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Sequential fitting-and-separating reflectance components for analytical bidirectional reflectance distribution

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    This study introduces a novel algorithm to separate surface reflectance components and estimate bidirectional reflectance distribution function (BRDF) parameters. The method accurately identifies and models diffuse and specular reflections for improved material analysis.

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    Area of Science:

    • Computer Vision and Graphics
    • Materials Science
    • Computational Imaging

    Background:

    • Accurate surface reflectance modeling is crucial for realistic rendering and material analysis.
    • Existing methods often struggle with complex reflectance distributions involving multiple specular components.
    • The Lafortune bidirectional reflectance distribution function (BRDF) model offers a flexible representation but is challenging to estimate.

    Purpose of the Study:

    • To develop a robust algorithm for separating surface reflectance components.
    • To simultaneously estimate bidirectional reflectance distribution function (BRDF) parameters for analytical models like Lafortune.
    • To infer the optimal number of BRDF lobes and their associated parameters.

    Main Methods:

    • A sequential fitting-and-separating algorithm is proposed.
    • Combines interval analysis-based branch-and-bound with iterative K-ordered scale estimation.
    • Applies to Lafortune, Cook-Torrance, and Ward BRDF models.

    Main Results:

    • The algorithm successfully separates dominant reflectance components.
    • Accurate estimation of BRDF parameters, including the number of lobes, is achieved.
    • Experimental validation using the MERL-MIT BRDF database demonstrates superiority over conventional minimization algorithms.

    Conclusions:

    • The proposed method offers a significant advancement in BRDF parameter estimation.
    • It provides a more accurate and robust approach for analyzing surface reflectance.
    • The algorithm's applicability extends to various analytical BRDF models.