  As a crucial task of autonomous driving, 3D object detection has made great
progress in recent years. However, monocular 3D object detection remains a
challenging problem due to the unsatisfactory performance in depth estimation.
Most existing monocular methods typically directly regress the scene depth
while ignoring important relationships between the depth and various geometric
elements (e.g. bounding box sizes, 3D object dimensions, and object poses). In
this paper, we propose to learn geometry-guided depth estimation with
projective modeling to advance monocular 3D object detection. Specifically, a
principled geometry formula with projective modeling of 2D and 3D depth
predictions in the monocular 3D object detection network is devised. We further
implement and embed the proposed formula to enable geometry-aware deep
representation learning, allowing effective 2D and 3D interactions for boosting
the depth estimation. Moreover, we provide a strong baseline through addressing
substantial misalignment between 2D annotation and projected boxes to ensure
robust learning with the proposed geometric formula. Experiments on the KITTI
dataset show that our method remarkably improves the detection performance of
the state-of-the-art monocular-based method without extra data by 2.80% on the
moderate test setting. The model and code will be released at
https://github.com/YinminZhang/MonoGeo.
