A method for estimating the errors in many-light rendering with supersampling
| Autor: | Kosuke Nabata, Hirokazu Sakai, Kei Iwasaki, Shinya Yasuaki |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology many-light rendering lcsh:QA75.5-76.95 Rendering (computer graphics) Computer graphics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Computer vision anti-aliasing Depth of field Cluster analysis depth of field ComputingMethodologies_COMPUTERGRAPHICS participating media business.industry Subsurface scattering 020207 software engineering Supersampling Computer Graphics and Computer-Aided Design Scalability 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence lcsh:Electronic computers. Computer science business |
| Zdroj: | Computational Visual Media, Vol 5, Iss 2, Pp 151-160 (2019) |
| ISSN: | 2096-0662 2096-0433 |
| DOI: | 10.1007/s41095-019-0137-0 |
| Popis: | In many-light rendering, a variety of visual and illumination effects, including anti-aliasing, depth of field, volumetric scattering, and subsurface scattering, are combined to create a number of virtual point lights (VPLs). This is done in order to simplify computation of the resulting illumination. Naive approaches that sum the direct illumination from many VPLs are computationally expensive; scalable methods can be computed more efficiently by clustering VPLs, and then estimating their sum by sampling a small number of VPLs. Although significant speed-up has been achieved using scalable methods, clustering leads to uncontrollable errors, resulting in noise in the rendered images. In this paper, we propose a method to improve the estimation accuracy of many-light rendering involving such visual and illumination effects. We demonstrate that our method can improve the estimation accuracy by a factor of 2.3 over the previous method. |
| Databáze: | OpenAIRE |
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