3D Semantic Label with Limited Annotations Benchmark
This table lists the benchmark results for the 3D semantic label with limited annotations 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Q2E | 0.741 1 | 0.984 1 | 0.821 2 | 0.757 4 | 0.739 1 | 0.868 2 | 0.600 1 | 0.849 1 | 0.595 6 | 0.659 1 | 0.971 2 | 0.490 2 | 0.299 2 | 0.689 4 | 0.822 3 | 0.749 1 | 0.788 4 | 0.641 1 | 0.935 2 | 0.860 1 | 0.699 2 | |
ActiveST | 0.735 2 | 0.983 2 | 0.769 4 | 0.798 1 | 0.701 2 | 0.852 5 | 0.527 2 | 0.801 2 | 0.680 1 | 0.629 2 | 0.973 1 | 0.447 10 | 0.312 1 | 0.757 1 | 0.799 4 | 0.747 2 | 0.795 3 | 0.632 2 | 0.952 1 | 0.855 2 | 0.684 3 | |
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation. | ||||||||||||||||||||||
DE-3DLearner LA | 0.704 3 | 0.774 7 | 0.766 5 | 0.764 3 | 0.687 4 | 0.832 7 | 0.413 11 | 0.790 4 | 0.639 2 | 0.599 4 | 0.952 4 | 0.478 6 | 0.222 8 | 0.746 2 | 0.859 1 | 0.678 4 | 0.806 2 | 0.607 6 | 0.915 5 | 0.847 3 | 0.703 1 | |
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022 | ||||||||||||||||||||||
WS3D_LA_Sem | 0.689 4 | 0.879 3 | 0.753 6 | 0.798 1 | 0.648 8 | 0.816 9 | 0.421 10 | 0.796 3 | 0.604 5 | 0.603 3 | 0.945 10 | 0.457 9 | 0.204 9 | 0.559 10 | 0.851 2 | 0.724 3 | 0.760 7 | 0.630 3 | 0.903 7 | 0.821 5 | 0.603 8 | |
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022 | ||||||||||||||||||||||
VIBUS | 0.684 5 | 0.848 4 | 0.752 7 | 0.708 9 | 0.691 3 | 0.861 3 | 0.474 5 | 0.770 5 | 0.611 4 | 0.538 9 | 0.951 5 | 0.478 6 | 0.275 4 | 0.676 5 | 0.671 11 | 0.649 8 | 0.788 4 | 0.610 5 | 0.869 9 | 0.808 10 | 0.657 4 | |
Beiwen Tian,Liyi Luo,Hao Zhao,Guyue Zhou: VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling. ISPRS Journal of Photogrammetry and Remote Sensing | ||||||||||||||||||||||
GaIA | 0.682 6 | 0.731 11 | 0.846 1 | 0.713 8 | 0.657 6 | 0.869 1 | 0.475 4 | 0.705 9 | 0.452 13 | 0.569 5 | 0.951 5 | 0.563 1 | 0.290 3 | 0.544 11 | 0.799 4 | 0.677 5 | 0.810 1 | 0.618 4 | 0.900 8 | 0.821 5 | 0.642 5 | |
Min Seok Lee*, Seok Woo Yang*, and Sung Won Han: GaIA: Graphical Information gain based Attention Network for Weakly Supervised 3D Point Cloud Semantic Segmentation. WACV 2023 | ||||||||||||||||||||||
LE | 0.680 7 | 0.744 9 | 0.731 9 | 0.727 6 | 0.664 5 | 0.859 4 | 0.427 9 | 0.759 6 | 0.562 7 | 0.562 6 | 0.948 7 | 0.480 4 | 0.245 6 | 0.735 3 | 0.765 6 | 0.648 10 | 0.786 6 | 0.591 7 | 0.931 3 | 0.817 7 | 0.624 7 | |
One-Thing-One-Click | 0.670 8 | 0.734 10 | 0.815 3 | 0.661 13 | 0.644 9 | 0.841 6 | 0.509 3 | 0.741 7 | 0.479 12 | 0.548 7 | 0.968 3 | 0.461 8 | 0.251 5 | 0.664 6 | 0.754 7 | 0.656 7 | 0.744 10 | 0.541 11 | 0.917 4 | 0.844 4 | 0.625 6 | |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 | ||||||||||||||||||||||
Viewpoint_BN_LA_AIR | 0.650 9 | 0.778 6 | 0.731 9 | 0.688 11 | 0.617 11 | 0.812 11 | 0.446 7 | 0.739 8 | 0.618 3 | 0.540 8 | 0.945 10 | 0.415 11 | 0.204 9 | 0.623 7 | 0.676 10 | 0.594 11 | 0.744 10 | 0.576 8 | 0.868 10 | 0.811 8 | 0.582 10 | |
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck. | ||||||||||||||||||||||
CSC_LA_SEM | 0.644 10 | 0.761 8 | 0.707 12 | 0.703 10 | 0.642 10 | 0.813 10 | 0.436 8 | 0.659 11 | 0.502 9 | 0.516 11 | 0.945 10 | 0.487 3 | 0.238 7 | 0.538 12 | 0.678 9 | 0.659 6 | 0.739 12 | 0.568 10 | 0.915 5 | 0.811 8 | 0.566 12 | |
PointContrast_LA_SEM | 0.636 11 | 0.694 12 | 0.738 8 | 0.731 5 | 0.653 7 | 0.817 8 | 0.467 6 | 0.651 12 | 0.517 8 | 0.522 10 | 0.946 8 | 0.479 5 | 0.198 11 | 0.575 9 | 0.526 13 | 0.649 8 | 0.747 8 | 0.569 9 | 0.845 11 | 0.803 11 | 0.600 9 | |
Scratch_LA_SEM | 0.621 12 | 0.802 5 | 0.715 11 | 0.687 12 | 0.570 12 | 0.800 12 | 0.386 12 | 0.703 10 | 0.486 11 | 0.514 12 | 0.946 8 | 0.390 12 | 0.181 12 | 0.620 8 | 0.670 12 | 0.487 13 | 0.746 9 | 0.539 12 | 0.804 12 | 0.798 12 | 0.580 11 | |
SQN_LA | 0.576 13 | 0.674 13 | 0.670 13 | 0.722 7 | 0.454 13 | 0.790 13 | 0.342 13 | 0.622 13 | 0.487 10 | 0.427 13 | 0.933 13 | 0.357 13 | 0.157 13 | 0.452 13 | 0.721 8 | 0.492 12 | 0.696 13 | 0.487 13 | 0.790 13 | 0.748 13 | 0.507 13 | |