Learning to Adapt for Stereo
This addresses robustness issues for practical applications like autonomous driving, but is incremental as it builds on existing deep stereo methods.
The paper tackles the problem of stereo depth estimation models failing to generalize to unseen environmental variations, and introduces a learning-to-adapt framework that enables unsupervised online adaptation, showing benefits in experiments on synthetic and real-world datasets.
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical applications such as autonomous driving. In this work, we introduce a "learning-to-adapt" framework that enables deep stereo methods to continuously adapt to new target domains in an unsupervised manner. Specifically, our approach incorporates the adaptation procedure into the learning objective to obtain a base set of parameters that are better suited for unsupervised online adaptation. To further improve the quality of the adaptation, we learn a confidence measure that effectively masks the errors introduced during the unsupervised adaptation. We evaluate our method on synthetic and real-world stereo datasets and our experiments evidence that learning-to-adapt is, indeed beneficial for online adaptation on vastly different domains.