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. A comprehensive list of studies in this survey is available at .

中文翻译：

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使用深度学习进行局部特征匹配：一项调查

局部特征匹配在计算机视觉领域有着广泛的应用，涵盖图像检索、3D 重建和对象识别等领域。然而，由于视点和照明变化等因素，提高匹配的准确性和鲁棒性仍然存在挑战。近年来，深度学习模型的引入引发了对局部特征匹配技术的广泛探索。这项工作的目标是提供局部特征匹配方法的全面概述。根据检测器的存在，这些方法分为两个关键部分。基于检测器的类别涵盖的模型包括检测然后描述、联合检测和描述、描述然后检测以及基于图的技术。相比之下，无检测器类别包括基于 CNN、基于变换器和基于补丁的方法。我们的研究超越了方法论分析，结合了对流行数据集和指标的评估，以促进最先进技术的定量比较。本文还探讨了局部特征匹配在运动结构、遥感图像配准和医学图像配准等不同领域的实际应用，强调了其在各个领域的多功能性和重要性。最终，我们努力概述该领域当前面临的挑战并提供未来的研究方向，从而为涉及局部特征匹配及其互连领域的研究人员提供参考。本次调查的完整研究列表可在 上找到。