Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection

Jiacheng Zhang, Jiaming Li, Xiangru Lin, Wei Zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16923-16932

Abstract


We delve into pseudo-labeling for semi-supervised monocular 3D object detection (SSM3OD) and discover two primary issues: a misalignment between the prediction quality of 3D and 2D attributes and the tendency of depth supervision derived from pseudo-labels to be noisy leading to significant optimization conflicts with other reliable forms of supervision. To tackle these issues we introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD. Our approach features a Decoupled Pseudo-label Generation (DPG) module designed to efficiently generate pseudo-labels by separately processing 2D and 3D attributes. This module incorporates a unique homography-based method for identifying dependable pseudo-labels in Bird's Eye View (BEV) space specifically for 3D attributes. Additionally we present a Depth Gradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels effectively decoupling the depth gradient and removing conflicting gradients. This dual decoupling strategy--at both the pseudo-label generation and gradient levels--significantly improves the utilization of pseudo-labels in SSM3OD. Our comprehensive experiments on the KITTI benchmark demonstrate the superiority of our method over existing approaches.

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[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Zhang_2024_CVPR, author = {Zhang, Jiacheng and Li, Jiaming and Lin, Xiangru and Zhang, Wei and Tan, Xiao and Han, Junyu and Ding, Errui and Wang, Jingdong and Li, Guanbin}, title = {Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {16923-16932} }