3D Semantic Instance Benchmark
The 3D semantic instance prediction task involves detecting and segmenting the object in an 3D scan mesh.
Evaluation and metricsOur evaluation ranks all methods according to the average precision for each class. We report the mean average precision AP at overlap 0.25 (AP 25%), overlap 0.5 (AP 50%), and over overlaps in the range [0.5:0.95:0.05] (AP). Note that multiple predictions of the same ground truth instance are penalized as false positives.
This table lists the benchmark results for the 3D semantic instance scenario.
Method | Info | avg ap 25% | bathtub | bed | bookshelf | cabinet | chair | counter | curtain | desk | door | otherfurniture | picture | refrigerator | shower curtain | sink | sofa | table | toilet | window |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OneFormer3D | 0.896 1 | 1.000 1 | 1.000 1 | 0.913 4 | 0.858 4 | 0.951 3 | 0.786 9 | 0.837 13 | 0.916 7 | 0.908 2 | 0.778 4 | 0.803 2 | 0.750 10 | 1.000 1 | 0.976 2 | 0.926 4 | 0.882 5 | 0.995 39 | 0.849 1 | |
Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: OneFormer3D: One Transformer for Unified Point Cloud Segmentation. | ||||||||||||||||||||
UniPerception | 0.884 2 | 1.000 1 | 0.979 14 | 0.872 13 | 0.869 2 | 0.892 19 | 0.806 6 | 0.890 5 | 0.835 21 | 0.892 4 | 0.755 10 | 0.811 1 | 0.779 8 | 0.955 39 | 0.951 3 | 0.876 18 | 0.914 1 | 0.997 33 | 0.840 2 | |
Spherical Mask(CtoF) | 0.875 3 | 1.000 1 | 0.991 9 | 0.873 12 | 0.850 5 | 0.946 5 | 0.691 18 | 0.752 27 | 0.926 4 | 0.889 6 | 0.759 8 | 0.794 4 | 0.820 2 | 1.000 1 | 0.912 13 | 0.900 7 | 0.878 9 | 1.000 1 | 0.769 14 | |
TD3D | 0.875 3 | 1.000 1 | 0.976 17 | 0.877 10 | 0.783 20 | 0.970 1 | 0.889 1 | 0.828 14 | 0.945 3 | 0.803 14 | 0.713 16 | 0.720 16 | 0.709 13 | 1.000 1 | 0.936 9 | 0.934 3 | 0.873 12 | 1.000 1 | 0.791 11 | |
Maksim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich: Top-Down Beats Bottom-Up in 3D Instance Segmentation. WACV 2024 | ||||||||||||||||||||
Queryformer | 0.874 5 | 1.000 1 | 0.978 16 | 0.809 29 | 0.876 1 | 0.936 9 | 0.702 15 | 0.716 32 | 0.920 6 | 0.875 9 | 0.766 5 | 0.772 6 | 0.818 4 | 1.000 1 | 0.995 1 | 0.916 5 | 0.892 2 | 1.000 1 | 0.767 15 | |
SoftGroup++ | 0.874 5 | 1.000 1 | 0.972 18 | 0.947 1 | 0.839 8 | 0.898 18 | 0.556 32 | 0.913 2 | 0.881 13 | 0.756 16 | 0.828 2 | 0.748 10 | 0.821 1 | 1.000 1 | 0.937 8 | 0.937 1 | 0.887 3 | 1.000 1 | 0.821 5 | |
Mask3D | 0.870 7 | 1.000 1 | 0.985 11 | 0.782 37 | 0.818 13 | 0.938 8 | 0.760 10 | 0.749 28 | 0.923 5 | 0.877 8 | 0.760 7 | 0.785 5 | 0.820 2 | 1.000 1 | 0.912 13 | 0.864 29 | 0.878 9 | 0.983 45 | 0.825 4 | |
Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, Bastian Leibe: Mask3D for 3D Semantic Instance Segmentation. ICRA 2023 | ||||||||||||||||||||
ExtMask3D | 0.867 8 | 1.000 1 | 1.000 1 | 0.756 44 | 0.816 14 | 0.940 7 | 0.795 7 | 0.760 26 | 0.862 15 | 0.888 7 | 0.739 12 | 0.763 7 | 0.774 9 | 1.000 1 | 0.929 11 | 0.878 17 | 0.879 7 | 1.000 1 | 0.819 7 | |
SoftGroup | 0.865 9 | 1.000 1 | 0.969 19 | 0.860 15 | 0.860 3 | 0.913 13 | 0.558 29 | 0.899 3 | 0.911 8 | 0.760 15 | 0.828 1 | 0.736 12 | 0.802 6 | 0.981 36 | 0.919 12 | 0.875 19 | 0.877 11 | 1.000 1 | 0.820 6 | |
Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, Chang D. Yoo: SoftGroup for 3D Instance Segmentaiton on Point Clouds. CVPR 2022 [Oral] | ||||||||||||||||||||
MAFT | 0.860 10 | 1.000 1 | 0.990 10 | 0.810 28 | 0.829 9 | 0.949 4 | 0.809 5 | 0.688 39 | 0.836 20 | 0.904 3 | 0.751 11 | 0.796 3 | 0.741 11 | 1.000 1 | 0.864 31 | 0.848 36 | 0.837 17 | 1.000 1 | 0.828 3 | |
IPCA-Inst | 0.851 11 | 1.000 1 | 0.968 20 | 0.884 9 | 0.842 7 | 0.862 31 | 0.693 17 | 0.812 19 | 0.888 12 | 0.677 28 | 0.783 3 | 0.698 17 | 0.807 5 | 1.000 1 | 0.911 19 | 0.865 28 | 0.865 14 | 1.000 1 | 0.757 18 | |
SPFormer | 0.851 11 | 1.000 1 | 0.994 5 | 0.806 30 | 0.774 22 | 0.942 6 | 0.637 21 | 0.849 11 | 0.859 17 | 0.889 5 | 0.720 15 | 0.730 14 | 0.665 19 | 1.000 1 | 0.911 19 | 0.868 27 | 0.873 13 | 1.000 1 | 0.796 9 | |
Sun Jiahao, Qing Chunmei, Tan Junpeng, Xu Xiangmin: Superpoint Transformer for 3D Scene Instance Segmentation. AAAI 2023 [Oral] | ||||||||||||||||||||
Mask3D_evaluation | 0.843 13 | 1.000 1 | 0.955 25 | 0.847 17 | 0.795 16 | 0.932 10 | 0.750 12 | 0.780 24 | 0.891 10 | 0.818 11 | 0.737 13 | 0.633 26 | 0.703 14 | 1.000 1 | 0.902 23 | 0.870 23 | 0.820 19 | 0.941 53 | 0.805 8 | |
SIM3D | 0.842 14 | 1.000 1 | 0.998 3 | 0.608 57 | 0.717 41 | 0.908 14 | 0.818 4 | 0.699 36 | 0.798 28 | 0.908 1 | 0.760 6 | 0.733 13 | 0.793 7 | 1.000 1 | 0.912 13 | 0.831 41 | 0.883 4 | 1.000 1 | 0.792 10 | |
ISBNet | 0.835 15 | 1.000 1 | 0.950 26 | 0.731 46 | 0.819 11 | 0.918 11 | 0.790 8 | 0.740 29 | 0.851 19 | 0.831 10 | 0.661 24 | 0.742 11 | 0.650 22 | 1.000 1 | 0.937 7 | 0.814 49 | 0.836 18 | 1.000 1 | 0.765 16 | |
Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen: ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution. CVPR 2023 | ||||||||||||||||||||
SphereSeg | 0.835 15 | 1.000 1 | 0.963 23 | 0.891 7 | 0.794 17 | 0.954 2 | 0.822 3 | 0.710 33 | 0.961 2 | 0.721 20 | 0.693 22 | 0.530 39 | 0.653 21 | 1.000 1 | 0.867 30 | 0.857 32 | 0.859 15 | 0.991 42 | 0.771 13 | |
GraphCut | 0.832 17 | 1.000 1 | 0.922 40 | 0.724 48 | 0.798 15 | 0.902 17 | 0.701 16 | 0.856 9 | 0.859 16 | 0.715 21 | 0.706 17 | 0.748 9 | 0.640 33 | 1.000 1 | 0.934 10 | 0.862 30 | 0.880 6 | 1.000 1 | 0.729 21 | |
TopoSeg | 0.832 17 | 1.000 1 | 0.981 13 | 0.933 2 | 0.819 12 | 0.826 40 | 0.524 38 | 0.841 12 | 0.811 25 | 0.681 27 | 0.759 9 | 0.687 18 | 0.727 12 | 0.981 36 | 0.911 19 | 0.883 13 | 0.853 16 | 1.000 1 | 0.756 19 | |
PBNet | 0.825 19 | 1.000 1 | 0.963 22 | 0.837 20 | 0.843 6 | 0.865 26 | 0.822 2 | 0.647 42 | 0.878 14 | 0.733 18 | 0.639 31 | 0.683 19 | 0.650 22 | 1.000 1 | 0.853 32 | 0.870 24 | 0.820 20 | 1.000 1 | 0.744 20 | |
W.Zhao, Y.Yan, C.Yang, J.Ye,X.Yang,K.Huang: Divide and Conquer: 3D Instance Segmentation With Point-Wise Binarization. ICCV 2023 | ||||||||||||||||||||
SSEC | 0.820 20 | 1.000 1 | 0.983 12 | 0.924 3 | 0.826 10 | 0.817 43 | 0.415 47 | 0.899 4 | 0.793 30 | 0.673 29 | 0.731 14 | 0.636 24 | 0.653 20 | 1.000 1 | 0.939 6 | 0.804 51 | 0.878 8 | 1.000 1 | 0.780 12 | |
DKNet | 0.815 21 | 1.000 1 | 0.930 32 | 0.844 18 | 0.765 26 | 0.915 12 | 0.534 36 | 0.805 21 | 0.805 27 | 0.807 13 | 0.654 25 | 0.763 8 | 0.650 22 | 1.000 1 | 0.794 44 | 0.881 14 | 0.766 24 | 1.000 1 | 0.758 17 | |
Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong: 3D Instances as 1D Kernels. ECCV 2022 | ||||||||||||||||||||
RPGN | 0.806 22 | 1.000 1 | 0.992 7 | 0.789 32 | 0.723 39 | 0.891 20 | 0.650 20 | 0.810 20 | 0.832 22 | 0.665 31 | 0.699 20 | 0.658 20 | 0.700 15 | 1.000 1 | 0.881 25 | 0.832 40 | 0.774 22 | 0.997 33 | 0.613 41 | |
Shichao Dong, Guosheng Lin, Tzu-Yi Hung: Learning Regional Purity for Instance Segmentation on 3D Point Clouds. ECCV 2022 | ||||||||||||||||||||
Box2Mask | 0.803 23 | 1.000 1 | 0.962 24 | 0.874 11 | 0.707 44 | 0.887 23 | 0.686 19 | 0.598 47 | 0.961 1 | 0.715 22 | 0.694 21 | 0.469 44 | 0.700 15 | 1.000 1 | 0.912 13 | 0.902 6 | 0.753 29 | 0.997 33 | 0.637 35 | |
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022 | ||||||||||||||||||||
HAIS | 0.803 23 | 1.000 1 | 0.994 5 | 0.820 24 | 0.759 27 | 0.855 32 | 0.554 33 | 0.882 6 | 0.827 24 | 0.615 37 | 0.676 23 | 0.638 23 | 0.646 31 | 1.000 1 | 0.912 13 | 0.797 54 | 0.767 23 | 0.994 40 | 0.726 22 | |
Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang: Hierarchical Aggregation for 3D Instance Segmentation. ICCV 2021 | ||||||||||||||||||||
Mask-Group | 0.792 25 | 1.000 1 | 0.968 21 | 0.812 25 | 0.766 25 | 0.864 27 | 0.460 41 | 0.815 18 | 0.888 11 | 0.598 41 | 0.651 28 | 0.639 22 | 0.600 39 | 0.918 42 | 0.941 4 | 0.896 9 | 0.721 36 | 1.000 1 | 0.723 23 | |
Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang: MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation. ICME 2022 | ||||||||||||||||||||
CSC-Pretrained | 0.791 26 | 1.000 1 | 0.996 4 | 0.829 23 | 0.767 24 | 0.889 22 | 0.600 24 | 0.819 17 | 0.770 35 | 0.594 42 | 0.620 35 | 0.541 36 | 0.700 15 | 1.000 1 | 0.941 4 | 0.889 11 | 0.763 25 | 1.000 1 | 0.526 51 | |
SSTNet | 0.789 27 | 1.000 1 | 0.840 54 | 0.888 8 | 0.717 40 | 0.835 36 | 0.717 14 | 0.684 40 | 0.627 50 | 0.724 19 | 0.652 27 | 0.727 15 | 0.600 39 | 1.000 1 | 0.912 13 | 0.822 44 | 0.757 28 | 1.000 1 | 0.691 29 | |
Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia: Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks. ICCV2021 | ||||||||||||||||||||
GICN | 0.788 28 | 1.000 1 | 0.978 15 | 0.867 14 | 0.781 21 | 0.833 37 | 0.527 37 | 0.824 15 | 0.806 26 | 0.549 50 | 0.596 38 | 0.551 32 | 0.700 15 | 1.000 1 | 0.853 32 | 0.935 2 | 0.733 33 | 1.000 1 | 0.651 32 | |
DANCENET | 0.786 29 | 1.000 1 | 0.936 29 | 0.783 35 | 0.737 36 | 0.852 34 | 0.742 13 | 0.647 42 | 0.765 37 | 0.811 12 | 0.624 34 | 0.579 29 | 0.632 36 | 1.000 1 | 0.909 22 | 0.898 8 | 0.696 41 | 0.944 49 | 0.601 44 | |
DENet | 0.786 29 | 1.000 1 | 0.929 33 | 0.736 45 | 0.750 33 | 0.720 56 | 0.755 11 | 0.934 1 | 0.794 29 | 0.590 43 | 0.561 44 | 0.537 37 | 0.650 22 | 1.000 1 | 0.882 24 | 0.804 52 | 0.789 21 | 1.000 1 | 0.719 24 | |
DualGroup | 0.782 31 | 1.000 1 | 0.927 34 | 0.811 26 | 0.772 23 | 0.853 33 | 0.631 23 | 0.805 21 | 0.773 32 | 0.613 38 | 0.611 36 | 0.610 27 | 0.650 22 | 0.835 53 | 0.881 25 | 0.879 16 | 0.750 31 | 1.000 1 | 0.675 30 | |
PointGroup | 0.778 32 | 1.000 1 | 0.900 44 | 0.798 31 | 0.715 42 | 0.863 28 | 0.493 39 | 0.706 34 | 0.895 9 | 0.569 48 | 0.701 18 | 0.576 30 | 0.639 34 | 1.000 1 | 0.880 27 | 0.851 34 | 0.719 37 | 0.997 33 | 0.709 26 | |
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020 [oral] | ||||||||||||||||||||
PE | 0.776 33 | 1.000 1 | 0.900 45 | 0.860 15 | 0.728 38 | 0.869 24 | 0.400 48 | 0.857 8 | 0.774 31 | 0.568 49 | 0.701 19 | 0.602 28 | 0.646 31 | 0.933 41 | 0.843 35 | 0.890 10 | 0.691 45 | 0.997 33 | 0.709 25 | |
Biao Zhang, Peter Wonka: Point Cloud Instance Segmentation using Probabilistic Embeddings. CVPR 2021 | ||||||||||||||||||||
AOIA | 0.767 34 | 1.000 1 | 0.937 28 | 0.810 27 | 0.740 35 | 0.906 15 | 0.550 34 | 0.800 23 | 0.706 42 | 0.577 47 | 0.624 33 | 0.544 35 | 0.596 44 | 0.857 45 | 0.879 29 | 0.880 15 | 0.750 30 | 0.992 41 | 0.658 31 | |
DD-UNet+Group | 0.764 35 | 1.000 1 | 0.897 47 | 0.837 19 | 0.753 30 | 0.830 39 | 0.459 43 | 0.824 15 | 0.699 44 | 0.629 35 | 0.653 26 | 0.438 47 | 0.650 22 | 1.000 1 | 0.880 27 | 0.858 31 | 0.690 46 | 1.000 1 | 0.650 33 | |
H. Liu, R. Liu, K. Yang, J. Zhang, K. Peng, R. Stiefelhagen: HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor. ICCVW 2021 | ||||||||||||||||||||
INS-Conv-instance | 0.762 36 | 1.000 1 | 0.923 37 | 0.765 40 | 0.785 19 | 0.905 16 | 0.600 24 | 0.655 41 | 0.646 49 | 0.683 26 | 0.647 29 | 0.530 38 | 0.650 22 | 1.000 1 | 0.824 37 | 0.830 42 | 0.693 44 | 0.944 49 | 0.644 34 | |
Dyco3D | 0.761 37 | 1.000 1 | 0.935 30 | 0.893 6 | 0.752 32 | 0.863 29 | 0.600 24 | 0.588 48 | 0.742 39 | 0.641 33 | 0.633 32 | 0.546 34 | 0.550 46 | 0.857 45 | 0.789 46 | 0.853 33 | 0.762 26 | 0.987 43 | 0.699 27 | |
Tong He; Chunhua Shen; Anton van den Hengel: DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. CVPR2021 | ||||||||||||||||||||
OccuSeg+instance | 0.742 38 | 1.000 1 | 0.923 37 | 0.785 33 | 0.745 34 | 0.867 25 | 0.557 30 | 0.578 51 | 0.729 40 | 0.670 30 | 0.644 30 | 0.488 42 | 0.577 45 | 1.000 1 | 0.794 44 | 0.830 42 | 0.620 54 | 1.000 1 | 0.550 47 | |
Lei Han, Tian Zheng, Lan Xu, Lu Fang: OccuSeg: Occupancy-aware 3D Instance Segmentation. CVPR2020 | ||||||||||||||||||||
RWSeg | 0.739 39 | 1.000 1 | 0.899 46 | 0.759 42 | 0.753 31 | 0.823 41 | 0.282 53 | 0.691 38 | 0.658 47 | 0.582 46 | 0.594 39 | 0.547 33 | 0.628 37 | 1.000 1 | 0.795 43 | 0.868 26 | 0.728 35 | 1.000 1 | 0.692 28 | |
3D-MPA | 0.737 40 | 1.000 1 | 0.933 31 | 0.785 33 | 0.794 18 | 0.831 38 | 0.279 55 | 0.588 48 | 0.695 45 | 0.616 36 | 0.559 45 | 0.556 31 | 0.650 22 | 1.000 1 | 0.809 41 | 0.875 20 | 0.696 42 | 1.000 1 | 0.608 43 | |
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner: 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation. CVPR 2020 | ||||||||||||||||||||
MTML | 0.731 41 | 1.000 1 | 0.992 7 | 0.779 39 | 0.609 53 | 0.746 51 | 0.308 52 | 0.867 7 | 0.601 53 | 0.607 39 | 0.539 48 | 0.519 40 | 0.550 46 | 1.000 1 | 0.824 37 | 0.869 25 | 0.729 34 | 1.000 1 | 0.616 39 | |
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral] | ||||||||||||||||||||
OSIS | 0.725 42 | 1.000 1 | 0.885 50 | 0.653 54 | 0.657 50 | 0.801 44 | 0.576 28 | 0.695 37 | 0.828 23 | 0.698 24 | 0.534 49 | 0.457 46 | 0.500 53 | 0.857 45 | 0.831 36 | 0.841 38 | 0.627 52 | 1.000 1 | 0.619 38 | |
SSEN | 0.724 43 | 1.000 1 | 0.926 35 | 0.781 38 | 0.661 48 | 0.845 35 | 0.596 27 | 0.529 54 | 0.764 38 | 0.653 32 | 0.489 55 | 0.461 45 | 0.500 53 | 0.859 44 | 0.765 47 | 0.872 22 | 0.761 27 | 1.000 1 | 0.577 45 | |
Dongsu Zhang, Junha Chun, Sang Kyun Cha, Young Min Kim: Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning. Arxiv | ||||||||||||||||||||
NeuralBF | 0.718 44 | 1.000 1 | 0.945 27 | 0.901 5 | 0.754 29 | 0.817 42 | 0.460 41 | 0.700 35 | 0.772 33 | 0.688 25 | 0.568 43 | 0.000 66 | 0.500 53 | 0.981 36 | 0.606 57 | 0.872 21 | 0.740 32 | 1.000 1 | 0.614 40 | |
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV 2023 | ||||||||||||||||||||
Sparse R-CNN | 0.714 45 | 1.000 1 | 0.926 36 | 0.694 49 | 0.699 46 | 0.890 21 | 0.636 22 | 0.516 55 | 0.693 46 | 0.743 17 | 0.588 40 | 0.369 51 | 0.601 38 | 0.594 59 | 0.800 42 | 0.886 12 | 0.676 47 | 0.986 44 | 0.546 48 | |
SALoss-ResNet | 0.695 46 | 1.000 1 | 0.855 52 | 0.579 60 | 0.589 55 | 0.735 54 | 0.484 40 | 0.588 48 | 0.856 18 | 0.634 34 | 0.571 42 | 0.298 52 | 0.500 53 | 1.000 1 | 0.824 37 | 0.818 45 | 0.702 40 | 0.935 56 | 0.545 49 | |
Zhidong Liang, Ming Yang, Hao Li, Chunxiang Wang: 3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation. IEEE Robotics and Automation Letters (IROS2020) | ||||||||||||||||||||
PanopticFusion-inst | 0.693 47 | 1.000 1 | 0.852 53 | 0.655 53 | 0.616 52 | 0.788 46 | 0.334 50 | 0.763 25 | 0.771 34 | 0.457 60 | 0.555 46 | 0.652 21 | 0.518 50 | 0.857 45 | 0.765 47 | 0.732 60 | 0.631 50 | 0.944 49 | 0.577 46 | |
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear) | ||||||||||||||||||||
Occipital-SCS | 0.688 48 | 1.000 1 | 0.913 41 | 0.730 47 | 0.737 37 | 0.743 53 | 0.442 44 | 0.855 10 | 0.655 48 | 0.546 51 | 0.546 47 | 0.263 54 | 0.508 52 | 0.889 43 | 0.568 58 | 0.771 57 | 0.705 39 | 0.889 59 | 0.625 37 | |
3D-BoNet | 0.687 49 | 1.000 1 | 0.887 49 | 0.836 21 | 0.587 56 | 0.643 63 | 0.550 34 | 0.620 44 | 0.724 41 | 0.522 55 | 0.501 53 | 0.243 55 | 0.512 51 | 1.000 1 | 0.751 49 | 0.807 50 | 0.661 49 | 0.909 58 | 0.612 42 | |
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight | ||||||||||||||||||||
ClickSeg_Instance | 0.685 50 | 1.000 1 | 0.818 56 | 0.600 58 | 0.715 43 | 0.795 45 | 0.557 30 | 0.533 53 | 0.591 55 | 0.601 40 | 0.519 51 | 0.429 49 | 0.638 35 | 0.938 40 | 0.706 52 | 0.817 47 | 0.624 53 | 0.944 49 | 0.502 53 | |
PCJC | 0.684 51 | 1.000 1 | 0.895 48 | 0.757 43 | 0.659 49 | 0.862 30 | 0.189 62 | 0.739 30 | 0.606 52 | 0.712 23 | 0.581 41 | 0.515 41 | 0.650 22 | 0.857 45 | 0.357 63 | 0.785 55 | 0.631 51 | 0.889 59 | 0.635 36 | |
SPG_WSIS | 0.678 52 | 1.000 1 | 0.880 51 | 0.836 21 | 0.701 45 | 0.727 55 | 0.273 57 | 0.607 46 | 0.706 43 | 0.541 53 | 0.515 52 | 0.174 58 | 0.600 39 | 0.857 45 | 0.716 51 | 0.846 37 | 0.711 38 | 1.000 1 | 0.506 52 | |
One_Thing_One_Click | 0.675 53 | 1.000 1 | 0.823 55 | 0.782 36 | 0.621 51 | 0.766 48 | 0.211 59 | 0.736 31 | 0.560 57 | 0.586 44 | 0.522 50 | 0.636 25 | 0.453 57 | 0.641 57 | 0.853 32 | 0.850 35 | 0.694 43 | 0.997 33 | 0.411 58 | |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 | ||||||||||||||||||||
SegGroup_ins | 0.637 54 | 1.000 1 | 0.923 39 | 0.593 59 | 0.561 57 | 0.746 52 | 0.143 64 | 0.504 56 | 0.766 36 | 0.485 58 | 0.442 56 | 0.372 50 | 0.530 49 | 0.714 54 | 0.815 40 | 0.775 56 | 0.673 48 | 1.000 1 | 0.431 57 | |
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022 | ||||||||||||||||||||
MASC | 0.615 55 | 0.711 62 | 0.802 57 | 0.540 61 | 0.757 28 | 0.777 47 | 0.029 65 | 0.577 52 | 0.588 56 | 0.521 56 | 0.600 37 | 0.436 48 | 0.534 48 | 0.697 55 | 0.616 56 | 0.838 39 | 0.526 56 | 0.980 46 | 0.534 50 | |
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation. | ||||||||||||||||||||
UNet-backbone | 0.605 56 | 1.000 1 | 0.909 42 | 0.764 41 | 0.603 54 | 0.704 57 | 0.415 46 | 0.301 61 | 0.548 58 | 0.461 59 | 0.394 57 | 0.267 53 | 0.386 59 | 0.857 45 | 0.649 55 | 0.817 46 | 0.504 58 | 0.959 47 | 0.356 61 | |
3D-SIS | 0.558 57 | 1.000 1 | 0.773 58 | 0.614 56 | 0.503 60 | 0.691 59 | 0.200 60 | 0.412 57 | 0.498 61 | 0.546 52 | 0.311 62 | 0.103 62 | 0.600 39 | 0.857 45 | 0.382 60 | 0.799 53 | 0.445 64 | 0.938 55 | 0.371 59 | |
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019 | ||||||||||||||||||||
R-PointNet | 0.544 58 | 0.500 65 | 0.655 64 | 0.661 52 | 0.663 47 | 0.765 49 | 0.432 45 | 0.214 64 | 0.612 51 | 0.584 45 | 0.499 54 | 0.204 57 | 0.286 63 | 0.429 62 | 0.655 54 | 0.650 65 | 0.539 55 | 0.950 48 | 0.499 54 | |
Hier3D | 0.540 59 | 1.000 1 | 0.727 59 | 0.626 55 | 0.467 63 | 0.693 58 | 0.200 60 | 0.412 57 | 0.480 62 | 0.528 54 | 0.318 61 | 0.077 65 | 0.600 39 | 0.688 56 | 0.382 60 | 0.768 58 | 0.472 60 | 0.941 53 | 0.350 62 | |
Tan: HCFS3D: Hierarchical Coupled Feature Selection Network for 3D Semantic and Instance Segmentation. | ||||||||||||||||||||
Region-18class | 0.497 60 | 0.250 67 | 0.902 43 | 0.689 50 | 0.540 58 | 0.747 50 | 0.276 56 | 0.610 45 | 0.268 66 | 0.489 57 | 0.348 58 | 0.000 66 | 0.243 66 | 0.220 65 | 0.663 53 | 0.814 48 | 0.459 62 | 0.928 57 | 0.496 55 | |
Sem_Recon_ins | 0.484 61 | 0.764 61 | 0.608 66 | 0.470 63 | 0.521 59 | 0.637 64 | 0.311 51 | 0.218 63 | 0.348 65 | 0.365 64 | 0.223 63 | 0.222 56 | 0.258 64 | 0.629 58 | 0.734 50 | 0.596 66 | 0.509 57 | 0.858 62 | 0.444 56 | |
tmp | 0.474 62 | 1.000 1 | 0.727 59 | 0.433 65 | 0.481 62 | 0.673 61 | 0.022 67 | 0.380 59 | 0.517 60 | 0.436 62 | 0.338 60 | 0.128 60 | 0.343 61 | 0.429 62 | 0.291 65 | 0.728 61 | 0.473 59 | 0.833 63 | 0.300 64 | |
SemRegionNet-20cls | 0.470 63 | 1.000 1 | 0.727 59 | 0.447 64 | 0.481 61 | 0.678 60 | 0.024 66 | 0.380 59 | 0.518 59 | 0.440 61 | 0.339 59 | 0.128 60 | 0.350 60 | 0.429 62 | 0.212 66 | 0.711 62 | 0.465 61 | 0.833 63 | 0.290 65 | |
ASIS | 0.422 64 | 0.333 66 | 0.707 62 | 0.676 51 | 0.401 64 | 0.650 62 | 0.350 49 | 0.177 65 | 0.594 54 | 0.376 63 | 0.202 64 | 0.077 64 | 0.404 58 | 0.571 60 | 0.197 67 | 0.674 64 | 0.447 63 | 0.500 66 | 0.260 66 | |
3D-BEVIS | 0.401 65 | 0.667 63 | 0.687 63 | 0.419 66 | 0.137 67 | 0.587 65 | 0.188 63 | 0.235 62 | 0.359 64 | 0.211 66 | 0.093 67 | 0.080 63 | 0.311 62 | 0.571 60 | 0.382 60 | 0.754 59 | 0.300 66 | 0.874 61 | 0.357 60 | |
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation. | ||||||||||||||||||||
Sgpn_scannet | 0.390 66 | 0.556 64 | 0.636 65 | 0.493 62 | 0.353 65 | 0.539 66 | 0.271 58 | 0.160 66 | 0.450 63 | 0.359 65 | 0.178 65 | 0.146 59 | 0.250 65 | 0.143 66 | 0.347 64 | 0.698 63 | 0.436 65 | 0.667 65 | 0.331 63 | |
MaskRCNN 2d->3d Proj | 0.261 67 | 0.903 60 | 0.081 67 | 0.008 67 | 0.233 66 | 0.175 67 | 0.280 54 | 0.106 67 | 0.150 67 | 0.203 67 | 0.175 66 | 0.480 43 | 0.218 67 | 0.143 66 | 0.542 59 | 0.404 67 | 0.153 67 | 0.393 67 | 0.049 67 | |