Research Output

Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering

  We propose a novel pre-filtering method that reduces the noise introduced by depth-of-field and motion blur effects in geometric
buffers (G-buffers) such as texture, normal and depth images. Our pre-filtering uses world positions and their variances to
effectively remove high-frequency noise while carefully preserving high-frequency edges in the G-buffers. We design a new
anisotropic filter based on a per-pixel covariance matrix of world position samples. A general error estimator, Stein’s unbiased
risk estimator, is then applied to estimate the optimal trade-off between the bias and variance of pre-filtered results. We have
demonstrated that our pre-filtering improves the results of existing filtering methods numerically and visually for challenging
scenes where depth-of-field and motion blurring introduce a significant amount of noise in the G-buffers.

  • Type:

    Article

  • Date:

    23 May 2017

  • Publication Status:

    Published

  • Publisher

    Wiley-Blackwell

  • DOI:

    10.1111/cgf.13155

  • ISSN:

    0167-7055

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.6 Computer graphics

  • Funders:

    Innovate UK

Citation

Moon, B., Iglesias-Guitian, J. A., McDonagh, S., & Mitchell, K. (2017). Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering. Computer Graphics Forum, 36(8), 600-612. https://doi.org/10.1111/cgf.13155

Authors

Keywords

Image filtering, denoising, Monte Carlo ray tracing,

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