  In this work, we propose an efficient and accurate monocular 3D detection
framework in single shot. Most successful 3D detectors take the projection
constraint from the 3D bounding box to the 2D box as an important component.
Four edges of a 2D box provide only four constraints and the performance
deteriorates dramatically with the small error of the 2D detector. Different
from these approaches, our method predicts the nine perspective keypoints of a
3D bounding box in image space, and then utilize the geometric relationship of
3D and 2D perspectives to recover the dimension, location, and orientation in
3D space. In this method, the properties of the object can be predicted stably
even when the estimation of keypoints is very noisy, which enables us to obtain
fast detection speed with a small architecture. Training our method only uses
the 3D properties of the object without the need for external networks or
supervision data. Our method is the first real-time system for monocular image
3D detection while achieves state-of-the-art performance on the KITTI
benchmark. Code will be released at https://github.com/Banconxuan/RTM3D.
