HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation

TL;DR

HomoMatcher introduces a semi-dense feature matching method that employs a lightweight homography estimation network to improve fine-matching accuracy and efficiency, benefiting applications like SLAM.

Contribution

The paper proposes a novel patch-to-patch matching approach using homography estimation to enhance fine-matching precision in semi-dense frameworks.

Findings

Achieves higher matching accuracy than previous semi-dense methods.

Maintains similar end-point-error accuracy as dense matchers.

Operates with low computational cost and high efficiency.

Abstract

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module. Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images. To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework. We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints. By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers. Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.