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Local feature matching using deep learning: A survey

文献类型：期刊论文

作者Xu, Shibiao2; Chen, Shunpeng2; Xu, Rongtao1; Wang, Changwei1; Lu, Peng2; Guo, Li2
刊名INFORMATION FUSION
出版日期2024-07-01
卷号107页码:25
关键词Local feature matching Image matching Deep learning Survey
ISSN号1566-2535
DOI10.1016/j.inffus.2024.102344
通讯作者Xu, Rongtao(xurongtao2019@ia.ac.cn)
英文摘要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.
WOS关键词IMAGE REGISTRATION ; NETWORK ; LOCALIZATION ; FRAMEWORK ; SAR ; NET
资助项目Beijing Natural Science Foundation[JQ23014] ; National Natural Science Foundation of China[62271074]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001201702200001
出版者ELSEVIER
资助机构Beijing Natural Science Foundation ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/57035]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Xu, Rongtao
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Shibiao,Chen, Shunpeng,Xu, Rongtao,et al. Local feature matching using deep learning: A survey[J]. INFORMATION FUSION,2024,107:25.
APA Xu, Shibiao,Chen, Shunpeng,Xu, Rongtao,Wang, Changwei,Lu, Peng,&Guo, Li.(2024).Local feature matching using deep learning: A survey.INFORMATION FUSION,107,25.
MLA Xu, Shibiao,et al."Local feature matching using deep learning: A survey".INFORMATION FUSION 107(2024):25.

入库方式： OAI收割

来源：自动化研究所

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