MAM2017: Eurographics Workshop on Material Appearance Modeling

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https://diglib.eg.org/handle/10.2312/2631738

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Browsing MAM2017: Eurographics Workshop on Material Appearance Modeling by Subject "Picture/Image Generation"

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    Challenges in Appearance Capture and Predictive Modeling of Textile Materials
    (The Eurographics Association, 2017) Castillo, Carlos; Aliaga, Carlos; López-Moreno, Jorge; Reinhard Klein and Holly Rushmeier
    The appearance of cloth is the result of complex light interactions within the structures present in textile materials, particularly challenging due to their multi-scale nature. In addition to the inherent complexity of cloth rendering, there is a lack of connection between computer graphics techniques and manufacturing processes followed in industry. We discuss existing techniques and pose questions about which are the right paths to follow for a better synergy between CG and textile research, including (but not restricted to): defining a standard set of properties required to predict the appearance of cloth to be manufactured; developing both acquisition techniques reliable and suitable for industrial processes and other frameworks more focused on inexpensive capturing (e.g. based on single pictures, Pantone labels); finding material representations that are robust in absence of several low-level parameters; creating a standard for color depth depending on the dye type and dying technique; developing a standard to account for post-process steps (washing, chemical treatments, etc) on the mechanical and optical properties of the textiles.
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    Diffraction Prediction in HDR Measurements
    (The Eurographics Association, 2017) Lucat, Antoine; Hegedus, R.; Pacanowski, Romain; Reinhard Klein and Holly Rushmeier
    Modern imaging techniques have proved to be very efficient to recover a scene with high dynamic range values. However, this high dynamic range can introduce star-burst patterns around highlights arising from the diffraction of the camera aperture. The spatial extent of this effect can be very wide and alters pixels values, which, in a measurement context, are not reliable anymore. To address this problem, we introduce a novel algorithm that predicts, from a closed-form PSF, where the diffraction will affect the pixels of an HDR image, making it possible to discard them from the measurement. Our results gives better results than common deconvolution techniques and the uncertainty values (convolution kernel and noise) of the algorithm output are recovered.