MAM2018: Eurographics Workshop on Material Appearance Modeling

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https://diglib.eg.org/handle/10.2312/2632655

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Browsing MAM2018: Eurographics Workshop on Material Appearance Modeling by Subject "Line and curve generation"

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    Deep Dual Loss BRDF Parameter Estimation
    (The Eurographics Association, 2018) Boss, Mark; Groh, Fabian; Herholz, Sebastian; Lensch, Hendrik P. A.; Reinhard Klein and Holly Rushmeier
    Surface parameter estimation is an essential field in computer games and movies. An exact representation of a real-world surface allows for a higher degree of realism. Capturing or artistically creating these materials is a time-consuming process. We propose a method which utilizes an encoder-decoder Convolutional Neural Network (CNN) to extract parameters for the Bidirectional Reflectance Distribution Function (BRDF) automatically from a sparse sample set. This is done by implementing a differentiable renderer, which allows for a loss backpropagation of rendered images. This photometric loss is essential because defining a numerical BRDF distance metric is difficult. A second loss is added, which compares the parameters maps directly. Therefore, the statistical properties of the BRDF model are learned, which reduces artifacts in the predicted parameters. This dual loss principal improves the result of the network significantly. Opposed to previous means this method retrieves information of the whole surface as spatially varying BRDF (SVBRDF) parameters with a sufficiently high resolution for intended real-world usage. The capture process for materials only requires five known light positions with a fixed camera position. This reduces the scanning time drastically, and a material sample can be obtained in seconds with an automated system.
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    Image-based Fitting of Procedural Yarn Models
    (The Eurographics Association, 2018) Saalfeld, Alina; Reibold, Florian; Dachsbacher, Carsten; Reinhard Klein and Holly Rushmeier
    While common in real life, rendering fiber and cloth accurately is challenging. Recent fiber-based, procedural rendering approaches proved to be able to capture a great amount of details of real yarn. However, the current automatic method of fitting the model parameters is expensive and inaccessible as it relies on micro CT scans of the reference yarn. The alternative is to have an artist fit the parameters by hand, which is impractical because of the large number of parameters. We present a proof-of-concept for a purely image-based approach to fit the parameters of a procedural yarn model. Using gradient descent and pixel-based loss functions, we are able to extract a subset of the model parameters from rendered images with known parameters. The appearance of the fitted models is nearly indistinguishable from the reference images.