“Denoising Your Monte Carlo Renders: Recent Advances in Image-Space Adaptive Sampling and Reconstruction” by Sen, Zwicker, Rousselle, Yoon and Kalantari

  • ©Pradeep Sen, Matthias Zwicker, Fabrice Rousselle, Sung-Eui Yoon, and Nima Khademi Kalantari

Conference:


Entry Number: 10

Title:

    Denoising Your Monte Carlo Renders: Recent Advances in Image-Space Adaptive Sampling and Reconstruction

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Abstract:


    Prerequisites
    Familiarity with rendering and basic concepts of Monte Carlo integration as implemented in modern rendering systems.

    Who Should Attend
    Industry professionals interested in recent advances in adaptive sampling and reconstruction for reducing the noise of Monte Carlo rendering. Researchers interested in open research challenges and opportunities for future work.  

    Description
    With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines.

    This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.  


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