We develop a deep architecture to learn to find good correspondences for
wide-baseline stereo. Given a set of putative sparse matches and the camera
intrinsics, we train our network in an end-to-end fashion to label the
correspondences as inliers or outliers, while simultaneously using them to
recover the relative pose, as encoded by the essential matrix. Our architecture
is based on a multi-layer perceptron operating on pixel coordinates rather than
directly on the image, and is thus simple and small. We introduce a novel
normalization technique, called Context Normalization, which allows us to
process each data point separately while imbuing it with global information,
and also makes the network invariant to the order of the correspondences. Our
experiments on multiple challenging datasets demonstrate that our method is
able to drastically improve the state of the art with little training data.

该研究使用多层感知器来优化广基准立体匹配的好匹配点，同时恢复相对位置关系，该网络基于像素坐标运算而非直接图像处理，提出了一种称为上下文规范化的新型正则化技术，使得可以单独处理每个数据点并赋予全局信息。在多个数据集的实验中，该方法使用少量训练数据就能显著提高现有技术的水平。