Factored axis-aligned filtering for rendering multiple distribution effects
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Durand_Factored axis.pdf
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13.54 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Mehta, Soham Uday
Yao, JiaXian
Ramamoorthi, Ravi
Durand, Fredo
Date Issued
July 2014
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Soham Uday Mehta, JiaXian Yao, Ravi Ramamoorthi, and Fredo Durand. 2014. Factored axis-aligned filtering for rendering multiple distribution effects. ACM Trans. Graph. 33, 4, Article 57 (July 2014), 12 pages.
Version
Author's final manuscript
Abstract
Monte Carlo (MC) ray-tracing for photo-realistic rendering often requires hours to render a single image due to the large sampling rates needed for convergence. Previous methods have attempted to filter sparsely sampled MC renders but these methods have high reconstruction overheads. Recent work has shown fast performance for individual effects, like soft shadows and indirect illumination, using axis-aligned filtering. While some components of light transport such as indirect or area illumination are smooth, they are often multiplied by high-frequency components such as texture, which prevents their sparse sampling and reconstruction.
We propose an approach to adaptively sample and filter for simultaneously rendering primary (defocus blur) and secondary (soft shadows and indirect illumination) distribution effects, based on a multi-dimensional frequency analysis of the direct and indirect illumination light fields. We describe a novel approach of factoring texture and irradiance in the presence of defocus blur, which allows for pre-filtering noisy irradiance when the texture is not noisy. Our approach naturally allows for different sampling rates for primary and secondary effects, further reducing the overall ray count. While the theory considers only Lambertian surfaces, we obtain promising results for moderately glossy surfaces. We demonstrate 30x sampling rate reduction compared to equal quality noise-free MC. Combined with a GPU implementation and low filtering over-head, we can render scenes with complex geometry and diffuse and glossy BRDFs in a few seconds.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2601097.2601113
