  Monocular 3D object detection (Mono3D) has achieved tremendous improvements
with emerging large-scale autonomous driving datasets and the rapid development
of deep learning techniques. However, caused by severe domain gaps (e.g., the
field of view (FOV), pixel size, and object size among datasets), Mono3D
detectors have difficulty in generalization, leading to drastic performance
degradation on unseen domains. To solve these issues, we combine the
position-invariant transform and multi-scale training with the pixel-size depth
strategy to construct an effective unified camera-generalized paradigm (CGP).
It fully considers discrepancies in the FOV and pixel size of images captured
by different cameras. Moreover, we further investigate the obstacle in
quantitative metrics when cross-dataset inference through an exhaustive
systematic study. We discern that the size bias of prediction leads to a
colossal failure. Hence, we propose the 2D-3D geometry-consistent object
scaling strategy (GCOS) to bridge the gap via an instance-level augment. Our
method called DGMono3D achieves remarkable performance on all evaluated
datasets and surpasses the SoTA unsupervised domain adaptation scheme even
without utilizing data on the target domain.
