  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. 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 BEV space, specifically for 3D
attributes. Additionally, we present a DepthGradient 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.
