  Monocular depth estimation aims at predicting depth from a single image or
video. Recently, self-supervised methods draw much attention since they are
free of depth annotations and achieve impressive performance on several daytime
benchmarks. However, they produce weird outputs in more challenging nighttime
scenarios because of low visibility and varying illuminations, which bring weak
textures and break brightness-consistency assumption, respectively. To address
these problems, in this paper we propose a novel framework with several
improvements: (1) we introduce Priors-Based Regularization to learn
distribution knowledge from unpaired depth maps and prevent model from being
incorrectly trained; (2) we leverage Mapping-Consistent Image Enhancement
module to enhance image visibility and contrast while maintaining brightness
consistency; and (3) we present Statistics-Based Mask strategy to tune the
number of removed pixels within textureless regions, using dynamic statistics.
Experimental results demonstrate the effectiveness of each component.
Meanwhile, our framework achieves remarkable improvements and state-of-the-art
results on two nighttime datasets.

Computer Vision and Pattern Recognition