  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
https://github.com/vignywang/Awesome-Local-Feature-Matching .
