Affiliation:
1. University of Montreal and Microsoft Corporation
2. Microsoft Corporation
3. University of Montreal
Abstract
We investigate the expressiveness of the microfacet model for isotropic bidirectional reflectance distribution functions (BRDFs) measured from real materials by introducing a
non-parametric factor model
that represents the model’s functional structure but abandons restricted parametric formulations of its factors. We propose a new objective based on
compressive weighting
that controls rendering error in high-dynamic-range BRDF fits better than previous factorization approaches. We develop a simple numerical procedure to minimize this objective and handle dependencies that arise between microfacet factors. Our method faithfully captures a more comprehensive set of materials than previous state-of-the-art parametric approaches yet remains compact (3.2KB per BRDF). We experimentally validate the benefit of the microfacet model over a naïve orthogonal factorization and show that fidelity for diffuse materials is modestly improved by fitting an unrestricted shadowing/masking factor. We also compare against a recent data-driven factorization approach [Bilgili et al. 2011] and show that our microfacet-based representation improves rendering accuracy for most materials while reducing storage by more than 10 ×.
Funder
MITACS, Microsoft Research, and NSERC
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
Reference47 articles.
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2. An Anisotropic Phong BRDF Model
3. A microfacet-based BRDF generator
4. Accurate fitting of measured reflectances using a Shifted Gamma micro-facet distribution
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