  Monocular 3D object detection is an essential task in autonomous driving.
However, most current methods consider each 3D object in the scene as an
independent training sample, while ignoring their inherent geometric relations,
thus inevitably resulting in a lack of leveraging spatial constraints. In this
paper, we propose a novel method that takes all the objects into consideration
and explores their mutual relationships to help better estimate the 3D boxes.
Moreover, since 2D detection is more reliable currently, we also investigate
how to use the detected 2D boxes as guidance to globally constrain the
optimization of the corresponding predicted 3D boxes. To this end, a
differentiable loss function, termed as Homography Loss, is proposed to achieve
the goal, which exploits both 2D and 3D information, aiming at balancing the
positional relationships between different objects by global constraints, so as
to obtain more accurately predicted 3D boxes. Thanks to the concise design, our
loss function is universal and can be plugged into any mature monocular 3D
detector, while significantly boosting the performance over their baseline.
Experiments demonstrate that our method yields the best performance (Nov. 2021)
compared with the other state-of-the-arts by a large margin on KITTI 3D
datasets.
