Continual Stereo Matching: Overcoming Forgetting and Adapting to Heterogeneous Driving Scenes

The core message of this work is to propose a Reusable Architecture Growth (RAG) framework that can continually learn to estimate the disparity of new driving scenes without forgetting previously learned scenes, and adaptively select the scene-specific architecture path at inference to handle rapid scene switches and unseen scenes.