  End-to-end deep networks represent the state of the art for stereo matching.
While excelling on images framing environments similar to the training set,
major drops in accuracy occur in unseen domains (e.g., when moving from
synthetic to real scenes). In this paper we introduce a novel family of
architectures, namely Matching-Space Networks (MS-Nets), with improved
generalization properties. By replacing learning-based feature extraction from
image RGB values with matching functions and confidence measures from
conventional wisdom, we move the learning process from the color space to the
Matching Space, avoiding over-specialization to domain specific features.
Extensive experimental results on four real datasets highlight that our
proposal leads to superior generalization to unseen environments over
conventional deep architectures, keeping accuracy on the source domain almost
unaltered. Our code is available at https://github.com/ccj5351/MS-Nets.
