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Local feature matching from detector-based to detector-free: a survey

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Abstract

Local feature matching has been a critical task in computer vision applications. In the early days of computer vision, local feature matching relied heavily on detector-based methods, where keypoint detectors were used to extract and describe the salient features of an image. However, with the advent of deep learning, detector-free methods that do not rely on keypoint detection have become increasingly popular. These methods directly learn feature descriptors from the image data, leading to improved performance and faster computation times. In this review, we explore the evolution of local feature matching from detector-based to detector-free methods, discussing the advantages and disadvantages of each approach and highlighting the recent advancements in the field. We also discuss the challenges and opportunities for future research in this area.

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Data Availability and access

The datasets analyzed during the current study are available from the following public domain resources: http://www.scan-net.org/; http://www.cs.cornell.edu/projects/megadepth/; https://github.com/hpatches; https://data.ciirc.cvut.cz/public/projects/2020VisualLocalization/Aachen-Day-Night/; http://www.ok.sc.e.titech.ac.jp/INLOC/.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 61976124 and 62372077. It is also supported in part by the Scientific Research Fund of Yunnan Provincial Education Department under Grant 2021J0007.

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Authors and Affiliations

  1. National Pilot School of Software, Yunnan University, Kunming, Yunnan Province, China

    Yun Liao, Qing Duan & Junhui Liu

  2. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning Province, China

    Yide Di, Mingyu Lu & Yijia Zhang

  3. Yunnan Lanyi Network Technology Co, Kunming, Yunnan Province, China

    Yun Liao, Yide Di, Kaijun Zhu & Hao Zhou

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  1. Yun Liao
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  2. Yide Di
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  3. Kaijun Zhu
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  4. Hao Zhou
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  5. Mingyu Lu
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  6. Yijia Zhang
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  7. Qing Duan
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  8. Junhui Liu
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Contributions

Yun Liao: Conceptualization and Methodology; Yide Di: Writing; Kaijun Zhu: Validation; Hao Zhou: Software; Mingyu Lu: Supervision; Yijia Zhang: Formal analysis; Qing Duan: Investigation; Junhui Liu: Data Curation.

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Correspondence to Yide Di.

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Liao, Y., Di, Y., Zhu, K. et al. Local feature matching from detector-based to detector-free: a survey. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05330-3

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