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.

本文提出一种名为Matching-Space Networks (MS-Nets)的新型网络结构，通过从RGB值中提取学习特征转而利用Matching Space的匹配函数和置信度测量来实现图像的立体匹配，避免过度专门化到特定领域的特征，达到了更好的泛化性能和准确率表现。

匹配空间立体网络用于跨领域泛化