“Adaptive Environment Sampling on CPU and GPU” by Atanasov, Koylazov, Taskov, Soklev, Chizhov, et al. …
Conference:
Type:
Entry Number: 70
Title:
- Adaptive Environment Sampling on CPU and GPU
Presenter(s)/Author(s):
Abstract:
We present a production-ready approach for efficient environment light sampling which takes visibility into account. During a brief learning phase we cache visibility information in the camera space. The cache is then used to adapt the environment sampling strategy during the final rendering. Unlike existing approaches that account for visibility, our algorithm uses a small amount of memory, provides a lightweight sampling procedure that benefits even unoccluded scenes and, importantly, requires no additional artist care, such as manual setting of portals or other scene-specific adjustments. The technique is unbiased, simple to implement and integrate into a render engine. Its modest memory requirements and simplicity enable efficient CPU and GPU implementations that significantly improve the render times, especially in complex production scenes.
References:
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David Cline, Daniel Adams, and Parris Egbert. 2008. Table-driven Adaptive Importance Sampling. In Proceedings of the Nineteenth Eurographics Conference on Rendering (EGSR ’08). Eurographics Association, 1115–1123.
Ondřej Karlík. 2014. It’s all about usability. In ACM SIGGRAPH 2014 Courses (SIGGRAPH ’14). ACM, New York, NY, USA, Article 17, 6 pages.
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Eric Veach. 1998. Robust Monte Carlo Methods for Light Transport Simulation. Ph.D. Dissertation. Stanford, CA, USA. Advisor(s) Guibas, Leonidas J. AAI9837162.
