 270.839 160.746 210.907 10.562 90.850 200.680 130.672 120.978 40.610 30.335 140.777 60.819 410.847 10.830 10.691 120.972 20.885 70.727 18
PointConvFormer0.749 150.793 370.790 320.807 330.750 200.856 110.524 230.881 110.588 490.642 230.977 70.591 80.274 410.781 40.929 20.804 50.796 210.642 300.947 70.885 70.715 26
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
One-Thing-One-Click0.693 390.743 610.794 290.655 810.684 370.822 450.497 370.719 620.622 320.617 310.977 70.447 640.339 120.750 230.664 700.703 390.790 280.596 500.946 80.855 280.647 45
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Swin3Dpermissive0.779 40.861 190.818 120.836 170.790 20.875 30.576 40.905 50.704 40.739 10.969 90.611 20.349 90.756 180.958 10.702 400.805 120.708 70.916 280.898 20.801 2
One Thing One Click0.701 370.825 300.796 270.723 580.716 300.832 350.433 690.816 340.634 280.609 340.969 90.418 770.344 100.559 630.833 370.715 330.808 110.560 650.902 370.847 340.680 36
ClickSeg_Semantic0.703 350.774 470.800 230.793 420.760 110.847 230.471 460.802 410.463 880.634 260.968 110.491 430.271 450.726 270.910 50.706 360.815 50.551 710.878 550.833 390.570 71
PPT-SpUNet-Joint0.766 50.932 40.794 290.829 220.751 190.854 120.540 170.903 60.630 300.672 120.963 120.565 190.357 70.788 30.900 90.737 220.802 130.685 140.950 50.887 50.780 4
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.
OA-CNN-L_ScanNet200.756 100.783 410.826 50.858 40.776 70.837 300.548 130.896 90.649 220.675 100.962 130.586 110.335 140.771 90.802 450.770 120.787 300.691 120.936 140.880 100.761 8
DMF-Net0.752 130.906 110.793 310.802 370.689 350.825 410.556 100.867 140.681 120.602 390.960 140.555 240.365 60.779 50.859 230.747 190.795 240.717 60.917 270.856 260.764 7
C.Yang, Y.Yan, W.Zhao, J.Ye, X.Yang, A.Hussain, B.Dong, K.Huang: Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation. ICONIP 2023
OctFormerpermissive0.766 50.925 60.808 190.849 80.786 40.846 240.566 80.876 120.690 90.674 110.960 140.576 150.226 610.753 200.904 70.777 100.815 50.722 50.923 240.877 110.776 6
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
PointTransformerV20.752 130.742 620.809 180.872 10.758 120.860 90.552 110.891 100.610 370.687 50.960 140.559 220.304 260.766 120.926 30.767 130.797 200.644 290.942 90.876 140.722 22
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
O-CNNpermissive0.762 90.924 70.823 60.844 130.770 80.852 160.577 30.847 230.711 20.640 240.958 170.592 70.217 670.762 140.888 140.758 160.813 80.726 20.932 190.868 170.744 11
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
Retro-FPN0.744 210.842 250.800 230.767 510.740 230.836 320.541 160.914 20.672 150.626 280.958 170.552 250.272 430.777 60.886 160.696 410.801 170.674 210.941 100.858 240.717 23
Peng Xiang*, Xin Wen*, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
CU-Hybrid Net0.764 70.924 70.819 100.840 150.757 140.853 140.580 20.848 210.709 30.643 200.958 170.587 100.295 290.753 200.884 170.758 160.815 50.725 30.927 210.867 180.743 12
ConDaFormer0.755 110.927 50.822 70.836 170.801 10.849 190.516 270.864 170.651 210.680 90.958 170.584 130.282 360.759 160.855 280.728 240.802 130.678 160.880 540.873 160.756 9
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Guisong Xia, Dacheng Tao: ConDaFormer : Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding. Neurips, 2023
BPNetcopyleft0.749 150.909 90.818 120.811 300.752 170.839 290.485 420.842 240.673 140.644 190.957 210.528 330.305 250.773 80.859 230.788 70.818 40.693 110.916 280.856 260.723 21
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
VMNetpermissive0.746 190.870 170.838 20.858 40.729 270.850 180.501 320.874 130.587 500.658 160.956 220.564 200.299 270.765 130.900 90.716 320.812 90.631 350.939 120.858 240.709 27
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)
MSP0.748 170.623 880.804 210.859 30.745 220.824 430.501 320.912 30.690 90.685 70.956 220.567 180.320 200.768 110.918 40.720 290.802 130.676 190.921 250.881 90.779 5
StratifiedFormerpermissive0.747 180.901 120.803 220.845 120.757 140.846 240.512 280.825 310.696 70.645 180.956 220.576 150.262 520.744 250.861 220.742 200.770 390.705 80.899 400.860 230.734 13
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
RPN0.736 260.776 450.790 320.851 60.754 160.854 120.491 410.866 150.596 470.686 60.955 250.536 280.342 110.624 450.869 190.787 80.802 130.628 360.927 210.875 150.704 29
EQ-Net0.743 220.620 890.799 260.849 80.730 260.822 450.493 390.897 80.664 160.681 80.955 250.562 210.378 30.760 150.903 80.738 210.801 170.673 220.907 320.877 110.745 10
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
PointTransformer++0.725 290.727 700.811 170.819 250.765 100.841 270.502 310.814 370.621 330.623 300.955 250.556 230.284 350.620 470.866 200.781 90.757 500.648 270.932 190.862 210.709 27
LargeKernel3D0.739 250.909 90.820 90.806 350.740 230.852 160.545 140.826 300.594 480.643 200.955 250.541 270.263 510.723 280.858 250.775 110.767 400.678 160.933 170.848 330.694 32
Yukang Chen*, Jianhui Liu*, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
SparseConvNet0.725 290.647 850.821 80.846 110.721 290.869 50.533 190.754 520.603 430.614 320.955 250.572 170.325 180.710 290.870 180.724 270.823 20.628 360.934 160.865 200.683 35
SAT0.742 230.860 200.765 440.819 250.769 90.848 210.533 190.829 290.663 170.631 270.955 250.586 110.274 410.753 200.896 110.729 230.760 460.666 240.921 250.855 280.733 14
MatchingNet0.724 310.812 340.812 160.810 310.735 250.834 340.495 380.860 180.572 560.602 390.954 310.512 370.280 380.757 170.845 340.725 260.780 320.606 460.937 130.851 320.700 31
VI-PointConv0.676 490.770 510.754 510.783 460.621 550.814 550.552 110.758 500.571 580.557 500.954 310.529 320.268 490.530 720.682 650.675 490.719 620.603 470.888 480.833 390.665 39
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
LRPNet0.742 230.816 320.806 200.807 330.752 170.828 390.575 50.839 260.699 50.637 250.954 310.520 350.320 200.755 190.834 360.760 150.772 360.676 190.915 300.862 210.717 23
PointMetaBase0.714 330.835 260.785 340.821 230.684 370.846 240.531 210.865 160.614 340.596 430.953 340.500 400.246 570.674 310.888 140.692 420.764 420.624 380.849 750.844 380.675 37
PointConvpermissive0.666 520.781 420.759 470.699 660.644 500.822 450.475 440.779 450.564 610.504 700.953 340.428 710.203 740.586 540.754 500.661 550.753 510.588 560.902 370.813 520.642 46
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointMRNet0.640 610.717 730.701 720.692 690.576 700.801 630.467 500.716 630.563 620.459 800.953 340.429 700.169 870.581 550.854 290.605 740.710 640.550 720.894 440.793 640.575 69
INS-Conv-semantic0.717 320.751 580.759 470.812 290.704 320.868 60.537 180.842 240.609 390.608 350.953 340.534 300.293 300.616 480.864 210.719 310.793 250.640 310.933 170.845 370.663 40
PicassoNet-IIpermissive0.692 400.732 660.772 400.786 430.677 390.866 70.517 260.848 210.509 740.626 280.952 380.536 280.225 630.545 690.704 610.689 460.810 100.564 640.903 360.854 300.729 15
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
APCF-Net0.631 690.742 620.687 840.672 740.557 760.792 700.408 740.665 760.545 640.508 670.952 380.428 710.186 810.634 410.702 620.620 700.706 680.555 690.873 610.798 590.581 67
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
MinkowskiNetpermissive0.736 260.859 210.818 120.832 210.709 310.840 280.521 250.853 190.660 190.643 200.951 400.544 260.286 340.731 260.893 120.675 490.772 360.683 150.874 600.852 310.727 18
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
PointASNLpermissive0.666 520.703 760.781 370.751 570.655 440.830 360.471 460.769 480.474 840.537 560.951 400.475 490.279 390.635 400.698 640.675 490.751 520.553 700.816 820.806 540.703 30
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
joint point-basedpermissive0.634 670.614 900.778 380.667 780.633 540.825 410.420 720.804 390.467 860.561 490.951 400.494 410.291 310.566 600.458 870.579 840.764 420.559 670.838 770.814 500.598 63
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
SConv0.636 650.830 280.697 750.752 560.572 720.780 750.445 610.716 630.529 670.530 590.951 400.446 650.170 860.507 780.666 690.636 670.682 770.541 780.886 490.799 570.594 65
DGNet0.684 440.712 740.784 350.782 470.658 420.835 330.499 360.823 330.641 250.597 420.950 440.487 440.281 370.575 570.619 740.647 620.764 420.620 410.871 660.846 360.688 34
PPCNN++permissive0.663 540.746 590.708 680.722 590.638 520.820 480.451 540.566 890.599 450.541 540.950 440.510 380.313 220.648 370.819 410.616 730.682 770.590 540.869 670.810 530.656 42
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
ROSMRF3D0.673 500.789 380.748 540.763 530.635 530.814 550.407 760.747 540.581 540.573 470.950 440.484 450.271 450.607 490.754 500.649 590.774 350.596 500.883 500.823 450.606 58
FusionNet0.688 420.704 750.741 610.754 550.656 430.829 370.501 320.741 570.609 390.548 520.950 440.522 340.371 40.633 420.756 490.715 330.771 380.623 390.861 710.814 500.658 41
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
SIConv0.625 750.830 280.694 770.757 540.563 740.772 790.448 580.647 790.520 700.509 660.949 480.431 690.191 790.496 800.614 750.647 620.672 810.535 810.876 570.783 730.571 70
PointMTL0.632 680.731 670.688 820.675 730.591 630.784 720.444 640.565 900.610 370.492 710.949 480.456 570.254 540.587 520.706 600.599 770.665 830.612 450.868 680.791 700.579 68
Virtual MVFusion0.746 190.771 490.819 100.848 100.702 330.865 80.397 790.899 70.699 50.664 150.948 500.588 90.330 160.746 240.851 320.764 140.796 210.704 90.935 150.866 190.728 16
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
FPConvpermissive0.639 620.785 400.760 460.713 640.603 590.798 650.392 810.534 940.603 430.524 620.948 500.457 560.250 550.538 700.723 570.598 780.696 720.614 420.872 630.799 570.567 74
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
AttAN0.609 790.760 540.667 860.649 830.521 820.793 680.457 530.648 780.528 680.434 870.947 520.401 810.153 930.454 860.721 580.648 610.717 630.536 800.904 340.765 800.485 92
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
PD-Net0.638 630.797 360.769 430.641 870.590 640.820 480.461 520.537 930.637 270.536 570.947 520.388 840.206 710.656 340.668 680.647 620.732 590.585 570.868 680.793 640.473 96
RFCR0.702 360.889 140.745 570.813 280.672 400.818 520.493 390.815 360.623 310.610 330.947 520.470 510.249 560.594 510.848 330.705 370.779 330.646 280.892 450.823 450.611 54
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
Superpoint Network0.683 470.851 230.728 650.800 390.653 450.806 600.468 480.804 390.572 560.602 390.946 550.453 610.239 600.519 740.822 390.689 460.762 450.595 520.895 430.827 430.630 51
Pointnet++ & Featurepermissive0.557 890.735 640.661 880.686 700.491 860.744 870.392 810.539 920.451 900.375 930.946 550.376 860.205 720.403 920.356 950.553 870.643 880.497 870.824 810.756 830.515 87
HPEIN0.618 770.729 680.668 850.647 840.597 620.766 800.414 730.680 710.520 700.525 610.946 550.432 670.215 680.493 810.599 760.638 660.617 920.570 600.897 410.806 540.605 60
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-Geometry Netpermissive0.685 430.866 180.748 540.819 250.645 490.794 670.450 570.802 410.587 500.604 370.945 580.464 530.201 750.554 650.840 350.723 280.732 590.602 480.907 320.822 470.603 61
RandLA-Netpermissive0.645 580.778 430.731 640.699 660.577 690.829 370.446 590.736 580.477 830.523 640.945 580.454 580.269 470.484 830.749 530.618 710.738 550.599 490.827 790.792 670.621 53
contrastBoundarypermissive0.705 340.769 520.775 390.809 320.687 360.820 480.439 670.812 380.661 180.591 450.945 580.515 360.171 850.633 420.856 260.720 290.796 210.668 230.889 470.847 340.689 33
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
HPGCNN0.656 560.698 780.743 590.650 820.564 730.820 480.505 300.758 500.631 290.479 740.945 580.480 470.226 610.572 580.774 480.690 440.735 570.614 420.853 740.776 770.597 64
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
VACNN++0.684 440.728 690.757 500.776 480.690 340.804 620.464 510.816 340.577 550.587 460.945 580.508 390.276 400.671 320.710 590.663 540.750 530.589 550.881 520.832 410.653 43
Feature_GeometricNetpermissive0.690 410.884 150.754 510.795 400.647 470.818 520.422 710.802 410.612 360.604 370.945 580.462 540.189 800.563 620.853 300.726 250.765 410.632 340.904 340.821 480.606 58
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
PointNet2-SFPN0.631 690.771 490.692 790.672 740.524 810.837 300.440 660.706 680.538 650.446 820.944 640.421 760.219 660.552 660.751 520.591 800.737 560.543 770.901 390.768 790.557 78
dtc_net0.625 750.703 760.751 530.794 410.535 790.848 210.480 430.676 740.528 680.469 770.944 640.454 580.004 1070.464 850.636 720.704 380.758 480.548 740.924 230.787 710.492 90
PointCNN with RGBpermissive0.458 970.577 940.611 970.356 1070.321 1040.715 900.299 970.376 1010.328 1040.319 980.944 640.285 980.164 890.216 1050.229 1000.484 930.545 990.456 950.755 880.709 930.475 95
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
PointConv-SFPN0.641 590.776 450.703 700.721 600.557 760.826 400.451 540.672 750.563 620.483 730.943 670.425 740.162 900.644 380.726 550.659 560.709 660.572 590.875 580.786 720.559 77
SAFNet-segpermissive0.654 570.752 570.734 630.664 790.583 680.815 540.399 780.754 520.639 260.535 580.942 680.470 510.309 240.665 330.539 790.650 580.708 670.635 330.857 730.793 640.642 46
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
PointSPNet0.637 640.734 650.692 790.714 630.576 700.797 660.446 590.743 560.598 460.437 850.942 680.403 800.150 940.626 440.800 460.649 590.697 710.557 680.846 760.777 760.563 75
PCNN0.498 950.559 950.644 920.560 960.420 950.711 910.229 1030.414 970.436 930.352 960.941 700.324 940.155 920.238 1020.387 940.493 910.529 1010.509 830.813 830.751 850.504 89
DCM-Net0.658 550.778 430.702 710.806 350.619 560.813 580.468 480.693 700.494 770.524 620.941 700.449 630.298 280.510 760.821 400.675 490.727 610.568 620.826 800.803 560.637 48
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
DPC0.592 820.720 710.700 730.602 920.480 870.762 830.380 860.713 660.585 530.437 850.940 720.369 870.288 320.434 890.509 850.590 820.639 900.567 630.772 870.755 840.592 66
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
LAP-D0.594 810.720 710.692 790.637 880.456 910.773 780.391 830.730 600.587 500.445 840.940 720.381 850.288 320.434 890.453 890.591 800.649 850.581 580.777 860.749 860.610 56
DenSeR0.628 730.800 350.625 940.719 610.545 780.806 600.445 610.597 840.448 910.519 650.938 740.481 460.328 170.489 820.499 860.657 570.759 470.592 530.881 520.797 600.634 49
DGCNN_reproducecopyleft0.446 990.474 1030.623 950.463 1010.366 990.651 970.310 930.389 1000.349 1020.330 970.937 750.271 1000.126 970.285 980.224 1010.350 1040.577 940.445 980.625 1000.723 910.394 99
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
MVPNetpermissive0.641 590.831 270.715 660.671 760.590 640.781 730.394 800.679 720.642 240.553 510.937 750.462 540.256 530.649 360.406 920.626 690.691 740.666 240.877 560.792 670.608 57
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
ROSMRF0.580 840.772 480.707 690.681 720.563 740.764 810.362 880.515 950.465 870.465 790.936 770.427 730.207 700.438 870.577 770.536 880.675 800.486 900.723 930.779 740.524 86
KP-FCNN0.684 440.847 240.758 490.784 450.647 470.814 550.473 450.772 470.605 410.594 440.935 780.450 620.181 830.587 520.805 440.690 440.785 310.614 420.882 510.819 490.632 50
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
TextureNetpermissive0.566 870.672 820.664 870.671 760.494 850.719 890.445 610.678 730.411 970.396 900.935 780.356 890.225 630.412 910.535 800.565 860.636 910.464 930.794 850.680 970.568 73
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
SPH3D-GCNpermissive0.610 780.858 220.772 400.489 990.532 800.792 700.404 770.643 800.570 590.507 690.935 780.414 780.046 1040.510 760.702 620.602 760.705 690.549 730.859 720.773 780.534 84
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
JSENetpermissive0.699 380.881 160.762 450.821 230.667 410.800 640.522 240.792 440.613 350.607 360.935 780.492 420.205 720.576 560.853 300.691 430.758 480.652 260.872 630.828 420.649 44
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
PointContrast_LA_SEM0.683 470.757 560.784 350.786 430.639 510.824 430.408 740.775 460.604 420.541 540.934 820.532 310.269 470.552 660.777 470.645 650.793 250.640 310.913 310.824 440.671 38
wsss-transformer0.600 800.634 860.743 590.697 680.601 610.781 730.437 680.585 870.493 780.446 820.933 830.394 820.011 1060.654 350.661 710.603 750.733 580.526 820.832 780.761 820.480 93
subcloud_weak0.516 920.676 800.591 1010.609 890.442 920.774 770.335 910.597 840.422 960.357 950.932 840.341 920.094 1000.298 970.528 830.473 950.676 790.495 880.602 1020.721 920.349 103
SegGroup_sempermissive0.627 740.818 310.747 560.701 650.602 600.764 810.385 850.629 810.490 790.508 670.931 850.409 790.201 750.564 610.725 560.618 710.692 730.539 790.873 610.794 620.548 81
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
Supervoxel-CNN0.635 660.656 830.711 670.719 610.613 570.757 840.444 640.765 490.534 660.566 480.928 860.478 480.272 430.636 390.531 810.664 530.645 870.508 850.864 700.792 670.611 54
SPLAT Netcopyleft0.393 1040.472 1040.511 1040.606 900.311 1050.656 950.245 1020.405 980.328 1040.197 1060.927 870.227 1050.000 1090.001 1090.249 990.271 1070.510 1020.383 1050.593 1030.699 950.267 105
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
SurfaceConvPF0.442 1000.505 990.622 960.380 1060.342 1020.654 960.227 1040.397 990.367 1000.276 1020.924 880.240 1030.198 770.359 940.262 980.366 1010.581 930.435 1000.640 990.668 980.398 98
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
SALANet0.670 510.816 320.770 420.768 500.652 460.807 590.451 540.747 540.659 200.545 530.924 880.473 500.149 950.571 590.811 430.635 680.746 540.623 390.892 450.794 620.570 71
CCRFNet0.589 830.766 530.659 890.683 710.470 900.740 880.387 840.620 830.490 790.476 750.922 900.355 900.245 580.511 750.511 840.571 850.643 880.493 890.872 630.762 810.600 62
FCPNpermissive0.447 980.679 790.604 1000.578 950.380 970.682 940.291 980.106 1070.483 820.258 1050.920 910.258 1020.025 1050.231 1040.325 960.480 940.560 970.463 940.725 920.666 990.231 107
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
FusionAwareConv0.630 720.604 920.741 610.766 520.590 640.747 860.501 320.734 590.503 760.527 600.919 920.454 580.323 190.550 680.420 910.678 480.688 750.544 750.896 420.795 610.627 52
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
Tangent Convolutionspermissive0.438 1020.437 1050.646 910.474 1000.369 980.645 980.353 890.258 1040.282 1060.279 1010.918 930.298 970.147 960.283 990.294 970.487 920.562 960.427 1010.619 1010.633 1010.352 102
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DMV, FTSDF0.501 940.558 960.608 990.424 1050.478 880.690 920.246 1010.586 860.468 850.450 810.911 940.394 820.160 910.438 870.212 1020.432 980.541 1000.475 920.742 900.727 900.477 94
SSC-UNetpermissive0.308 1070.353 1060.290 1080.278 1080.166 1080.553 1050.169 1070.286 1030.147 1080.148 1080.908 950.182 1070.064 1030.023 1080.018 1090.354 1030.363 1060.345 1060.546 1060.685 960.278 104
DVVNet0.562 880.648 840.700 730.770 490.586 670.687 930.333 920.650 770.514 730.475 760.906 960.359 880.223 650.340 950.442 900.422 990.668 820.501 860.708 940.779 740.534 84
ScanNet+FTSDF0.383 1050.297 1070.491 1050.432 1040.358 1010.612 1030.274 990.116 1060.411 970.265 1030.904 970.229 1040.079 1020.250 1000.185 1050.320 1050.510 1020.385 1040.548 1040.597 1060.394 99
GMLPs0.538 900.495 1000.693 780.647 840.471 890.793 680.300 950.477 960.505 750.358 940.903 980.327 930.081 1010.472 840.529 820.448 970.710 640.509 830.746 890.737 880.554 80
SQN_0.1%0.569 860.676 800.696 760.657 800.497 840.779 760.424 700.548 910.515 720.376 920.902 990.422 750.357 70.379 930.456 880.596 790.659 840.544 750.685 960.665 1000.556 79
PNET20.442 1000.548 970.548 1020.597 930.363 1000.628 1020.300 950.292 1020.374 990.307 990.881 1000.268 1010.186 810.238 1020.204 1040.407 1000.506 1050.449 960.667 980.620 1020.462 97
SD-DETR0.576 850.746 590.609 980.445 1030.517 830.643 990.366 870.714 650.456 890.468 780.870 1010.432 670.264 500.558 640.674 660.586 830.688 750.482 910.739 910.733 890.537 83
3DSM_DMMF0.631 690.626 870.745 570.801 380.607 580.751 850.506 290.729 610.565 600.491 720.866 1020.434 660.197 780.595 500.630 730.709 350.705 690.560 650.875 580.740 870.491 91
PanopticFusion-label0.529 910.491 1010.688 820.604 910.386 960.632 1000.225 1050.705 690.434 940.293 1000.815 1030.348 910.241 590.499 790.669 670.507 900.649 850.442 990.796 840.602 1030.561 76
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
3DMV0.484 960.484 1020.538 1030.643 860.424 940.606 1040.310 930.574 880.433 950.378 910.796 1040.301 960.214 690.537 710.208 1030.472 960.507 1040.413 1020.693 950.602 1030.539 82
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
ScanNetpermissive0.306 1080.203 1080.366 1070.501 980.311 1050.524 1060.211 1060.002 1090.342 1030.189 1070.786 1050.145 1080.102 990.245 1010.152 1060.318 1060.348 1070.300 1070.460 1070.437 1080.182 108
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
Online SegFusion0.515 930.607 910.644 920.579 940.434 930.630 1010.353 890.628 820.440 920.410 880.762 1060.307 950.167 880.520 730.403 930.516 890.565 950.447 970.678 970.701 940.514 88
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++permissive0.339 1060.584 930.478 1060.458 1020.256 1070.360 1080.250 1000.247 1050.278 1070.261 1040.677 1070.183 1060.117 980.212 1060.145 1070.364 1020.346 1080.232 1080.548 1040.523 1070.252 106
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
3DWSSS0.425 1030.525 980.647 900.522 970.324 1030.488 1070.077 1080.712 670.353 1010.401 890.636 1080.281 990.176 840.340 950.565 780.175 1080.551 980.398 1030.370 1080.602 1030.361 101
ERROR0.054 1090.000 1090.041 1090.172 1090.030 1090.062 1090.001 1090.035 1080.004 1090.051 1090.143 1090.019 1090.003 1080.041 1070.050 1080.003 1090.054 1090.018 1090.005 1090.264 1090.082 109