  Recently, three-dimensional (3D) detection based on stereo images has
progressed remarkably; however, most advanced methods adopt anchor-based
two-dimensional (2D) detection or depth estimation to address this problem.
Nevertheless, high computational cost inhibits these methods from achieving
real-time performance. In this study, we propose a 3D object detection method,
Stereo CenterNet (SC), using geometric information in stereo imagery. SC
predicts the four semantic key points of the 3D bounding box of the object in
space and utilizes 2D left and right boxes, 3D dimension, orientation, and key
points to restore the bounding box of the object in the 3D space. Subsequently,
we adopt an improved photometric alignment module to further optimize the
position of the 3D bounding box. Experiments conducted on the KITTI dataset
indicate that the proposed SC exhibits the best speed-accuracy trade-off among
advanced methods without using extra data.
