Kadir Cenk Alpay
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.
Supplemental Material
Available for Download
- 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 Scholar
Digital Library
- Benedikt Bitterli. 2016. Rendering resources. https://benedikt-bitterli.me/resources/.Google Scholar
- Yuchi Huo and Sung-eui Yoon. 2021. A survey on deep learning-based Monte Carlo denoising. Computational Visual Media 7, 2 (2021), 169–185.Google ScholarCross Ref
- James T Kajiya. 1986. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques. 143–150.Google ScholarDigital Library
- Matthias Zwicker, Wojciech Jarosz, Jaakko Lehtinen, Bochang Moon, Ravi Ramamoorthi, Fabrice Rousselle, Pradeep Sen, Cyril Soler, and S-E Yoon. 2015. Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. In Computer graphics forum, Vol. 34. Wiley Online Library, 667–681.Google Scholar
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