3D Semantic Label Results - ScanNet Benchmark

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	avg iou	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	floor	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	wall	window

PTv3 ScanNet	0.794 2	0.941 3	0.813 20	0.851 9	0.782 6	0.890 3	0.597 1	0.916 5	0.696 9	0.713 5	0.979 1	0.635 2	0.384 3	0.793 3	0.907 10	0.821 5	0.790 33	0.696 14	0.967 3	0.903 2	0.805 2
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral)
DITR ScanNet	0.793 3	0.811 40	0.852 2	0.889 1	0.774 10	0.907 1	0.592 2	0.927 3	0.719 1	0.718 3	0.961 17	0.652 1	0.348 12	0.817 1	0.927 5	0.795 10	0.824 2	0.749 1	0.948 9	0.887 7	0.771 11

TTT-KD	0.773 7	0.646 95	0.818 15	0.809 38	0.774 10	0.878 4	0.581 3	0.943 1	0.687 13	0.704 7	0.978 5	0.607 6	0.336 18	0.775 12	0.912 8	0.838 4	0.823 3	0.694 15	0.967 3	0.899 3	0.794 5
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick, Can Ekkazan, Pedro Hermosilla: TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models.
CU-Hybrid Net	0.764 11	0.924 8	0.819 13	0.840 20	0.757 21	0.853 19	0.580 4	0.848 29	0.709 4	0.643 28	0.958 23	0.587 15	0.295 37	0.753 28	0.884 22	0.758 23	0.815 8	0.725 4	0.927 27	0.867 25	0.743 19

O-CNN	0.762 13	0.924 8	0.823 8	0.844 17	0.770 13	0.852 21	0.577 5	0.847 31	0.711 3	0.640 32	0.958 23	0.592 11	0.217 76	0.762 21	0.888 19	0.758 23	0.813 12	0.726 3	0.932 25	0.868 24	0.744 18
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
Swin3D	0.779 6	0.861 22	0.818 15	0.836 23	0.790 3	0.875 5	0.576 6	0.905 9	0.704 6	0.739 1	0.969 11	0.611 3	0.349 11	0.756 26	0.958 1	0.702 49	0.805 16	0.708 9	0.916 36	0.898 4	0.801 3

Mix3D	0.781 5	0.964 2	0.855 1	0.843 18	0.781 7	0.858 13	0.575 7	0.831 36	0.685 15	0.714 4	0.979 1	0.594 10	0.310 30	0.801 2	0.892 18	0.841 2	0.819 5	0.723 5	0.940 15	0.887 7	0.725 28
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
LRPNet	0.742 31	0.816 37	0.806 27	0.807 40	0.752 24	0.828 48	0.575 7	0.839 34	0.699 7	0.637 33	0.954 38	0.520 43	0.320 27	0.755 27	0.834 42	0.760 22	0.772 43	0.676 26	0.915 38	0.862 29	0.717 31

PNE	0.755 17	0.786 45	0.835 5	0.834 25	0.758 19	0.849 24	0.570 9	0.836 35	0.648 31	0.668 20	0.978 5	0.581 20	0.367 7	0.683 38	0.856 32	0.804 7	0.801 21	0.678 22	0.961 5	0.889 6	0.716 33
P. Hermosilla: Point Neighborhood Embeddings.
OctFormer	0.766 9	0.925 7	0.808 26	0.849 11	0.786 5	0.846 29	0.566 10	0.876 19	0.690 11	0.674 16	0.960 19	0.576 21	0.226 70	0.753 28	0.904 11	0.777 16	0.815 8	0.722 6	0.923 31	0.877 16	0.776 9
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023
ResLFE_HDS	0.772 8	0.939 4	0.824 7	0.854 7	0.771 12	0.840 34	0.564 11	0.900 11	0.686 14	0.677 14	0.961 17	0.537 35	0.348 12	0.769 16	0.903 12	0.785 14	0.815 8	0.676 26	0.939 16	0.880 13	0.772 10

OccuSeg+Semantic	0.764 11	0.758 61	0.796 34	0.839 21	0.746 29	0.907 1	0.562 12	0.850 28	0.680 17	0.672 18	0.978 5	0.610 4	0.335 20	0.777 10	0.819 48	0.847 1	0.830 1	0.691 17	0.972 2	0.885 10	0.727 26

DiffSegNet	0.758 14	0.725 78	0.789 41	0.843 18	0.762 17	0.856 14	0.562 12	0.920 4	0.657 28	0.658 22	0.958 23	0.589 13	0.337 17	0.782 6	0.879 23	0.787 12	0.779 39	0.678 22	0.926 29	0.880 13	0.799 4

DMF-Net	0.752 19	0.906 13	0.793 38	0.802 44	0.689 43	0.825 50	0.556 14	0.867 22	0.681 16	0.602 48	0.960 19	0.555 31	0.365 8	0.779 9	0.859 29	0.747 26	0.795 29	0.717 7	0.917 35	0.856 34	0.764 12
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
PTv3-PPT-ALC	0.798 1	0.911 10	0.812 21	0.854 7	0.770 13	0.856 14	0.555 15	0.943 1	0.660 25	0.735 2	0.979 1	0.606 7	0.492 1	0.792 4	0.934 3	0.841 2	0.819 5	0.716 8	0.947 10	0.906 1	0.822 1

PointTransformerV2	0.752 19	0.742 68	0.809 25	0.872 2	0.758 19	0.860 12	0.552 16	0.891 17	0.610 45	0.687 8	0.960 19	0.559 29	0.304 33	0.766 19	0.926 6	0.767 20	0.797 25	0.644 37	0.942 13	0.876 19	0.722 30
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling. NeurIPS 2022
VI-PointConv	0.676 57	0.770 57	0.754 61	0.783 53	0.621 64	0.814 65	0.552 16	0.758 59	0.571 66	0.557 59	0.954 38	0.529 40	0.268 58	0.530 80	0.682 74	0.675 58	0.719 72	0.603 55	0.888 56	0.833 47	0.665 47
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
OA-CNN-L_ScanNet20	0.756 16	0.783 47	0.826 6	0.858 5	0.776 8	0.837 37	0.548 18	0.896 15	0.649 30	0.675 15	0.962 16	0.586 16	0.335 20	0.771 15	0.802 52	0.770 19	0.787 35	0.691 17	0.936 19	0.880 13	0.761 13

PonderV2	0.785 4	0.978 1	0.800 30	0.833 26	0.788 4	0.853 19	0.545 19	0.910 8	0.713 2	0.705 6	0.979 1	0.596 9	0.390 2	0.769 16	0.832 44	0.821 5	0.792 32	0.730 2	0.975 1	0.897 5	0.785 6
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.
LargeKernel3D	0.739 33	0.909 11	0.820 11	0.806 42	0.740 31	0.852 21	0.545 19	0.826 38	0.594 56	0.643 28	0.955 32	0.541 34	0.263 60	0.723 36	0.858 31	0.775 18	0.767 47	0.678 22	0.933 23	0.848 41	0.694 40
Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia: LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs. CVPR 2023
O3DSeg	0.668 60	0.822 35	0.771 50	0.496 109	0.651 55	0.833 42	0.541 21	0.761 58	0.555 72	0.611 41	0.966 14	0.489 52	0.370 6	0.388 102	0.580 86	0.776 17	0.751 60	0.570 68	0.956 6	0.817 58	0.646 54

Retro-FPN	0.744 28	0.842 29	0.800 30	0.767 58	0.740 31	0.836 39	0.541 21	0.914 6	0.672 21	0.626 36	0.958 23	0.552 32	0.272 52	0.777 10	0.886 21	0.696 50	0.801 21	0.674 29	0.941 14	0.858 32	0.717 31
Peng Xiang, Xin Wen, Yu-Shen Liu, Hui Zhang, Yi Fang, Zhizhong Han: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation. ICCV 2023
PPT-SpUNet-Joint	0.766 9	0.932 5	0.794 36	0.829 28	0.751 26	0.854 17	0.540 23	0.903 10	0.630 38	0.672 18	0.963 15	0.565 25	0.357 9	0.788 5	0.900 14	0.737 29	0.802 17	0.685 20	0.950 7	0.887 7	0.780 7
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. CVPR 2024
INS-Conv-semantic	0.717 40	0.751 64	0.759 57	0.812 35	0.704 40	0.868 8	0.537 24	0.842 32	0.609 47	0.608 44	0.953 43	0.534 38	0.293 38	0.616 56	0.864 27	0.719 39	0.793 30	0.640 39	0.933 23	0.845 45	0.663 48

SparseConvNet	0.725 37	0.647 94	0.821 10	0.846 15	0.721 37	0.869 7	0.533 25	0.754 61	0.603 51	0.614 40	0.955 32	0.572 23	0.325 24	0.710 37	0.870 24	0.724 35	0.823 3	0.628 44	0.934 22	0.865 27	0.683 43

SAT	0.742 31	0.860 23	0.765 53	0.819 31	0.769 15	0.848 26	0.533 25	0.829 37	0.663 23	0.631 35	0.955 32	0.586 16	0.274 50	0.753 28	0.896 16	0.729 31	0.760 54	0.666 32	0.921 33	0.855 36	0.733 22

PointMetaBase	0.714 41	0.835 30	0.785 42	0.821 29	0.684 45	0.846 29	0.531 27	0.865 24	0.614 42	0.596 52	0.953 43	0.500 48	0.246 66	0.674 39	0.888 19	0.692 51	0.764 50	0.624 46	0.849 85	0.844 46	0.675 45

IPCA	0.731 36	0.890 16	0.837 4	0.864 3	0.726 36	0.873 6	0.530 28	0.824 40	0.489 90	0.647 25	0.978 5	0.609 5	0.336 18	0.624 53	0.733 61	0.758 23	0.776 41	0.570 68	0.949 8	0.877 16	0.728 24

MVF-GNN	0.743 29	0.731 73	0.810 24	0.726 65	0.775 9	0.843 32	0.528 29	0.897 13	0.679 18	0.674 16	0.954 38	0.583 19	0.322 26	0.782 6	0.720 67	0.802 9	0.785 36	0.707 10	0.935 20	0.863 28	0.745 16

PointConvFormer	0.749 21	0.793 43	0.790 39	0.807 40	0.750 28	0.856 14	0.524 30	0.881 18	0.588 57	0.642 31	0.977 9	0.591 12	0.274 50	0.781 8	0.929 4	0.804 7	0.796 26	0.642 38	0.947 10	0.885 10	0.715 34
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
JSENet	0.699 46	0.881 19	0.762 54	0.821 29	0.667 49	0.800 74	0.522 31	0.792 52	0.613 43	0.607 45	0.935 87	0.492 50	0.205 81	0.576 64	0.853 36	0.691 52	0.758 56	0.652 34	0.872 72	0.828 50	0.649 52
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
MinkowskiNet	0.736 34	0.859 24	0.818 15	0.832 27	0.709 39	0.840 34	0.521 32	0.853 27	0.660 25	0.643 28	0.951 49	0.544 33	0.286 42	0.731 34	0.893 17	0.675 58	0.772 43	0.683 21	0.874 69	0.852 39	0.727 26
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
PicassoNet-II	0.692 48	0.732 72	0.772 48	0.786 50	0.677 47	0.866 9	0.517 33	0.848 29	0.509 83	0.626 36	0.952 47	0.536 36	0.225 72	0.545 77	0.704 70	0.689 55	0.810 14	0.564 73	0.903 44	0.854 38	0.729 23
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
ConDaFormer	0.755 17	0.927 6	0.822 9	0.836 23	0.801 1	0.849 24	0.516 34	0.864 25	0.651 29	0.680 13	0.958 23	0.584 18	0.282 44	0.759 24	0.855 34	0.728 32	0.802 17	0.678 22	0.880 63	0.873 23	0.756 15
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
DiffSeg3D2	0.745 27	0.725 78	0.814 19	0.837 22	0.751 26	0.831 44	0.514 35	0.896 15	0.674 19	0.684 11	0.960 19	0.564 26	0.303 34	0.773 13	0.820 47	0.713 43	0.798 24	0.690 19	0.923 31	0.875 20	0.757 14

StratifiedFormer	0.747 24	0.901 14	0.803 29	0.845 16	0.757 21	0.846 29	0.512 36	0.825 39	0.696 9	0.645 26	0.956 29	0.576 21	0.262 61	0.744 33	0.861 28	0.742 27	0.770 46	0.705 11	0.899 48	0.860 31	0.734 21
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
DTC	0.757 15	0.843 28	0.820 11	0.847 14	0.791 2	0.862 11	0.511 37	0.870 21	0.707 5	0.652 24	0.954 38	0.604 8	0.279 47	0.760 22	0.942 2	0.734 30	0.766 48	0.701 13	0.884 58	0.874 22	0.736 20

3DSM_DMMF	0.631 78	0.626 97	0.745 67	0.801 45	0.607 67	0.751 95	0.506 38	0.729 70	0.565 68	0.491 82	0.866 112	0.434 75	0.197 87	0.595 58	0.630 82	0.709 44	0.705 79	0.560 74	0.875 67	0.740 97	0.491 101

HPGCNN	0.656 65	0.698 87	0.743 69	0.650 90	0.564 82	0.820 57	0.505 39	0.758 59	0.631 37	0.479 84	0.945 67	0.480 56	0.226 70	0.572 66	0.774 55	0.690 53	0.735 67	0.614 50	0.853 84	0.776 86	0.597 73
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
PointTransformer++	0.725 37	0.727 77	0.811 23	0.819 31	0.765 16	0.841 33	0.502 40	0.814 45	0.621 41	0.623 38	0.955 32	0.556 30	0.284 43	0.620 55	0.866 26	0.781 15	0.757 58	0.648 35	0.932 25	0.862 29	0.709 35

VMNet	0.746 25	0.870 20	0.838 3	0.858 5	0.729 35	0.850 23	0.501 41	0.874 20	0.587 58	0.658 22	0.956 29	0.564 26	0.299 35	0.765 20	0.900 14	0.716 40	0.812 13	0.631 43	0.939 16	0.858 32	0.709 35
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)
FusionAwareConv	0.630 81	0.604 102	0.741 71	0.766 59	0.590 73	0.747 96	0.501 41	0.734 68	0.503 85	0.527 70	0.919 102	0.454 67	0.323 25	0.550 76	0.420 101	0.678 57	0.688 85	0.544 84	0.896 50	0.795 70	0.627 61
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
MSP	0.748 23	0.623 98	0.804 28	0.859 4	0.745 30	0.824 52	0.501 41	0.912 7	0.690 11	0.685 10	0.956 29	0.567 24	0.320 27	0.768 18	0.918 7	0.720 37	0.802 17	0.676 26	0.921 33	0.881 12	0.779 8

FusionNet	0.688 50	0.704 84	0.741 71	0.754 62	0.656 51	0.829 46	0.501 41	0.741 66	0.609 47	0.548 61	0.950 53	0.522 42	0.371 5	0.633 50	0.756 56	0.715 41	0.771 45	0.623 47	0.861 80	0.814 59	0.658 49
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
DGNet	0.684 52	0.712 83	0.784 43	0.782 54	0.658 50	0.835 40	0.499 45	0.823 41	0.641 33	0.597 51	0.950 53	0.487 53	0.281 45	0.575 65	0.619 83	0.647 71	0.764 50	0.620 49	0.871 75	0.846 44	0.688 42

One-Thing-One-Click	0.693 47	0.743 67	0.794 36	0.655 89	0.684 45	0.822 54	0.497 46	0.719 71	0.622 40	0.617 39	0.977 9	0.447 73	0.339 16	0.750 31	0.664 79	0.703 48	0.790 33	0.596 58	0.946 12	0.855 36	0.647 53
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
MatchingNet	0.724 39	0.812 39	0.812 21	0.810 37	0.735 33	0.834 41	0.495 47	0.860 26	0.572 64	0.602 48	0.954 38	0.512 45	0.280 46	0.757 25	0.845 40	0.725 34	0.780 38	0.606 54	0.937 18	0.851 40	0.700 39

RFCR	0.702 44	0.889 17	0.745 67	0.813 34	0.672 48	0.818 62	0.493 48	0.815 44	0.623 39	0.610 42	0.947 61	0.470 60	0.249 65	0.594 59	0.848 39	0.705 46	0.779 39	0.646 36	0.892 53	0.823 53	0.611 63
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
EQ-Net	0.743 29	0.620 99	0.799 33	0.849 11	0.730 34	0.822 54	0.493 48	0.897 13	0.664 22	0.681 12	0.955 32	0.562 28	0.378 4	0.760 22	0.903 12	0.738 28	0.801 21	0.673 30	0.907 40	0.877 16	0.745 16
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
RPN	0.736 34	0.776 51	0.790 39	0.851 9	0.754 23	0.854 17	0.491 50	0.866 23	0.596 55	0.686 9	0.955 32	0.536 36	0.342 15	0.624 53	0.869 25	0.787 12	0.802 17	0.628 44	0.927 27	0.875 20	0.704 37

BPNet	0.749 21	0.909 11	0.818 15	0.811 36	0.752 24	0.839 36	0.485 51	0.842 32	0.673 20	0.644 27	0.957 28	0.528 41	0.305 32	0.773 13	0.859 29	0.788 11	0.818 7	0.693 16	0.916 36	0.856 34	0.723 29
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
dtc_net	0.625 84	0.703 85	0.751 63	0.794 48	0.535 88	0.848 26	0.480 52	0.676 84	0.528 77	0.469 87	0.944 73	0.454 67	0.004 117	0.464 94	0.636 81	0.704 47	0.758 56	0.548 83	0.924 30	0.787 80	0.492 100

PointConv	0.666 61	0.781 48	0.759 57	0.699 74	0.644 59	0.822 54	0.475 53	0.779 53	0.564 69	0.504 80	0.953 43	0.428 80	0.203 83	0.586 62	0.754 57	0.661 64	0.753 59	0.588 64	0.902 45	0.813 61	0.642 55
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
KP-FCNN	0.684 52	0.847 27	0.758 59	0.784 52	0.647 56	0.814 65	0.473 54	0.772 55	0.605 49	0.594 53	0.935 87	0.450 71	0.181 92	0.587 60	0.805 51	0.690 53	0.785 36	0.614 50	0.882 60	0.819 57	0.632 59
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
ClickSeg_Semantic	0.703 43	0.774 53	0.800 30	0.793 49	0.760 18	0.847 28	0.471 55	0.802 49	0.463 97	0.634 34	0.968 13	0.491 51	0.271 54	0.726 35	0.910 9	0.706 45	0.815 8	0.551 80	0.878 64	0.833 47	0.570 80

PointASNL	0.666 61	0.703 85	0.781 45	0.751 64	0.655 52	0.830 45	0.471 55	0.769 56	0.474 93	0.537 65	0.951 49	0.475 58	0.279 47	0.635 48	0.698 73	0.675 58	0.751 60	0.553 79	0.816 92	0.806 63	0.703 38
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
DCM-Net	0.658 64	0.778 49	0.702 81	0.806 42	0.619 65	0.813 68	0.468 57	0.693 79	0.494 86	0.524 72	0.941 79	0.449 72	0.298 36	0.510 85	0.821 46	0.675 58	0.727 71	0.568 71	0.826 90	0.803 65	0.637 57
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
Superpoint Network	0.683 55	0.851 26	0.728 75	0.800 46	0.653 53	0.806 70	0.468 57	0.804 47	0.572 64	0.602 48	0.946 64	0.453 70	0.239 69	0.519 83	0.822 45	0.689 55	0.762 53	0.595 60	0.895 51	0.827 51	0.630 60

PointMRNet	0.640 70	0.717 82	0.701 82	0.692 77	0.576 79	0.801 73	0.467 59	0.716 72	0.563 70	0.459 90	0.953 43	0.429 79	0.169 97	0.581 63	0.854 35	0.605 83	0.710 74	0.550 81	0.894 52	0.793 73	0.575 78

VACNN++	0.684 52	0.728 76	0.757 60	0.776 55	0.690 42	0.804 72	0.464 60	0.816 42	0.577 63	0.587 55	0.945 67	0.508 47	0.276 49	0.671 40	0.710 68	0.663 63	0.750 62	0.589 63	0.881 61	0.832 49	0.653 51

PD-Net	0.638 72	0.797 42	0.769 52	0.641 95	0.590 73	0.820 57	0.461 61	0.537 103	0.637 35	0.536 66	0.947 61	0.388 93	0.206 80	0.656 42	0.668 77	0.647 71	0.732 69	0.585 65	0.868 77	0.793 73	0.473 106

AttAN	0.609 88	0.760 60	0.667 96	0.649 91	0.521 91	0.793 78	0.457 62	0.648 88	0.528 77	0.434 97	0.947 61	0.401 90	0.153 103	0.454 95	0.721 66	0.648 70	0.717 73	0.536 89	0.904 42	0.765 90	0.485 102
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
SALANet	0.670 59	0.816 37	0.770 51	0.768 57	0.652 54	0.807 69	0.451 63	0.747 63	0.659 27	0.545 62	0.924 98	0.473 59	0.149 105	0.571 67	0.811 50	0.635 77	0.746 63	0.623 47	0.892 53	0.794 71	0.570 80

PointConv-SFPN	0.641 68	0.776 51	0.703 80	0.721 68	0.557 85	0.826 49	0.451 63	0.672 85	0.563 70	0.483 83	0.943 76	0.425 83	0.162 100	0.644 46	0.726 62	0.659 65	0.709 76	0.572 67	0.875 67	0.786 81	0.559 86

PPCNN++	0.663 63	0.746 65	0.708 78	0.722 67	0.638 61	0.820 57	0.451 63	0.566 99	0.599 53	0.541 63	0.950 53	0.510 46	0.313 29	0.648 45	0.819 48	0.616 82	0.682 87	0.590 62	0.869 76	0.810 62	0.656 50
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
Feature-Geometry Net	0.685 51	0.866 21	0.748 64	0.819 31	0.645 58	0.794 77	0.450 66	0.802 49	0.587 58	0.604 46	0.945 67	0.464 62	0.201 84	0.554 73	0.840 41	0.723 36	0.732 69	0.602 56	0.907 40	0.822 55	0.603 70

SIConv	0.625 84	0.830 32	0.694 87	0.757 61	0.563 83	0.772 89	0.448 67	0.647 89	0.520 79	0.509 76	0.949 57	0.431 78	0.191 88	0.496 89	0.614 84	0.647 71	0.672 91	0.535 90	0.876 66	0.783 82	0.571 79

PointSPNet	0.637 73	0.734 71	0.692 89	0.714 71	0.576 79	0.797 76	0.446 68	0.743 65	0.598 54	0.437 95	0.942 77	0.403 89	0.150 104	0.626 52	0.800 53	0.649 68	0.697 81	0.557 77	0.846 86	0.777 85	0.563 84

RandLA-Net	0.645 67	0.778 49	0.731 74	0.699 74	0.577 78	0.829 46	0.446 68	0.736 67	0.477 92	0.523 74	0.945 67	0.454 67	0.269 56	0.484 92	0.749 60	0.618 80	0.738 64	0.599 57	0.827 89	0.792 76	0.621 62

TextureNet	0.566 97	0.672 91	0.664 97	0.671 84	0.494 94	0.719 99	0.445 70	0.678 83	0.411 106	0.396 100	0.935 87	0.356 98	0.225 72	0.412 100	0.535 90	0.565 96	0.636 101	0.464 103	0.794 95	0.680 107	0.568 82
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
SConv	0.636 74	0.830 32	0.697 85	0.752 63	0.572 81	0.780 85	0.445 70	0.716 72	0.529 76	0.530 68	0.951 49	0.446 74	0.170 96	0.507 87	0.666 78	0.636 76	0.682 87	0.541 87	0.886 57	0.799 66	0.594 74

DenSeR	0.628 82	0.800 41	0.625 104	0.719 69	0.545 87	0.806 70	0.445 70	0.597 94	0.448 100	0.519 75	0.938 83	0.481 55	0.328 23	0.489 91	0.499 96	0.657 66	0.759 55	0.592 61	0.881 61	0.797 69	0.634 58

PointMTL	0.632 77	0.731 73	0.688 92	0.675 81	0.591 72	0.784 82	0.444 73	0.565 100	0.610 45	0.492 81	0.949 57	0.456 66	0.254 63	0.587 60	0.706 69	0.599 86	0.665 93	0.612 53	0.868 77	0.791 79	0.579 77

Supervoxel-CNN	0.635 75	0.656 92	0.711 77	0.719 69	0.613 66	0.757 94	0.444 73	0.765 57	0.534 75	0.566 57	0.928 96	0.478 57	0.272 52	0.636 47	0.531 91	0.664 62	0.645 97	0.508 95	0.864 79	0.792 76	0.611 63

PointNet2-SFPN	0.631 78	0.771 55	0.692 89	0.672 82	0.524 90	0.837 37	0.440 75	0.706 77	0.538 74	0.446 92	0.944 73	0.421 85	0.219 75	0.552 74	0.751 59	0.591 90	0.737 65	0.543 86	0.901 47	0.768 89	0.557 87

contrastBoundary	0.705 42	0.769 58	0.775 47	0.809 38	0.687 44	0.820 57	0.439 76	0.812 46	0.661 24	0.591 54	0.945 67	0.515 44	0.171 95	0.633 50	0.856 32	0.720 37	0.796 26	0.668 31	0.889 55	0.847 42	0.689 41
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
wsss-transformer	0.600 90	0.634 96	0.743 69	0.697 76	0.601 70	0.781 83	0.437 77	0.585 97	0.493 87	0.446 92	0.933 92	0.394 91	0.011 116	0.654 43	0.661 80	0.603 84	0.733 68	0.526 91	0.832 88	0.761 92	0.480 103

One Thing One Click	0.701 45	0.825 34	0.796 34	0.723 66	0.716 38	0.832 43	0.433 78	0.816 42	0.634 36	0.609 43	0.969 11	0.418 86	0.344 14	0.559 71	0.833 43	0.715 41	0.808 15	0.560 74	0.902 45	0.847 42	0.680 44

SQN_0.1%	0.569 96	0.676 89	0.696 86	0.657 88	0.497 93	0.779 86	0.424 79	0.548 101	0.515 81	0.376 102	0.902 109	0.422 84	0.357 9	0.379 103	0.456 98	0.596 88	0.659 94	0.544 84	0.685 106	0.665 110	0.556 88

Feature_GeometricNet	0.690 49	0.884 18	0.754 61	0.795 47	0.647 56	0.818 62	0.422 80	0.802 49	0.612 44	0.604 46	0.945 67	0.462 63	0.189 89	0.563 70	0.853 36	0.726 33	0.765 49	0.632 42	0.904 42	0.821 56	0.606 67
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
joint point-based	0.634 76	0.614 100	0.778 46	0.667 86	0.633 63	0.825 50	0.420 81	0.804 47	0.467 95	0.561 58	0.951 49	0.494 49	0.291 39	0.566 68	0.458 97	0.579 94	0.764 50	0.559 76	0.838 87	0.814 59	0.598 72
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
HPEIN	0.618 86	0.729 75	0.668 95	0.647 92	0.597 71	0.766 90	0.414 82	0.680 81	0.520 79	0.525 71	0.946 64	0.432 76	0.215 77	0.493 90	0.599 85	0.638 75	0.617 102	0.570 68	0.897 49	0.806 63	0.605 69
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
APCF-Net	0.631 78	0.742 68	0.687 94	0.672 82	0.557 85	0.792 80	0.408 83	0.665 86	0.545 73	0.508 77	0.952 47	0.428 80	0.186 90	0.634 49	0.702 71	0.620 79	0.706 78	0.555 78	0.873 70	0.798 68	0.581 76
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointContrast_LA_SEM	0.683 55	0.757 62	0.784 43	0.786 50	0.639 60	0.824 52	0.408 83	0.775 54	0.604 50	0.541 63	0.934 91	0.532 39	0.269 56	0.552 74	0.777 54	0.645 74	0.793 30	0.640 39	0.913 39	0.824 52	0.671 46

ROSMRF3D	0.673 58	0.789 44	0.748 64	0.763 60	0.635 62	0.814 65	0.407 85	0.747 63	0.581 62	0.573 56	0.950 53	0.484 54	0.271 54	0.607 57	0.754 57	0.649 68	0.774 42	0.596 58	0.883 59	0.823 53	0.606 67

SPH3D-GCN	0.610 87	0.858 25	0.772 48	0.489 110	0.532 89	0.792 80	0.404 86	0.643 90	0.570 67	0.507 79	0.935 87	0.414 87	0.046 114	0.510 85	0.702 71	0.602 85	0.705 79	0.549 82	0.859 81	0.773 87	0.534 93
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
SAFNet-seg	0.654 66	0.752 63	0.734 73	0.664 87	0.583 77	0.815 64	0.399 87	0.754 61	0.639 34	0.535 67	0.942 77	0.470 60	0.309 31	0.665 41	0.539 89	0.650 67	0.708 77	0.635 41	0.857 83	0.793 73	0.642 55
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
Virtual MVFusion	0.746 25	0.771 55	0.819 13	0.848 13	0.702 41	0.865 10	0.397 88	0.899 12	0.699 7	0.664 21	0.948 59	0.588 14	0.330 22	0.746 32	0.851 38	0.764 21	0.796 26	0.704 12	0.935 20	0.866 26	0.728 24
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
MVPNet	0.641 68	0.831 31	0.715 76	0.671 84	0.590 73	0.781 83	0.394 89	0.679 82	0.642 32	0.553 60	0.937 84	0.462 63	0.256 62	0.649 44	0.406 102	0.626 78	0.691 84	0.666 32	0.877 65	0.792 76	0.608 66
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
FPConv	0.639 71	0.785 46	0.760 56	0.713 72	0.603 68	0.798 75	0.392 90	0.534 104	0.603 51	0.524 72	0.948 59	0.457 65	0.250 64	0.538 78	0.723 65	0.598 87	0.696 82	0.614 50	0.872 72	0.799 66	0.567 83
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
Pointnet++ & Feature	0.557 99	0.735 70	0.661 98	0.686 78	0.491 95	0.744 97	0.392 90	0.539 102	0.451 99	0.375 103	0.946 64	0.376 95	0.205 81	0.403 101	0.356 105	0.553 97	0.643 98	0.497 97	0.824 91	0.756 93	0.515 96

LAP-D	0.594 91	0.720 80	0.692 89	0.637 96	0.456 101	0.773 88	0.391 92	0.730 69	0.587 58	0.445 94	0.940 81	0.381 94	0.288 40	0.434 98	0.453 99	0.591 90	0.649 95	0.581 66	0.777 96	0.749 96	0.610 65

CCRFNet	0.589 93	0.766 59	0.659 99	0.683 79	0.470 100	0.740 98	0.387 93	0.620 93	0.490 88	0.476 85	0.922 100	0.355 99	0.245 67	0.511 84	0.511 94	0.571 95	0.643 98	0.493 99	0.872 72	0.762 91	0.600 71

SegGroup_sem	0.627 83	0.818 36	0.747 66	0.701 73	0.602 69	0.764 91	0.385 94	0.629 91	0.490 88	0.508 77	0.931 95	0.409 88	0.201 84	0.564 69	0.725 63	0.618 80	0.692 83	0.539 88	0.873 70	0.794 71	0.548 90
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
DPC	0.592 92	0.720 80	0.700 83	0.602 101	0.480 96	0.762 93	0.380 95	0.713 75	0.585 61	0.437 95	0.940 81	0.369 96	0.288 40	0.434 98	0.509 95	0.590 92	0.639 100	0.567 72	0.772 97	0.755 94	0.592 75
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
SD-DETR	0.576 95	0.746 65	0.609 108	0.445 114	0.517 92	0.643 109	0.366 96	0.714 74	0.456 98	0.468 88	0.870 111	0.432 76	0.264 59	0.558 72	0.674 75	0.586 93	0.688 85	0.482 101	0.739 101	0.733 99	0.537 92

ROSMRF	0.580 94	0.772 54	0.707 79	0.681 80	0.563 83	0.764 91	0.362 97	0.515 105	0.465 96	0.465 89	0.936 86	0.427 82	0.207 79	0.438 96	0.577 87	0.536 98	0.675 90	0.486 100	0.723 103	0.779 83	0.524 95

Online SegFusion	0.515 103	0.607 101	0.644 102	0.579 103	0.434 103	0.630 111	0.353 98	0.628 92	0.440 101	0.410 98	0.762 117	0.307 104	0.167 98	0.520 82	0.403 103	0.516 99	0.565 105	0.447 107	0.678 107	0.701 104	0.514 98
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
Tangent Convolutions	0.438 112	0.437 115	0.646 101	0.474 111	0.369 108	0.645 108	0.353 98	0.258 115	0.282 117	0.279 111	0.918 103	0.298 106	0.147 106	0.283 109	0.294 107	0.487 102	0.562 106	0.427 111	0.619 111	0.633 112	0.352 113
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
Weakly-Openseg v3	0.604 89	0.901 14	0.762 54	0.627 97	0.478 97	0.820 57	0.346 100	0.689 80	0.353 110	0.528 69	0.933 92	0.217 115	0.172 94	0.530 80	0.725 63	0.593 89	0.737 65	0.515 92	0.858 82	0.772 88	0.515 96

subcloud_weak	0.516 102	0.676 89	0.591 111	0.609 98	0.442 102	0.774 87	0.335 101	0.597 94	0.422 105	0.357 105	0.932 94	0.341 101	0.094 110	0.298 107	0.528 93	0.473 105	0.676 89	0.495 98	0.602 112	0.721 102	0.349 114

DVVNet	0.562 98	0.648 93	0.700 83	0.770 56	0.586 76	0.687 103	0.333 102	0.650 87	0.514 82	0.475 86	0.906 106	0.359 97	0.223 74	0.340 105	0.442 100	0.422 109	0.668 92	0.501 96	0.708 104	0.779 83	0.534 93

3DMV	0.484 106	0.484 112	0.538 114	0.643 94	0.424 104	0.606 114	0.310 103	0.574 98	0.433 104	0.378 101	0.796 115	0.301 105	0.214 78	0.537 79	0.208 113	0.472 106	0.507 114	0.413 112	0.693 105	0.602 114	0.539 91
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
DGCNN_reproduce	0.446 109	0.474 113	0.623 105	0.463 112	0.366 109	0.651 107	0.310 103	0.389 110	0.349 112	0.330 107	0.937 84	0.271 109	0.126 107	0.285 108	0.224 111	0.350 114	0.577 104	0.445 108	0.625 110	0.723 101	0.394 110
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
PNET2	0.442 110	0.548 107	0.548 113	0.597 102	0.363 110	0.628 112	0.300 105	0.292 113	0.374 108	0.307 109	0.881 110	0.268 110	0.186 90	0.238 112	0.204 114	0.407 110	0.506 115	0.449 106	0.667 108	0.620 113	0.462 108

GMLPs	0.538 100	0.495 110	0.693 88	0.647 92	0.471 99	0.793 78	0.300 105	0.477 106	0.505 84	0.358 104	0.903 108	0.327 102	0.081 111	0.472 93	0.529 92	0.448 107	0.710 74	0.509 93	0.746 99	0.737 98	0.554 89

PointCNN with RGB	0.458 107	0.577 104	0.611 107	0.356 118	0.321 114	0.715 100	0.299 107	0.376 111	0.328 114	0.319 108	0.944 73	0.285 107	0.164 99	0.216 115	0.229 110	0.484 103	0.545 109	0.456 105	0.755 98	0.709 103	0.475 105
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 108	0.679 88	0.604 110	0.578 104	0.380 107	0.682 104	0.291 108	0.106 118	0.483 91	0.258 116	0.920 101	0.258 111	0.025 115	0.231 114	0.325 106	0.480 104	0.560 107	0.463 104	0.725 102	0.666 109	0.231 118
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
GrowSP++	0.323 117	0.114 119	0.589 112	0.499 108	0.147 119	0.555 115	0.290 109	0.336 112	0.290 116	0.262 114	0.865 113	0.102 119	0.000 119	0.037 118	0.000 120	0.000 120	0.462 116	0.381 116	0.389 118	0.664 111	0.473 106

ScanNet+FTSDF	0.383 115	0.297 117	0.491 116	0.432 115	0.358 111	0.612 113	0.274 110	0.116 117	0.411 106	0.265 113	0.904 107	0.229 113	0.079 112	0.250 110	0.185 115	0.320 115	0.510 112	0.385 114	0.548 114	0.597 117	0.394 110

PointNet++	0.339 116	0.584 103	0.478 117	0.458 113	0.256 117	0.360 119	0.250 111	0.247 116	0.278 118	0.261 115	0.677 118	0.183 116	0.117 108	0.212 116	0.145 117	0.364 112	0.346 119	0.232 119	0.548 114	0.523 118	0.252 117
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
3DMV, FTSDF	0.501 104	0.558 106	0.608 109	0.424 116	0.478 97	0.690 102	0.246 112	0.586 96	0.468 94	0.450 91	0.911 104	0.394 91	0.160 101	0.438 96	0.212 112	0.432 108	0.541 110	0.475 102	0.742 100	0.727 100	0.477 104

SPLAT Net	0.393 114	0.472 114	0.511 115	0.606 99	0.311 115	0.656 105	0.245 113	0.405 108	0.328 114	0.197 117	0.927 97	0.227 114	0.000 119	0.001 120	0.249 109	0.271 117	0.510 112	0.383 115	0.593 113	0.699 105	0.267 116
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
PCNN	0.498 105	0.559 105	0.644 102	0.560 105	0.420 105	0.711 101	0.229 114	0.414 107	0.436 102	0.352 106	0.941 79	0.324 103	0.155 102	0.238 112	0.387 104	0.493 101	0.529 111	0.509 93	0.813 93	0.751 95	0.504 99

SurfaceConvPF	0.442 110	0.505 109	0.622 106	0.380 117	0.342 112	0.654 106	0.227 115	0.397 109	0.367 109	0.276 112	0.924 98	0.240 112	0.198 86	0.359 104	0.262 108	0.366 111	0.581 103	0.435 110	0.640 109	0.668 108	0.398 109
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PanopticFusion-label	0.529 101	0.491 111	0.688 92	0.604 100	0.386 106	0.632 110	0.225 116	0.705 78	0.434 103	0.293 110	0.815 114	0.348 100	0.241 68	0.499 88	0.669 76	0.507 100	0.649 95	0.442 109	0.796 94	0.602 114	0.561 85
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
ScanNet	0.306 119	0.203 118	0.366 118	0.501 107	0.311 115	0.524 117	0.211 117	0.002 120	0.342 113	0.189 118	0.786 116	0.145 118	0.102 109	0.245 111	0.152 116	0.318 116	0.348 118	0.300 118	0.460 117	0.437 119	0.182 119
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SSC-UNet	0.308 118	0.353 116	0.290 119	0.278 119	0.166 118	0.553 116	0.169 118	0.286 114	0.147 119	0.148 119	0.908 105	0.182 117	0.064 113	0.023 119	0.018 119	0.354 113	0.363 117	0.345 117	0.546 116	0.685 106	0.278 115

3DWSSS	0.425 113	0.525 108	0.647 100	0.522 106	0.324 113	0.488 118	0.077 119	0.712 76	0.353 110	0.401 99	0.636 119	0.281 108	0.176 93	0.340 105	0.565 88	0.175 118	0.551 108	0.398 113	0.370 119	0.602 114	0.361 112

ERROR	0.054 120	0.000 120	0.041 120	0.172 120	0.030 120	0.062 120	0.001 120	0.035 119	0.004 120	0.051 120	0.143 120	0.019 120	0.003 118	0.041 117	0.050 118	0.003 119	0.054 120	0.018 120	0.005 120	0.264 120	0.082 120

3D Semantic Label Benchmark