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| Content Provider | ACM Digital Library |
|---|---|
| Author | Křivánek, Jaroslav Cañada, Juan Georgiev, Iliyan Kaplanyan, Anton S. |
| Abstract | Robust and efficient light-transport simulation based on statistical methods is the subject of renewed research interest, propelled by the desire to accurately render general environments with complex materials and light sources, which is often difficult with current solutions. In addition, it has been recognized that advanced methods, which can render many effects in one pass without excessive tweaking, increase artists' productivity and allow them to focus on their creative work. For this reason, the movie industry is shifting away from approximate rendering solutions toward physically based rendering methods, which poses new challenges in terms of strict requirements on high image quality and algorithm robustness. Many of the recent advances in light-transport simulation, such as new Markov chain Monte Carlo methods or robust combination of bidirectional path tracing with photon mapping, are made possible by interpreting light transport as an integral in the space of light paths. However, there is a great deal of confusion among practitioners and researchers alike regarding these path-space methods. The goal of this course is twofold. First, it presents a coherent review of the path-integral formulation of light transport and its applications, including the most recent ones, and it shows that rendering algorithms that may seem complex at first sight are, in fact, naturally derived from this general framework. A significant part of the course is devoted to application of Markov chain Monte Carlo methods for light-transport simulation, such as Metropolis Light Transport and its variants. The second part of the course discusses practical aspects of applying advanced light-transport simulation methods in the movie industry and other application domains, such as architectural and product visualization. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | PDF MP4 |
| ISBN | 9781450323390 |
| DOI | 10.1145/2504435.2504439 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Audio Text |
| Resource Type | Article |
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