• Login
    View Item 
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • Material Appearance Modeling
    • MAM2018: Eurographics Workshop on Material Appearance Modeling
    • View Item
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • Material Appearance Modeling
    • MAM2018: Eurographics Workshop on Material Appearance Modeling
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Deep Dual Loss BRDF Parameter Estimation

    Thumbnail
    View/Open
    041-044.pdf (3.660Mb)
    Date
    2018
    Author
    Boss, Mark
    Groh, Fabian
    Herholz, Sebastian
    Lensch, Hendrik P. A.
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    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.
    BibTeX
    @inproceedings {m.20181199,
    booktitle = {Workshop on Material Appearance Modeling},
    editor = {Reinhard Klein and Holly Rushmeier},
    title = {{Deep Dual Loss BRDF Parameter Estimation}},
    author = {Boss, Mark and Groh, Fabian and Herholz, Sebastian and Lensch, Hendrik P. A.},
    year = {2018},
    publisher = {The Eurographics Association},
    ISSN = {2309-5059},
    ISBN = {978-3-03868-055-0},
    DOI = {10.2312/mam.20181199}
    }
    URI
    https://doi.org/10.2312/mam.20181199
    https://diglib.eg.org:443/handle/10.2312/mam20181199
    Collections
    • MAM2018: Eurographics Workshop on Material Appearance Modeling

    Eurographics Association copyright © 2013 - 2021 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2021 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA