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

NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences