  Pseudo-LiDAR-based methods for monocular 3D object detection have generated
large attention in the community due to performance gains showed on the KITTI3D
benchmark dataset, in particular on the commonly reported validation split.
This generated a distorted impression about the superiority of Pseudo-LiDAR
approaches against methods working with RGB-images only. Our first contribution
consists in rectifying this view by analysing and showing experimentally that
the validation results published by Pseudo-LiDAR-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 Pseudo-LiDAR-based methods. Surprisingly, the
bias remains also after geographically removing the overlap, revealing the
presence of a more structured contamination. This leaves the test set as the
only reliable mean of comparison, where published Pseudo-LiDAR-based methods do
not excel. Our second contribution brings Pseudo-LiDAR-based methods back up in
the ranking with the introduction of a 3D confidence prediction module. Thanks
to the proposed architectural changes, our modified Pseudo-LiDAR-based methods
exhibit extraordinary gains on the test scores (up to +8% 3D AP).
