2026
Learning Depth from Past Selves: Self-Evolution Contrast for Robust Depth Estimation
AAAI 2026technical
Self-supervised depth estimation has gained significant attention in autonomous driving and robotics. However, existing methods exhibit substantial performance degradation under adverse weather conditions such as rain and fog, where reduced visibility critically impairs depth prediction. To address