Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose.

翻译：单目深度估计（MDE）旨在生成可用于下游任务的3D信息，例如与自动驾驶车辆（AV）或驾驶员辅助相关的车载感知任务。因此，一个相关且重要的问题是：用于MDE评估的标准指标能否成为未来基于MDE的驾驶感知任务准确性的良好指标。本文针对这一问题展开研究。具体而言，我们将三维点云上的3D目标检测任务作为车载感知的代理任务，使用来自MDE模型的三维点云训练并测试当前最先进的3D目标检测器。我们将目标检测结果的排序与MDE模型的深度估计指标排序进行对比。结论表明，MDE评估指标所产生的模型排序确实能较为合理地反映我们可能预期的3D目标检测结果。在各项指标中，绝对相对误差（abs-rel）似乎是最适合此目的的指标。