The KITTI Vision Benchmark Suite

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.

Backbone：DLA34

@article{SHI2022219,
title = {Stereo CenterNet-based 3D object
detection for autonomous driving},
journal = {Neurocomputing},
volume = {471},
pages = {219-229},
year = {2022},
issn = {0925-2312},
doi =
{https://doi.org/10.1016/j.neucom.2021.11.048},
url =
{https://www.sciencedirect.com/science/article/pii/S
0925231221017264},
author = {Yuguang Shi and Yu Guo and
Zhenqiang Mi and Xinjie Li},

}