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    We developed an invertible neural BRDF model and Bayesian framework for estimating object reflectance and illumination from a single image. This method enhances inverse rendering accuracy using deep learning priors.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Object inverse rendering aims to recover material properties and lighting from images.
    • Traditional methods often struggle with complex reflectance and illumination scenarios.
    • Deep learning offers new avenues for modeling intricate physical properties.

    Purpose of the Study:

    • To introduce a novel neural network-based Bidirectional Reflectance Distribution Function (BRDF) model.
    • To develop a Bayesian framework for joint estimation of reflectance and illumination.
    • To enable efficient object inverse rendering from a single image.

    Main Methods:

    • Utilized normalizing flow for an invertible neural BRDF model (iBRDF).
    • Extracted a strong reflectance prior by conditioning the iBRDF model.
    • Devised a deep illumination prior leveraging neural network structural biases.
    • Employed Maximum A Posteriori (MAP) estimation with stochastic gradient descent.

    Main Results:

    • Validated the iBRDF model's accuracy on extensive measured data.
    • Demonstrated successful object inverse rendering on synthetic and real images.
    • Showcased efficient joint estimation of reflectance and illumination.

    Conclusions:

    • The invertible neural BRDF model provides expressive, computationally simple, and physically plausible reflectance representations.
    • Deep learning priors significantly improve the accuracy of inverse rendering.
    • This approach offers a powerful new method for solving challenging radiometric inverse problems.