Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation

arXiv:2404.13854v1 Announce Type: new
Abstract: 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 …

abstract arxiv attention compensation cs.cv data distribution domain domain adaptation however images lighting loss repair supervision type videos