The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous présentons une nouvelle méthode permettant de représenter avec précision la réflectance des peintures métalliques à l’aide d’un modèle de réflectance à deux couches avec des fonctions de distribution de microfacettes échantillonnées. Nous modélisons la structure des peintures métallisées simplifiée par deux couches : une surface de liant qui suit une distribution de microfacettes et une sous-couche qui suit également une distribution de facettes. Dans la sous-couche, les réflectances diffuse et spéculaire représentent respectivement les pigments colorés et les paillettes métalliques. Nous utilisons une méthode itérative basée sur le principe de relaxation de Gauss-Seidel qui ajuste de manière stable les données mesurées à notre modèle hautement non linéaire. Nous optimisons le modèle en traitant les termes de distribution des microfacettes comme une forme non paramétrique linéaire par morceaux afin d'augmenter son degré de liberté. Le modèle proposé est validé en l'appliquant à différentes peintures métallisées. Les résultats montrent que notre modèle a de meilleures performances d'ajustement par rapport aux modèles utilisés dans d'autres études. Notre modèle offre une meilleure précision grâce aux termes non paramétriques utilisés dans le modèle, et permet également d'analyser efficacement les caractéristiques des peintures métalliques grâce à la forme analytique intégrée dans le modèle. Les termes non paramétriques pour la distribution des microfacettes dans notre modèle nécessitent des données densément mesurées, mais pas pour l'ensemble du domaine BRDF (fonction de distribution de réflectance bidirectionnelle), afin que notre méthode puisse réduire la charge d'acquisition des données pendant la mesure. En particulier, cela devient efficace pour un système qui utilise un système de mesure basé sur un échantillon courbe qui nous permet d'obtenir des données denses dans le domaine des microfacettes par une seule mesure.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Gang Yeon KIM, Kwan H. LEE, "A Reflectance Model for Metallic Paints Using a Two-Layer Structure Surface with Microfacet Distributions" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 11, pp. 3076-3087, November 2010, doi: 10.1587/transinf.E93.D.3076.
Abstract: We present a new method that can represent the reflectance 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 paints 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3076/_p
Copier
@ARTICLE{e93-d_11_3076,
author={Gang Yeon KIM, Kwan H. LEE, },
journal={IEICE TRANSACTIONS on Information},
title={A Reflectance Model for Metallic Paints Using a Two-Layer Structure Surface with Microfacet Distributions},
year={2010},
volume={E93-D},
number={11},
pages={3076-3087},
abstract={We present a new method that can represent the reflectance 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 paints 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.},
keywords={},
doi={10.1587/transinf.E93.D.3076},
ISSN={1745-1361},
month={November},}
Copier
TY - JOUR
TI - A Reflectance Model for Metallic Paints Using a Two-Layer Structure Surface with Microfacet Distributions
T2 - IEICE TRANSACTIONS on Information
SP - 3076
EP - 3087
AU - Gang Yeon KIM
AU - Kwan H. LEE
PY - 2010
DO - 10.1587/transinf.E93.D.3076
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2010
AB - We present a new method that can represent the reflectance 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 paints 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.
ER -