“A Compact Representation for Multiple Scaering in Participating Media using Neural Networks”
Conference:
Type:
Entry Number: 16
Title:
- A Compact Representation for Multiple Scaering in Participating Media using Neural Networks
Presenter(s)/Author(s):
Abstract:
Many materials, such as milk or wax, exhibit scattering effects; incoming light enters the material and is scattered inside, giving a translucent aspect. These effects are computationally intensive as they require simulating a large number of events. Full computations are expensive, even with accelerating methods such as Virtual Ray Lights. The dipole approximation [Jensen et al. 2001] is fast, but a strong approximation. Precomputing the material response for multiple scattering [Moon et al. 2007; Wang and Holzschuch 2017] integrates well with existing rendering algorithms, allowing separate computation for single- and double- scattering, and fast computation for multiple scattering. Their main issue is efficient storage for the precomputed multiple scattering data.
References:
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Jonathan T. Moon, Bruce Walter, and Stephen R. Marschner. 2007. Rendering Discrete Random Media Using Precomputed Scattering Solutions. In Rendering Techniques (proc. EGSR). 231–242.
Jan Novák, Derek Nowrouzezahrai, Carsten Dachsbacher, and Wojciech Jarosz. 2012. Virtual Ray Lights for Rendering Scenes with Participating Media. ACM Trans. Graph. (proc. SIGGRAPH) 31, 4, Article 60 (July 2012), 11 pages.
Peiran Ren, Jiaping Wang, Minmin Gong, Stephen Lin, Xin Tong, and Baining Guo. 2013. Global Illumination with Radiance Regression Functions. ACM Trans. Graph. 32, 4, Article 130 (July 2013), 12 pages.
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Beibei Wang and Nicolas Holzschuch. 2017. Precomputed Multiple Scattering for Light Simulation in Participating Medium. In ACM SIGGRAPH 2017 Talks (SIGGRAPH ’17). Article 35, 2 pages.
