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Neural Importance Sampling for Layered BSDFs
This project aims to improve the simulation of complex materials such as layered paint, fabrics and coated metals in computer graphics applications. We will develop a neural network-based sampling model that allows accurate and efficient light transport within these materials. In addition, we will integrate this model with general path-guiding techniques to minimize noise on both microscopic and macroscopic levels of light interaction. This collaboration strengthens ongoing research between ÉTS (Canada) and XLIM (France), and provides advanced training for students in modern rendering methods that are directly compatible with digital content creation pipelines in animation, visual effects and video games.
Faculty Supervisor:
Adrien Gruson
Student:
Partner:
Université de Poitiers
Discipline:
Computer science
Sector:
Education
University:
École de technologie supérieure
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.