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.

翻译：深度感知是单目3D检测任务中的关键组成部分，通常涉及病态问题。鉴于样本挖掘技术在2D目标检测中取得的成功，我们提出一种简单而有效的挖掘策略，用于改善3D目标检测中的深度感知。具体而言，我们引入一种简洁的度量标准来评估深度预测质量，该标准为模型选择待挖掘的样本。此外，我们提出一种梯度感知与模型感知挖掘策略（GMM）用于深度学习，通过易挖掘方式利用预测深度质量来优化深度学习。GMM是一种通用策略，可轻松应用于多个先进的单目3D检测器，提升深度预测的准确性。在nuScenes数据集上的大量实验表明，所提方法显著提升了3D目标检测性能，同时以较大优势超越了其他先进的样本挖掘技术。在nuScenes基准测试中，GMM在单目目标检测中取得了当前最佳性能（mAP 42.1%，NDS 47.3%）。