  Nighttime self-supervised monocular depth estimation has received increasing
attention in recent years. However, using night images for self-supervision is
unreliable because the photometric consistency assumption is usually violated
in the videos taken under complex lighting conditions. Even with domain
adaptation or photometric loss repair, performance is still limited by the poor
supervision of night images on trainable networks. In this paper, we propose a
self-supervised nighttime monocular depth estimation method that does not use
any night images during training. Our framework utilizes day images as a stable
source for self-supervision and applies physical priors (e.g., wave optics,
reflection model and read-shot noise model) to compensate for some key
day-night differences. With day-to-night data distribution compensation, our
framework can be trained in an efficient one-stage self-supervised manner.
Though no nighttime images are considered during training, qualitative and
quantitative results demonstrate that our method achieves SoTA depth estimating
results on the challenging nuScenes-Night and RobotCar-Night compared with
existing methods.
