  Pseudo-LiDAR-based methods for monocular 3D object detection have received
considerable attention in the community due to the performance gains exhibited
on the KITTI3D benchmark, in particular on the commonly reported validation
split. This generated a distorted impression about the superiority of
Pseudo-LiDAR-based (PL-based) approaches over methods working with RGB images
only. Our first contribution consists in rectifying this view by pointing out
and showing experimentally that the validation results published by PL-based
methods are substantially biased. The source of the bias resides in an overlap
between the KITTI3D object detection validation set and the training/validation
sets used to train depth predictors feeding PL-based methods. Surprisingly, the
bias remains also after geographically removing the overlap. This leaves the
test set as the only reliable set for comparison, where published PL-based
methods do not excel. Our second contribution brings PL-based methods back up
in the ranking with the design of a novel deep architecture which introduces a
3D confidence prediction module. We show that 3D confidence estimation
techniques derived from RGB-only 3D detection approaches can be successfully
integrated into our framework and, more importantly, that improved performance
can be obtained with a newly designed 3D confidence measure, leading to
state-of-the-art performance on the KITTI3D benchmark.
