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    • 40-Issue 4
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    Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering

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    Date
    2021
    Author
    Luan, Fujun
    Zhao, Shuang ORCID
    Bala, Kavita
    Dong, Zhao
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    Abstract
    Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing inverse rendering problem. In this paper, we introduce a new analysis-by-synthesis technique capable of producing high-quality reconstructions through robust coarse-to-fine optimization and physics-based differentiable rendering. Unlike most previous methods that handle geometry and reflectance largely separately, our method unifies the optimization of both by leveraging image gradients with respect to both object reflectance and geometry. To obtain physically accurate gradient estimates, we develop a new GPU-based Monte Carlo differentiable renderer leveraging recent advances in differentiable rendering theory to offer unbiased gradients while enjoying better performance than existing tools like PyTorch3D [RRN*20] and redner [LADL18]. To further improve robustness, we utilize several shape and material priors as well as a coarse-to-fine optimization strategy to reconstruct geometry. Using both synthetic and real input images, we demonstrate that our technique can produce reconstructions with higher quality than previous methods.
    BibTeX
    @article {10.1111:cgf.14344,
    journal = {Computer Graphics Forum},
    title = {{Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering}},
    author = {Luan, Fujun and Zhao, Shuang and Bala, Kavita and Dong, Zhao},
    year = {2021},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14344}
    }
    URI
    https://doi.org/10.1111/cgf.14344
    https://diglib.eg.org:443/handle/10.1111/cgf14344
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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.
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