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Robust Feature Matching via Multi-Scale Grid Structure

Feature matching is a critical prerequisite of many computer vision tasks. This paper focuses on differentiating inliers and outliers from a set of putative feature correspondences to obtain a satisfactory matching result. In order to better exploit the spatial consistency of the true matches, we introduce the multi-scale grid structure into the feature matching problem, reducing the time complexity of neighborhood construction simultaneously. Furthermore, to make the motion statistics more reliable, the multi-neighbor structure is utilized to boost the robustness. Experimental results reveal that our method outperforms other state-of-the-art feature matching methods both in accuracy and efficiency.