The proposed cumulative homogeneous-heterogeneous adaptation framework (CH2DA-Flow) can be extended to handle other types of adverse weather conditions, such as hail, dust storms, or extreme lighting conditions, by incorporating additional adaptation strategies tailored to the unique characteristics of these weather phenomena. For instance, hail can introduce rapid occlusions and varying luminance, similar to rain, necessitating a dynamic weather adaptation approach that focuses on the additional motion artifacts caused by hail impacts.
For dust storms, which primarily affect visibility and contrast, the method could leverage the depth association homogeneous adaptation to account for the gradual degradation of visual features without introducing significant motion disturbances. This would involve analyzing the impact of dust on optical flow estimation and adjusting the depth association strategy accordingly.
Moreover, the framework could benefit from a modular design that allows for the integration of new weather-specific adaptations. By analyzing the physical and visual effects of each new weather condition on optical flow, researchers can develop targeted adaptations that enhance the robustness of the method across a broader range of adverse weather scenarios. This adaptability is crucial for applications in autonomous driving and surveillance, where varying weather conditions can significantly impact performance.