Paper Info
Title
A Reflectance Model For Metallic Paints Using A Two-Layer Structure Surface With Microfacet Distributions
Abstract
We present a new method that can represent the re flectance of metallic paints accurately using a two layer reflectance model with sampled microfacet distribution functions We model the structure of metallic paints simplified by two layers a binder surface that follows a microfacet distribution and a sub layer that also follows a facet distribution In the sub layer the diffuse and the specular reflectance represent color pigments and metallic flakes respectively We use an iterative method based on the principle of Gauss Seidel relaxation that stably fits the measured data to our highly non linear model We optimize the model by handling the microfacet distribution terms as a piecewise linear non parametric form in order to increase its degree of freedom The proposed model is validated by applying it to various metallic paints The results show that our model has better fitting performance compared to the models used in other studies Our model provides better accuracy due to the non parametric terms employed in the model and also gives efficiency in analyzing the characteristics of metallic palms by the analytical form embedded in the model The non parametric terms for the microfacet distribution in our model require densely measured data but not for the entire BRDF(bidirectional reflectance distribution function) domain so that our method can reduce the burden of data acquisition during measurement Especially it becomes efficient for a system that uses a curved sample based measurement system which allows us to obtain dense data in microfacet domain by a single measurement.
Year
DOI
Venue
2010
10.1587/transinf.E93.D.3076
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
DocType
Volume
metallic paint, reflectance modeling, multi layer, surface measure and fit, non parametric basis function
Journal
E93D
Issue
ISSN
Citations 
11
1745-1361
0
PageRank 
References 
Authors
0.34
12
2
Name
Order
Citations
PageRank
Gang Yeon Kim100.34
Kwan H. Lee214829.48