Feature correspondence selection is pivotal to many feature-matching based
tasks in computer vision. Searching for spatially k-nearest neighbors is a
common strategy for extracting local information in many previous works.
However, there is no guarantee that the spatially k-nearest neighbors of
correspondences are consistent because the spatial distribution of false
correspondences is often irregular. To address this issue, we present a
compatibility-specific mining method to search for consistent neighbors.
Moreover, in order to extract and aggregate more reliable features from
neighbors, we propose a hierarchical network named NM-Net with a series of
convolution layers taking the generated graph as input, which is insensitive to
the order of correspondences. Our experimental results have shown the proposed
method achieves the state-of-the-art performance on four datasets with various
inlier ratios and varying numbers of feature consistencies.

该研究提出了一种兼容性特定的挖掘方法来搜索一致的邻居，在提取并聚合更可靠的特征方面，使用了一种具有层次结构的神经网络 NM-Net。