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Spectral reflectance estimation from one RGB image using self-interreflections in a concave object.

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    This study estimates spectral reflectance from a single RGB image by analyzing light interreflections in concave objects. This novel method outperforms existing techniques, even with calibration.

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    Area of Science:

    • Computer Vision
    • Computational Imaging
    • Color Science

    Background:

    • Estimating spectral reflectance is crucial for material recognition and image analysis.
    • Existing methods often require multiple images under varied lighting conditions, limiting practical applications.
    • Concave objects exhibit unique color gradients due to light interreflections.

    Purpose of the Study:

    • To develop a method for estimating spectral reflectance from matte, V-shaped surfaces using a single RGB image.
    • To leverage light interreflections within concave objects for spectral reflectance estimation.
    • To compare the proposed single-image method against multi-image state-of-the-art approaches.

    Main Methods:

    • Utilizing the color gradient generated by light interreflections in a single RGB image.
    • Applying mathematical solutions to the interreflection equation for V-shaped surfaces.
    • Conducting simulations and experiments on real images of folded papers under directional lighting.

    Main Results:

    • Simulations indicate the single-image interreflection method matches or exceeds the performance of three-image methods.
    • Experiments demonstrate superior performance of the interreflection-based approach over existing methods, even with calibration.
    • The study analyzes the mathematical solution and the impact of surface parameters on method performance.

    Conclusions:

    • A single RGB image is sufficient for accurate spectral reflectance estimation of concave surfaces by analyzing interreflections.
    • The proposed interreflection-based method offers a more efficient and effective alternative to current multi-image techniques.
    • This approach advances material characterization from images, particularly for matte, uniformly colored objects.