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Award ID contains: 1909028

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  1. Abstract

    We present a deep learning based solution for separating the direct and global light transport components from a single photograph captured under high frequency structured lighting with a co‐axial projector‐camera setup. We employ an architecture with one encoder and two decoders that shares information between the encoder and the decoders, as well as between both decoders to ensure a consistent decomposition between both light transport components. Furthermore, our deep learning separation approach does not require binary structured illumination, allowing us to utilize the full resolution capabilities of the projector. Consequently, our deep separation network is able to achieve high fidelity decompositions for lighting frequency sensitive features such as subsurface scattering and specular reflections. We evaluate and demonstrate our direct and global separation method on a wide variety of synthetic and captured scenes.

     
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  2. Abstract

    We propose a novel image‐driven fitting strategy for isotropic BRDFs. Whereas existing BRDF fitting methods minimize a cost function directly on the error between the fitted analytical BRDF and the measured isotropic BRDF samples, we also take into account the resulting material appearance in visualizations of the BRDF. This change of fitting paradigm improves the appearance reproduction fidelity, especially for analytical BRDF models that lack the expressiveness to reproduce the measured surface reflectance. We formulate BRDF fitting as a two‐stage process that first generates a series of candidate BRDF fits based only on the BRDF error with measured BRDF samples. Next, from these candidates, we select the BRDF fit that minimizes the visual error. We demonstrate qualitatively and quantitatively improved fits for the Cook‐Torrance and GGX microfacet BRDF models. Furthermore, we present an analysis of the BRDF fitting results, and show that the image‐driven isotropic BRDF fits generalize well to other light conditions, and that depending on the measured material, a different weighting of errors with respect to the measured BRDF is necessary.

     
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  3. Free, publicly-accessible full text available June 27, 2024
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