Self-Supervised Monocular Depth Estimation in Dark Environments: Compensating for Data Distribution Differences

A self-supervised monocular depth estimation framework that does not use any nighttime images during training, but instead compensates for key day-night differences in photometric and noise distributions to enable effective training on daytime images.