Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods. These methods are categorized into two key segments based on the presence of detectors. The Detector-based category encompasses models inclusive of Detect-then-Describe, Joint Detection and Description, Describe-then-Detect, as well as Graph Based techniques. In contrast, the Detector-free category comprises CNN Based, Transformer Based, and Patch Based methods. Our study extends beyond methodological analysis, incorporating evaluations of prevalent datasets and metrics to facilitate a quantitative comparison of state-of-the-art techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration, underscoring its versatility and significance across various fields. Ultimately, we endeavor to outline the current challenges faced in this domain and furnish future research directions, thereby serving as a reference for researchers involved in local feature matching and its interconnected domains.

翻译：局部特征匹配在计算机视觉领域享有广泛应用，涵盖图像检索、三维重建和物体识别等方向。然而，由于视角和光照变化等因素，提升匹配的准确性与鲁棒性仍面临挑战。近年来，深度学习模型的引入引发了局部特征匹配技术的广泛探索。本文旨在对局部特征匹配方法进行全面综述。根据是否包含检测器，这些方法被分为两大类：基于检测器的类别包括"检测-描述"、"联合检测与描述"、"描述-检测"及基于图的方法；而无检测器类别则包含基于CNN、基于Transformer和基于Patch的方法。本研究不仅局限于方法分析，还整合了主流数据集和评估指标，以量化比较前沿技术。论文进一步探讨了局部特征匹配在运动恢复结构、遥感图像配准和医学图像配准等不同领域的实际应用，凸显其跨领域的通用性和重要性。最后，本文梳理了该领域当前面临的挑战，并提出未来研究方向，为从事局部特征匹配及其相关领域的研究者提供参考。