Factored axis-aligned filtering for rendering multiple distribution effects
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...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100017 https://orcid.org/0000-0001-9919-069X |
| _version_ | 1826194974634409984 |
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| author | Mehta, Soham Uday Yao, JiaXian Ramamoorthi, Ravi Durand, Fredo |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Mehta, Soham Uday Yao, JiaXian Ramamoorthi, Ravi Durand, Fredo |
| author_sort | Mehta, Soham Uday |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:04:47Z |
| format | Article |
| id | mit-1721.1/100017 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:04:47Z |
| publishDate | 2015 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1000172022-09-26T15:36:22Z Factored axis-aligned filtering for rendering multiple distribution effects Mehta, Soham Uday Yao, JiaXian Ramamoorthi, Ravi Durand, Fredo Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Durand, Fredo 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. National Science Foundation (U.S.) (Grant CGV 1115242) National Science Foundation (U.S.) (Grant CGV 1116303) Intel Corporation (Science and Technology Center for Visual Computing) 2015-11-24T13:33:04Z 2015-11-24T13:33:04Z 2014-07 Article http://purl.org/eprint/type/ConferencePaper 07300301 http://hdl.handle.net/1721.1/100017 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. https://orcid.org/0000-0001-9919-069X en_US http://dx.doi.org/10.1145/2601097.2601113 ACM Transactions on Graphics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) Other univ. web domain |
| spellingShingle | Mehta, Soham Uday Yao, JiaXian Ramamoorthi, Ravi Durand, Fredo Factored axis-aligned filtering for rendering multiple distribution effects |
| title | Factored axis-aligned filtering for rendering multiple distribution effects |
| title_full | Factored axis-aligned filtering for rendering multiple distribution effects |
| title_fullStr | Factored axis-aligned filtering for rendering multiple distribution effects |
| title_full_unstemmed | Factored axis-aligned filtering for rendering multiple distribution effects |
| title_short | Factored axis-aligned filtering for rendering multiple distribution effects |
| title_sort | factored axis aligned filtering for rendering multiple distribution effects |
| url | http://hdl.handle.net/1721.1/100017 https://orcid.org/0000-0001-9919-069X |
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