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    • 40-Issue 4
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    Deep Compositional Denoising for High-quality Monte Carlo Rendering

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    Date
    2021
    Author
    Zhang, Xianyao ORCID
    Manzi, Marco
    Vogels, Thijs ORCID
    Dahlberg, Henrik
    Gross, Markus
    Papas, Marios
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    Abstract
    We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernelpredicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and corresponding feature maps, which are consecutively reconstructed by a denoising module. The components are predicted based on statistics aggregated at the pixel level by the renderer. Denoising these components individually allows the use of per-component kernels that adapt to each component's noisy signal characteristics. Experimentally, we show that the proposed decomposition module consistently improves the denoising quality of current state-of-the-art kernel-predicting denoisers on large-scale academic and production datasets.
    BibTeX
    @article {10.1111:cgf.14337,
    journal = {Computer Graphics Forum},
    title = {{Deep Compositional Denoising for High-quality Monte Carlo Rendering}},
    author = {Zhang, Xianyao and Manzi, Marco and Vogels, Thijs and Dahlberg, Henrik and Gross, Markus and Papas, Marios},
    year = {2021},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14337}
    }
    URI
    https://doi.org/10.1111/cgf.14337
    https://diglib.eg.org:443/handle/10.1111/cgf14337
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    Eurographics Association copyright © 2013 - 2023 
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