Nicholas Milef
ABSTRACT
In this work, we propose a temporally-stable denoising system that is capable of reconstructing MC renderings in a foveated manner. We develop a multi-scale convolutional neural network that starts at a base (downsampled) resolution and denoises progressively higher resolutions. Our network learns to use the lower resolutions and the previous frames to denoise each foveal layer. We demonstrate how this architecture produces accurate denoised results at a much lower computational cost.
Supplemental Material
- Brian Guenter, Mark Finch, Steven Drucker, Desney Tan, and John Snyder. 2012. Foveated 3D graphics. ACM Transactions on Graphics (TOG) 31, 6 (2012), 1–10.Google Scholar
Digital Library
- J Hasselgren, J Munkberg, M Salvi, A Patney, and A Lefohn. 2020. Neural Temporal Adaptive Sampling and Denoising. In Computer Graphics Forum, Vol. 39. Wiley Online Library, 147–155.Google Scholar
- Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, and Jan Novák. 2018. Denoising with Kernel Prediction and Asymmetric Loss Functions. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018) 37, 4, Article 124 (2018), 124:1–124:15 pages. https://doi.org/10.1145/3197517.3201388Google ScholarDigital Library
Index Terms
Foveated Monte-Carlo Denoising
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