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<p>Local image descriptors are widely used for identifying image features and performing various computer vision tasks, such as object recognition, image matching, and scene reconstruction. However, comparing these descriptors can be time-consuming, especially for large image collections, which can cause real-time or mobile processing challenges. One solution to this problem is using binary descriptors, which can be compared with the help of Hamming distance. Examples of binary descriptors that offer high matching accuracy are RFD and RFDoc, but they can be computationally demanding on edge devices. In this paper, we extend our conference paper on Fast and Accurate RFD-like descriptor Approximation (FARA), which provides CPU-friendly integer-only gradient map computation and sequential memory accesses. We adapt it to use Single Instruction Multiple Data extensions of CPUs at all stages of the descriptor computation pipeline. This approach achieves a twofold acceleration in gradient map computation and reduces the overall descriptor computation time by half compared to the fast RFDoc implementation on x86 and ARM architectures. We also perform descriptor fine-tuning and show that FARA is as accurate as RFDoc in document localization and classification problems on the MIDV-500, MIDV-2019, and MIDV-2020 datasets. FARA also demonstrates comparable accuracy with standard RFD on Brown and Oxford datasets.</p>

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Fast and accurate RFD-like descriptor approximation for SIMD architectures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Artem Sher,
  • Anton Trusov,
  • Mikhail Maksimenko,
  • Elena Limonova,
  • Vladimir L. Arlazarov

摘要

Local image descriptors are widely used for identifying image features and performing various computer vision tasks, such as object recognition, image matching, and scene reconstruction. However, comparing these descriptors can be time-consuming, especially for large image collections, which can cause real-time or mobile processing challenges. One solution to this problem is using binary descriptors, which can be compared with the help of Hamming distance. Examples of binary descriptors that offer high matching accuracy are RFD and RFDoc, but they can be computationally demanding on edge devices. In this paper, we extend our conference paper on Fast and Accurate RFD-like descriptor Approximation (FARA), which provides CPU-friendly integer-only gradient map computation and sequential memory accesses. We adapt it to use Single Instruction Multiple Data extensions of CPUs at all stages of the descriptor computation pipeline. This approach achieves a twofold acceleration in gradient map computation and reduces the overall descriptor computation time by half compared to the fast RFDoc implementation on x86 and ARM architectures. We also perform descriptor fine-tuning and show that FARA is as accurate as RFDoc in document localization and classification problems on the MIDV-500, MIDV-2019, and MIDV-2020 datasets. FARA also demonstrates comparable accuracy with standard RFD on Brown and Oxford datasets.