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    • 42-Issue 4
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    Accelerating Hair Rendering by Learning High-Order Scattered Radiance

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    Date
    2023
    Author
    KT, Aakash
    Jarabo, Adrian
    Aliaga, Carlos
    Chiang, Matt Jen-Yuan
    Maury, Olivier
    Hery, Christophe
    Narayanan, P. J.
    Nam, Giljoo
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    Abstract
    Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. Path tracing in hair takes long to converge while other techniques are either too approximate while still being computationally expensive or make assumptions about the scene. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency. Our method makes no assumptions about the scene and provides control over the renderer's bias & speedup. We achieve this by training a small multilayer perceptron (MLP) to learn the higher-order radiance online, while rendering progresses. We describe how to robustly train this network and thoroughly analyze our resulting renderer's characteristics. We evaluate our method on various hairstyles and lighting conditions. We also compare our method against a recent learning based & a traditional real-time hair rendering method and demonstrate better quantitative & qualitative results. Our method achieves a significant improvement in speed with respect to path tracing, achieving a run-time reduction of 40%-70% while only introducing a small amount of bias.
    BibTeX
    @article {10.1111:cgf.14895,
    journal = {Computer Graphics Forum},
    title = {{Accelerating Hair Rendering by Learning High-Order Scattered Radiance}},
    author = {KT, Aakash and Jarabo, Adrian and Aliaga, Carlos and Chiang, Matt Jen-Yuan and Maury, Olivier and Hery, Christophe and Narayanan, P. J. and Nam, Giljoo},
    year = {2023},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14895}
    }
    URI
    https://doi.org/10.1111/cgf.14895
    https://diglib.eg.org:443/handle/10.1111/cgf14895
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA