Local feature matching is challenging due to textureless and repetitive patterns. Existing methods focus on using appearance features and global interaction and matching, while the importance of geometry priors in local feature matching has not been fully exploited. Different from these methods, in this paper, we delve into the importance of geometry prior and propose Structured Epipolar Matcher (SEM) for local feature matching, which can leverage the geometric information in an iterative matching way. The proposed model enjoys several merits. First, our proposed Structured Feature Extractor can model the relative positional relationship between pixels and high-confidence anchor points. Second, our proposed Epipolar Attention and Matching can filter out irrelevant areas by utilizing the epipolar constraint. Extensive experimental results on five standard benchmarks demonstrate the superior performance of our SEM compared to state-of-the-art methods. Project page: https://sem2023.github.io.

翻译：局部特征匹配因纹理缺失和重复模式而面临挑战。现有方法主要关注外观特征及全局交互与匹配，但几何先验在局部特征匹配中的重要性尚未被充分发掘。与这些方法不同，本文深入探究了几何先验的重要性，并提出了一种结构化极线匹配器（SEM）用于局部特征匹配，该方法能以迭代匹配的方式利用几何信息。所提出的模型具有多个优点。首先，我们提出的结构化特征提取器能够建模像素与高置信度锚点之间的相对位置关系。其次，我们提出的极线注意力与匹配机制可通过利用极线约束过滤无关区域。在五个标准基准数据集上的广泛实验结果表明，我们的SEM相较于现有最先进方法具有更优越的性能。项目页面：https://sem2023.github.io。