Research Output

Adaptive polynomial rendering.

  In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our method locally approximates an image with polynomial functions and the optimal order of each polynomial function is estimated so that our reconstruction error can be minimized. To robustly estimate the optimal order, we propose a multi-stage error estimation process that iteratively estimates our reconstruction error. In addition, we present an energy-preserving outlier removal technique to remove spike noise without causing noticeable energy loss in our reconstruction result. Also, we adaptively allocate additional ray samples to high error regions guided by our error estimation. We demonstrate that our approach outperforms state-of-the-art methods by controlling the tradeoff between reconstruction bias and variance through locally defining our polynomial order, even without need for filtering bandwidth optimization, the common approach of other recent methods.

  • Type:

    Article

  • Date:

    11 July 2016

  • Publication Status:

    Published

  • Publisher

    Association for Computing Machinery (ACM)

  • DOI:

    10.1145/2897824.2925936

  • ISSN:

    0730-0301

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.6 Computer graphics

  • Funders:

    Innovate UK

Citation

Moon, B., McDonagh, S., Mitchell, K., & Gross, M. (2016). Adaptive polynomial rendering. ACM transactions on graphics, 35(4), (1-10). doi:10.1145/2897824.2925936. ISSN 0730-0301

Authors

Keywords

Computer Graphics, Computer-Aided Design,

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