  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.
