A Permutation Model for the Self-Supervised Stereo Matching Problem

This paper proposes a novel permutation formulation to the stereo matching problem. Our proposed approach introduces a permutation volume which provides a natural representation of stereo constraints and disentangles stereo matching from monocular disparity estimation. It also has the benefit of simultaneously computing disparity and a confidence measure which provides explainability and a simple confidence heuristic for occlusions. In the context of self-supervised learning, the stereo performance is validated for standard testing datasets and the confidence maps are validated through stereo-visibility. Results show that the permutation volume increases stereo performance and features good generalization behaviour. We believe that measuring confidence is a key part of explainability which is instrumental to adoption of deep methods in critical stereo applications such as autonomous navigation.