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.

翻译：图像对之间的特征匹配是计算机视觉中的一个基础问题，驱动着SLAM等众多应用。近年来，半稠密匹配方法取得了显著的性能提升，并建立了广为接受的由粗到精范式。然而，现有方法大多侧重于改进粗粒度特征表示，而非精匹配模块。先前的精匹配技术依赖于点对块匹配概率期望或直接回归，通常缺乏精度，且无法保证特征点在序列图像间的连续性。为解决这一局限，本文聚焦于增强半稠密匹配框架中的精匹配模块。我们采用一个轻量高效的单应性估计网络，来生成由粗匹配获得的图像块之间的透视映射。这种块对块方法实现了两个图像块的整体对齐，通过引入额外的约束获得了更高的亚像素精度。通过利用图像块间的单应性估计，我们能够以较低的计算成本实现稠密匹配结果。大量实验表明，与先前的半稠密匹配器相比，我们的方法达到了更高的精度。同时，我们的稠密匹配结果在保持半稠密效率的同时，展现出与先前稠密匹配器相近的端点误差精度。