“Denoising and Guided Upsampling of Monte Carlo Path Traced Low Resolution RenderingsContributorsKadir Cenk AlpayAhmet Oguz Akyuz” by Alpay and Akyüz

  • ©Kadir Cenk Alpay and Ahmet Oğuz Akyüz

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Entry Number: 37

Title:

    Denoising and Guided Upsampling of Monte Carlo Path Traced Low Resolution RenderingsContributorsKadir Cenk AlpayAhmet Oguz Akyuz

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Abstract:


    Monte Carlo path tracing generates renderings by estimating the rendering equation using the Monte Carlo method. Studies focus on rendering a noisy image at the original resolution with a low sample per pixel count to decrease the rendering time. Image-space denoising is then applied to produce a visually appealing output. However, denoising process cannot handle the high variance of the noisy image accurately if the sample count is reduced harshly to finish the rendering in a shorter time. We propose a framework that renders the image at a reduced resolution to cast more samples than the harshly lowered sample count in the same time budget. The image is then robustly denoised, and the denoised result is upsampled using original resolution G-buffer of the scene as guidance.

References:


    Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony Derose, and Fabrice Rousselle. 2017. Kernel-predicting convolutional networks for denoising Monte Carlo renderings.ACM Trans. Graph. 36, 4 (2017), 97–1.Google ScholarDigital Library
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