LIM: Lightweight Image Local Feature Matching
Image matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/5/164 |
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| _version_ | 1850257813047083008 |
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| author | Shanquan Ying Jianfeng Zhao Guannan Li Junjie Dai |
| author_facet | Shanquan Ying Jianfeng Zhao Guannan Li Junjie Dai |
| author_sort | Shanquan Ying |
| collection | DOAJ |
| description | Image matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational demands hinder practical deployment on resource-constrained devices, such as mobile and embedded platforms. To address this challenge, we propose LIM, a lightweight image local feature matching network designed for computationally constrained embedded systems. LIM integrates efficient feature extraction and matching modules that significantly reduce model complexity while maintaining competitive performance. Our design emphasizes robustness to extreme viewpoint and rotational variations, making it suitable for real-world deployment scenarios. Extensive experiments on multiple benchmarks demonstrate that LIM achieves a favorable trade-off between speed and accuracy, running more than 3× faster than existing deep matching methods, while preserving high-quality matching results. These characteristics position LIM as an effective solution for real-time applications in power-limited environments. |
| format | Article |
| id | doaj-art-4a4bef4bf8bd4dceac99236322a623fd |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-4a4bef4bf8bd4dceac99236322a623fd2025-08-20T01:56:19ZengMDPI AGJournal of Imaging2313-433X2025-05-0111516410.3390/jimaging11050164LIM: Lightweight Image Local Feature MatchingShanquan Ying0Jianfeng Zhao1Guannan Li2Junjie Dai3College of Science and Technology, Ningbo University, Ningbo 315212, ChinaCollege of Science and Technology, Ningbo University, Ningbo 315212, ChinaHuzhou Institute of Zhejiang University, Huzhou 313000, ChinaCollege of Science and Technology, Ningbo University, Ningbo 315212, ChinaImage matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational demands hinder practical deployment on resource-constrained devices, such as mobile and embedded platforms. To address this challenge, we propose LIM, a lightweight image local feature matching network designed for computationally constrained embedded systems. LIM integrates efficient feature extraction and matching modules that significantly reduce model complexity while maintaining competitive performance. Our design emphasizes robustness to extreme viewpoint and rotational variations, making it suitable for real-world deployment scenarios. Extensive experiments on multiple benchmarks demonstrate that LIM achieves a favorable trade-off between speed and accuracy, running more than 3× faster than existing deep matching methods, while preserving high-quality matching results. These characteristics position LIM as an effective solution for real-time applications in power-limited environments.https://www.mdpi.com/2313-433X/11/5/164lightweight networksimage feature matchingdeep learningembedded systemsrotation robustness |
| spellingShingle | Shanquan Ying Jianfeng Zhao Guannan Li Junjie Dai LIM: Lightweight Image Local Feature Matching Journal of Imaging lightweight networks image feature matching deep learning embedded systems rotation robustness |
| title | LIM: Lightweight Image Local Feature Matching |
| title_full | LIM: Lightweight Image Local Feature Matching |
| title_fullStr | LIM: Lightweight Image Local Feature Matching |
| title_full_unstemmed | LIM: Lightweight Image Local Feature Matching |
| title_short | LIM: Lightweight Image Local Feature Matching |
| title_sort | lim lightweight image local feature matching |
| topic | lightweight networks image feature matching deep learning embedded systems rotation robustness |
| url | https://www.mdpi.com/2313-433X/11/5/164 |
| work_keys_str_mv | AT shanquanying limlightweightimagelocalfeaturematching AT jianfengzhao limlightweightimagelocalfeaturematching AT guannanli limlightweightimagelocalfeaturematching AT junjiedai limlightweightimagelocalfeaturematching |