  Depth perception is a crucial component of monoc-ular 3D detection tasks that
typically involve ill-posed problems. In light of the success of sample mining
techniques in 2D object detection, we propose a simple yet effective mining
strategy for improving depth perception in 3D object detection. Concretely, we
introduce a plain metric to evaluate the quality of depth predictions, which
chooses the mined sample for the model. Moreover, we propose a Gradient-aware
and Model-perceive Mining strategy (GMM) for depth learning, which exploits the
predicted depth quality for better depth learning through easy mining. GMM is a
general strategy that can be readily applied to several state-of-the-art
monocular 3D detectors, improving the accuracy of depth prediction. Extensive
experiments on the nuScenes dataset demonstrate that the proposed methods
significantly improve the performance of 3D object detection while
outperforming other state-of-the-art sample mining techniques by a considerable
margin. On the nuScenes benchmark, GMM achieved the state-of-the-art (42.1% mAP
and 47.3% NDS) performance in monocular object detection.
