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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ActiveST | 0.748 1 | 0.984 1 | 0.804 3 | 0.759 5 | 0.720 2 | 0.849 5 | 0.516 2 | 0.791 3 | 0.670 1 | 0.654 2 | 0.974 1 | 0.495 5 | 0.382 1 | 0.811 1 | 0.828 5 | 0.787 1 | 0.780 6 | 0.640 2 | 0.952 1 | 0.861 3 | 0.701 1 | |
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation. | ||||||||||||||||||||||
Q2E | 0.743 2 | 0.984 1 | 0.803 4 | 0.770 1 | 0.725 1 | 0.881 1 | 0.572 1 | 0.806 2 | 0.663 2 | 0.665 1 | 0.972 2 | 0.506 3 | 0.305 2 | 0.652 6 | 0.829 4 | 0.761 2 | 0.809 2 | 0.660 1 | 0.951 2 | 0.862 2 | 0.682 2 | |
DE-3DLearner LA | 0.709 3 | 0.877 4 | 0.772 8 | 0.744 9 | 0.694 3 | 0.836 7 | 0.453 6 | 0.787 4 | 0.623 4 | 0.598 4 | 0.953 4 | 0.490 7 | 0.216 11 | 0.682 5 | 0.879 1 | 0.727 3 | 0.802 3 | 0.604 5 | 0.922 3 | 0.845 4 | 0.676 3 | |
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022 | ||||||||||||||||||||||
WS3D_LA_Sem | 0.694 4 | 0.895 3 | 0.743 10 | 0.767 2 | 0.675 6 | 0.826 10 | 0.496 3 | 0.817 1 | 0.612 5 | 0.613 3 | 0.947 10 | 0.460 9 | 0.254 6 | 0.558 11 | 0.811 7 | 0.710 5 | 0.776 8 | 0.616 3 | 0.874 11 | 0.822 6 | 0.603 12 | |
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022 | ||||||||||||||||||||||
One-Thing-One-Click | 0.694 4 | 0.760 9 | 0.815 2 | 0.706 13 | 0.684 5 | 0.840 6 | 0.492 4 | 0.701 9 | 0.557 7 | 0.596 5 | 0.972 2 | 0.497 4 | 0.281 4 | 0.709 2 | 0.757 8 | 0.689 6 | 0.789 4 | 0.600 7 | 0.907 7 | 0.864 1 | 0.671 4 | |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 | ||||||||||||||||||||||
VIBUS | 0.691 6 | 0.860 5 | 0.731 12 | 0.738 10 | 0.672 7 | 0.860 2 | 0.470 5 | 0.766 5 | 0.625 3 | 0.547 11 | 0.949 5 | 0.491 6 | 0.255 5 | 0.693 4 | 0.715 10 | 0.712 4 | 0.778 7 | 0.597 8 | 0.911 5 | 0.816 9 | 0.635 7 | |
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 | ||||||||||||||||||||||
LE | 0.688 7 | 0.856 7 | 0.779 6 | 0.754 7 | 0.687 4 | 0.834 8 | 0.438 8 | 0.732 7 | 0.536 9 | 0.577 6 | 0.948 6 | 0.508 2 | 0.248 7 | 0.699 3 | 0.831 3 | 0.636 8 | 0.752 11 | 0.586 9 | 0.895 9 | 0.821 7 | 0.643 6 | |
GaIA | 0.685 8 | 0.759 10 | 0.834 1 | 0.759 5 | 0.650 8 | 0.859 3 | 0.427 10 | 0.694 10 | 0.524 10 | 0.575 7 | 0.948 6 | 0.537 1 | 0.304 3 | 0.534 12 | 0.853 2 | 0.678 7 | 0.820 1 | 0.581 10 | 0.914 4 | 0.828 5 | 0.626 8 | |
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 | ||||||||||||||||||||||
Viewpoint_BN_LA_AIR | 0.669 9 | 0.847 8 | 0.732 11 | 0.724 11 | 0.613 12 | 0.827 9 | 0.443 7 | 0.742 6 | 0.562 6 | 0.551 10 | 0.947 10 | 0.441 12 | 0.218 10 | 0.650 7 | 0.753 9 | 0.621 9 | 0.765 10 | 0.601 6 | 0.905 8 | 0.814 12 | 0.618 9 | |
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck. | ||||||||||||||||||||||
CSC_LA_SEM | 0.665 10 | 0.857 6 | 0.756 9 | 0.763 4 | 0.647 9 | 0.852 4 | 0.432 9 | 0.684 12 | 0.543 8 | 0.514 12 | 0.948 6 | 0.469 8 | 0.179 12 | 0.599 9 | 0.702 11 | 0.620 10 | 0.789 4 | 0.614 4 | 0.911 5 | 0.815 11 | 0.607 11 | |
PointContrast_LA_SEM | 0.653 11 | 0.717 12 | 0.775 7 | 0.754 7 | 0.626 11 | 0.804 13 | 0.391 12 | 0.689 11 | 0.485 13 | 0.572 9 | 0.945 12 | 0.448 10 | 0.232 9 | 0.603 8 | 0.813 6 | 0.591 12 | 0.775 9 | 0.537 12 | 0.885 10 | 0.816 9 | 0.608 10 | |
Scratch_LA_SEM | 0.643 12 | 0.699 13 | 0.793 5 | 0.718 12 | 0.636 10 | 0.816 11 | 0.411 11 | 0.707 8 | 0.490 12 | 0.574 8 | 0.948 6 | 0.448 10 | 0.173 13 | 0.559 10 | 0.689 12 | 0.604 11 | 0.722 12 | 0.556 11 | 0.853 12 | 0.820 8 | 0.651 5 | |
SQN_LA | 0.598 13 | 0.741 11 | 0.681 13 | 0.766 3 | 0.482 13 | 0.805 12 | 0.389 13 | 0.658 13 | 0.499 11 | 0.437 13 | 0.936 13 | 0.386 13 | 0.243 8 | 0.422 13 | 0.663 13 | 0.552 13 | 0.700 13 | 0.519 13 | 0.809 13 | 0.750 13 | 0.515 13 | |