  Gated imaging is an emerging sensor technology for self-driving cars that
provides high-contrast images even under adverse weather influence. It has been
shown that this technology can even generate high-fidelity dense depth maps
with accuracy comparable to scanning LiDAR systems. In this work, we extend the
recent Gated2Depth framework with aleatoric uncertainty providing an additional
confidence measure for the depth estimates. This confidence can help to filter
out uncertain estimations in regions without any illumination. Moreover, we
show that training on dense depth maps generated by LiDAR depth completion
algorithms can further improve the performance.
