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 |
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ActiveST | 0.703 1 | 0.977 1 | 0.776 2 | 0.657 4 | 0.707 1 | 0.874 1 | 0.541 1 | 0.744 1 | 0.605 1 | 0.610 1 | 0.968 1 | 0.442 3 | 0.126 5 | 0.705 1 | 0.785 1 | 0.742 1 | 0.791 1 | 0.586 1 | 0.940 1 | 0.839 1 | 0.645 1 | |
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation. | ||||||||||||||||||||||
RM3D | ![]() | 0.662 2 | 0.812 3 | 0.762 3 | 0.742 1 | 0.635 2 | 0.828 5 | 0.474 2 | 0.736 2 | 0.588 2 | 0.546 2 | 0.947 4 | 0.450 2 | 0.174 4 | 0.536 4 | 0.752 2 | 0.668 2 | 0.735 4 | 0.583 2 | 0.902 5 | 0.797 5 | 0.573 4 |
Kangcheng Liu: RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging. International Journal of Computer Vision (IJCV), 2022 | ||||||||||||||||||||||
DE-3DLearner LA | 0.639 3 | 0.839 2 | 0.723 5 | 0.681 2 | 0.629 3 | 0.839 4 | 0.424 3 | 0.728 3 | 0.538 4 | 0.526 3 | 0.945 5 | 0.427 5 | 0.120 6 | 0.511 6 | 0.643 5 | 0.547 5 | 0.781 2 | 0.566 4 | 0.905 3 | 0.809 4 | 0.607 3 | |
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022 | ||||||||||||||||||||||
GaIA | 0.638 4 | 0.536 11 | 0.783 1 | 0.651 5 | 0.600 4 | 0.840 2 | 0.413 4 | 0.728 3 | 0.490 6 | 0.520 5 | 0.948 3 | 0.475 1 | 0.299 1 | 0.518 5 | 0.680 3 | 0.629 3 | 0.729 5 | 0.573 3 | 0.906 2 | 0.815 3 | 0.626 2 | |
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 | 0.608 5 | 0.791 4 | 0.726 4 | 0.651 5 | 0.589 5 | 0.779 8 | 0.346 8 | 0.662 7 | 0.493 5 | 0.524 4 | 0.923 12 | 0.430 4 | 0.234 3 | 0.572 2 | 0.638 6 | 0.411 9 | 0.708 6 | 0.533 7 | 0.855 6 | 0.782 6 | 0.508 6 | |
One-Thing-One-Click | 0.594 6 | 0.756 5 | 0.722 6 | 0.494 11 | 0.546 7 | 0.795 6 | 0.371 5 | 0.725 5 | 0.559 3 | 0.488 6 | 0.957 2 | 0.367 7 | 0.261 2 | 0.547 3 | 0.575 11 | 0.225 11 | 0.671 9 | 0.543 5 | 0.904 4 | 0.826 2 | 0.557 5 | |
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021 | ||||||||||||||||||||||
VIBUS | ![]() | 0.586 7 | 0.736 7 | 0.623 10 | 0.664 3 | 0.559 6 | 0.840 2 | 0.358 6 | 0.666 6 | 0.447 7 | 0.429 9 | 0.944 6 | 0.421 6 | 0.000 12 | 0.411 8 | 0.629 7 | 0.614 4 | 0.745 3 | 0.541 6 | 0.848 8 | 0.758 7 | 0.493 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 | ||||||||||||||||||||||
PointContrast_LA_SEM | 0.550 8 | 0.735 8 | 0.676 7 | 0.601 8 | 0.475 8 | 0.794 7 | 0.288 10 | 0.621 9 | 0.378 11 | 0.430 8 | 0.940 7 | 0.303 9 | 0.089 9 | 0.379 9 | 0.580 10 | 0.531 6 | 0.689 8 | 0.422 10 | 0.852 7 | 0.758 7 | 0.468 8 | |
Viewpoint_BN_LA_AIR | 0.548 9 | 0.747 6 | 0.574 12 | 0.631 7 | 0.456 9 | 0.762 10 | 0.355 7 | 0.639 8 | 0.412 8 | 0.404 10 | 0.940 7 | 0.335 8 | 0.107 7 | 0.277 11 | 0.645 4 | 0.495 7 | 0.666 10 | 0.517 8 | 0.818 9 | 0.740 10 | 0.431 10 | |
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck. | ||||||||||||||||||||||
CSC_LA_SEM | 0.531 10 | 0.659 9 | 0.638 9 | 0.578 9 | 0.417 10 | 0.775 9 | 0.254 11 | 0.537 10 | 0.396 9 | 0.439 7 | 0.939 9 | 0.284 11 | 0.083 10 | 0.414 7 | 0.599 9 | 0.488 8 | 0.698 7 | 0.444 9 | 0.785 10 | 0.747 9 | 0.440 9 | |
SQN_LA | 0.486 11 | 0.587 10 | 0.649 8 | 0.527 10 | 0.372 11 | 0.718 11 | 0.320 9 | 0.510 11 | 0.393 10 | 0.325 11 | 0.924 11 | 0.290 10 | 0.095 8 | 0.287 10 | 0.607 8 | 0.356 10 | 0.626 11 | 0.416 11 | 0.672 11 | 0.680 12 | 0.359 11 | |
Scratch_LA_SEM | 0.382 12 | 0.389 12 | 0.606 11 | 0.401 12 | 0.303 12 | 0.705 12 | 0.169 12 | 0.460 12 | 0.292 12 | 0.282 12 | 0.939 9 | 0.207 12 | 0.004 11 | 0.147 12 | 0.201 12 | 0.184 12 | 0.592 12 | 0.389 12 | 0.409 12 | 0.714 11 | 0.250 12 | |