  Monocular 3D object detection is an essential component in autonomous driving
while challenging to solve, especially for those occluded samples which are
only partially visible. Most detectors consider each 3D object as an
independent training target, inevitably resulting in a lack of useful
information for occluded samples. To this end, we propose a novel method to
improve the monocular 3D object detection by considering the relationship of
paired samples. This allows us to encode spatial constraints for
partially-occluded objects from their adjacent neighbors. Specifically, the
proposed detector computes uncertainty-aware predictions for object locations
and 3D distances for the adjacent object pairs, which are subsequently jointly
optimized by nonlinear least squares. Finally, the one-stage uncertainty-aware
prediction structure and the post-optimization module are dedicatedly
integrated for ensuring the run-time efficiency. Experiments demonstrate that
our method yields the best performance on KITTI 3D detection benchmark, by
outperforming state-of-the-art competitors by wide margins, especially for the
hard samples.
