DASP: Improving Nighttime Depth Estimation Through Self-Supervised Learning and Domain Adaptation

This research paper presents a novel approach to address a challenging computer vision problem: monocular depth estimation in nighttime environments. The use of self-supervised learning and domain adaptation techniques suggests a robust methodology for improving performance in low-light conditions.

• •Addresses the problem of monocular depth estimation specifically for nighttime scenes.
• •Utilizes self-supervised learning, reducing the need for labeled data.
• •Employs domain adaptation to improve performance in challenging lighting conditions.