Denoising and Guided Upsampling of Monte Carlo Path Traced Low Resolution Renderings

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2022-07-27

Author

Alpay, Kadir Cenk
Akyüz, Ahmet Oğuz

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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.

Subject Keywords

denoising, guided upsampling, Monte Carlo rendering, neural nets

URI

https://hdl.handle.net/11511/99644

DOI

https://doi.org/10.1145/3532719.3543250

Conference Name

Special Interest Group on Computer Graphics and Interactive Techniques Conference - Posters, SIGGRAPH 2022

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

K. C. Alpay and A. O. Akyüz, “Denoising and Guided Upsampling of Monte Carlo Path Traced Low Resolution Renderings,” presented at the Special Interest Group on Computer Graphics and Interactive Techniques Conference - Posters, SIGGRAPH 2022, Vancouver, Kanada, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99644.