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Estimating Homogeneous Data-Driven BRDF Parameters From a Reflectance Map Under Known Natural Lighting.

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    This study presents a robust method for estimating Bidirectional Reflectance Distribution Function (BRDF) model parameters from reflectance maps using natural lighting. The approach leverages material class similarities for accurate parameter estimation, enhancing computer graphics rendering.

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    Area of Science:

    • Computer Graphics
    • Material Science
    • Computational Imaging

    Background:

    • Bidirectional Reflectance Distribution Function (BRDF) models are crucial for realistic rendering.
    • Estimating BRDF parameters from reflectance data under natural lighting presents challenges.
    • Leveraging material similarities can regularize parameter estimation.

    Purpose of the Study:

    • To develop a robust method for estimating parameters of a fully-linear, data-driven BRDF model.
    • To utilize reflectance map data under known natural lighting conditions.
    • To improve the accuracy and efficiency of BRDF parameter estimation through regularization.

    Main Methods:

    • Employing a Gaussian mixture model to approximate the space of homogeneous BRDFs.
    • Assigning material classes to each Gaussian component in the mixture model.
    • Formulating parameter estimation as a non-linear maximum a-posteriori optimization with a linear approximation for efficient solutions per material class.

    Main Results:

    • Demonstrated robust estimation of BRDF model parameters.
    • Successfully leveraged reflectance similarities within material classes for regularization.
    • Validated the method using the MERL BRDF database under diverse lighting conditions.

    Conclusions:

    • The proposed method provides robust and accurate BRDF parameter estimation.
    • The approach effectively utilizes material class priors for regularization.
    • The method shows promise for real-world applications in computer graphics and material analysis.