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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

Arch 4A-E Poster #225
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 aunique 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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