  Estimating the 3D position and orientation of objects in the environment with
a single RGB camera is a critical and challenging task for low-cost urban
autonomous driving and mobile robots. Most of the existing algorithms are based
on the geometric constraints in 2D-3D correspondence, which stems from generic
6D object pose estimation. We first identify how the ground plane provides
additional clues in depth reasoning in 3D detection in driving scenes. Based on
this observation, we then improve the processing of 3D anchors and introduce a
novel neural network module to fully utilize such application-specific priors
in the framework of deep learning. Finally, we introduce an efficient neural
network embedded with the proposed module for 3D object detection. We further
verify the power of the proposed module with a neural network designed for
monocular depth prediction. The two proposed networks achieve state-of-the-art
performances on the KITTI 3D object detection and depth prediction benchmarks,
respectively. The code will be published in
https://www.github.com/Owen-Liuyuxuan/visualDet3D
