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arXiv · 1904.00320

NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences

Chen Zhao·Zhiguo Cao·Chi Li·Xin Li·Jiaqi Yang
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Abstract

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.

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Chen Zhao, Zhiguo Cao, Chi Li, Xin Li, Jiaqi Yang. 2019-03-31. NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences. https://arxiv.org/abs/1904.00320

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