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    • 38-Issue 4
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    Flexible SVBRDF Capture with a Multi-Image Deep Network

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    Date
    2019
    Author
    Deschaintre, Valentin
    Aittala, Miika
    Durand, Fredo
    Drettakis, George
    Bousseau, Adrien
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    Abstract
    Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of realworld materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images - a sweet spot between existing single-image and complex multi-image approaches.
    BibTeX
    @article {10.1111:cgf.13765,
    journal = {Computer Graphics Forum},
    title = {{Flexible SVBRDF Capture with a Multi-Image Deep Network}},
    author = {Deschaintre, Valentin and Aittala, Miika and Durand, Fredo and Drettakis, George and Bousseau, Adrien},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13765}
    }
    URI
    https://doi.org/10.1111/cgf.13765
    https://diglib.eg.org:443/handle/10.1111/cgf13765
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