Abstract
Collecting higher-quality three-dimensional points-cloud data in various scenarios practically and robustly has led to a strong demand for such dToF-based LiDAR systems with higher ambient noise rejection ability and limited optical power consumption, which is a sharp conflict. To alleviate such a clash, an idea of utilizing a strong ambient noise rejection ability of intensity and RGB images is proposed, based on which a lightweight CNN is newly, to the best of our knowledge, designed, achieving a state-of-the-art performance even with 90 × less inference time and 480 × fewer FLOPs. With such net deployed on edge devices, a complete AI-LiDAR system is presented, showing a 100 × fewer signal photon demand in simulation experiments when creating depth images of the same quality.
© 2023 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Miao Sun, Yifan Wu, Lebei Cui, Hengwei Yu, Jie Li, Jian Qian, Jier Wang, Lei Qiu, Patrick Yin Chiang, and Shenglong Zhuo
Opt. Lett. 48(13) 3415-3418 (2023)
Zhanghao Sun, David B. Lindell, Olav Solgaard, and Gordon Wetzstein
Opt. Express 28(10) 14948-14962 (2020)
Zhenya Zang, Dong Xiao, and David Day-Uei Li
Opt. Express 29(13) 19278-19291 (2021)