  In this paper, we strive for solving the ambiguities arisen by the
astoundingly high density of raw PseudoLiDAR for monocular 3D object detection
for autonomous driving. Without much computational overhead, we propose a
supervised and an unsupervised sparsification scheme of PseudoLiDAR prior to 3D
detection. Both the strategies assist the standard 3D detector gain better
performance over the raw PseudoLiDAR baseline using only ~5% of its points on
the KITTI object detection benchmark, thus making our monocular framework and
LiDAR-based counterparts computationally equivalent (Figure 1). Moreover, our
architecture agnostic refinements provide state-of-the-art results on KITTI3D
test set for "Car" and "Pedestrian" categories with 54% relative improvement
for "Pedestrian". Finally, exploratory analysis is performed on the discrepancy
between monocular and LiDAR-based 3D detection frameworks to guide future
endeavours.
