Shenyi Li

Learning Depth from Past Selves: Self-Evolution Contrast for Robust Depth Estimation

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