High-Resolution Gated Depth Estimation for Self-Driving Cars in AdverseWeather

Gated imaging has become a promising technology for self-driving cars under adverse weather conditions because this technology is able to suppress backscatter efficiently. Moreover, gated images do not only provide intensity images but can also generate perfectly aligned depth maps. Recently, a benchmark for depth estimation in adverse weather conditions has been recorded in a fogchamber with limited length. To evaluate and benchmark gated depth estimation in these conditions, we propose a novel short-range gating scheme that is adapted to the fogchamber range. We show that gated depth estimation performs significantly more stable in adverse weather conditions compared to other stateof-the-art 3D sensing methods such as monocular depth estimation, stereo vision, and LiDAR depth completion.