  This paper investigates the geometric consistency for monocular 3D object
detection, which suffers from the ill-posed depth estimation. We first conduct
a thorough analysis to reveal how existing methods fail to consistently
localize objects when different geometric shifts occur. In particular, we
design a series of geometric manipulations to diagnose existing detectors and
then illustrate their vulnerability to consistently associate the depth with
object apparent sizes and positions. To alleviate this issue, we propose four
geometry-aware data augmentation approaches to enhance the geometric
consistency of the detectors. We first modify some commonly used data
augmentation methods for 2D images so that they can maintain geometric
consistency in 3D spaces. We demonstrate such modifications are important. In
addition, we propose a 3D-specific image perturbation method that employs the
camera movement. During the augmentation process, the camera system with the
corresponding image is manipulated, while the geometric visual cues for depth
recovery are preserved. We show that by using the geometric consistency
constraints, the proposed augmentation techniques lead to improvements on the
KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art
results. In addition, we demonstrate that the augmentation methods are well
suited for semi-supervised training and cross-dataset generalization.
