genBRDF
Abstract
We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modeling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterize complex high-dimensional reflectance functions through a large-scale optimization. We present a number of analysis tools and data visualization techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 27, 2014
- Source ID
- 10.1145/2601097.2601193
Entities
People
- Adam Brady
- Jason Lawrence
- Pieter Peers
- Westley Weimer
Organizations
- Defense Advanced Research Projects Agency
- Division of Computing and Communication Foundations
- Division of Information and Intelligent Systems
- Nvidia
- University of Virginia