Estimating Optical Flow in Adverse Weather Conditions: A Cumulative Homogeneous-Heterogeneous Adaptation Approach

The proposed CH2DA-Flow framework can effectively estimate high-quality optical flows under various adverse weather conditions, including dynamic weather (e.g., rain and snow) and static weather (e.g., fog and veiling), by progressively and explicitly transferring motion knowledge from clean scenes to real degraded domains.