  Monocular 3D object detection is of great significance for autonomous driving
but remains challenging. The core challenge is to predict the distance of
objects in the absence of explicit depth information. Unlike regressing the
distance as a single variable in most existing methods, we propose a novel
geometry-based distance decomposition to recover the distance by its factors.
The decomposition factors the distance of objects into the most representative
and stable variables, i.e. the physical height and the projected visual height
in the image plane. Moreover, the decomposition maintains the self-consistency
between the two heights, leading to robust distance prediction when both
predicted heights are inaccurate. The decomposition also enables us to trace
the causes of the distance uncertainty for different scenarios. Such
decomposition makes the distance prediction interpretable, accurate, and
robust. Our method directly predicts 3D bounding boxes from RGB images with a
compact architecture, making the training and inference simple and efficient.
The experimental results show that our method achieves the state-of-the-art
performance on the monocular 3D Object Detection and Birds Eye View tasks of
the KITTI dataset, and can generalize to images with different camera
intrinsics.
