Abstract
This paper generalizes existing BRDF fitting algorithms presented in the literature that aims to find a mapping of the parameters of an arbitrary source material model to the parameters of a target material model. A material model in this context is a function that maps a list of parameters, such as roughness or specular color, to a BRDF. Our conversion function approximates the original model as close as possible under a chosen similarity metric, either in physical reflectivities or perceptually, and calculates the error with respect to this conversion. Our conversion function imposes no constraints other than that the dimensionality of the represented BRDFs match.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bagher, M.M., Soler, C., Holzschuch, N.: Accurate fitting of measured reflectances using a Shifted Gamma micro-facet distribution. Comput. Graph. Forum 31, 1509–1518 (2012)
Brady, A., Lawrence, J., Peers, P., Weimer, W.: genBRDF: discovering new analytic BRDFs with genetic programming. ACM Trans. Graph. 33, 114:1–114:11 (2014)
Renhorn, I.G.E., Boreman, G.D.: Analytical fitting model for rough-surface BRDF. Opt. Express 16, 12892–12898 (2008)
Guarnera, D., et al.: Perceptually validated cross-renderer analytical BRDF parameter remapping. IEEE Trans. Vis. Comput. Graph. 1 (2018)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Kreyszig, E., Kreyszig, H., Norminton, E.J.: Advanced Engineering Mathematics. Wiley, Hoboken (2011)
Lee, Y., Yu, C., Lee, S.W.: Sequential fitting-and-separating reflectance components for analytical bidirectional reflectance distribution function estimation. Appl. Opt. 57, 242–250 (2018)
Matusik, W., Pfister, H., Brand, M., McMillan, L.: A data-driven reflectance model. ACM Trans. Graph. 22, 759–769 (2003)
Montes, R., Urena, C.: An Overview of BRDF Models, February 2012
Ngan, A., Durand, F., Matusik, W.: Experimental analysis of BRDF models. In: Eurographics Symposium on Rendering 2005 (2005)
Pacanowski, R., Salazar Celis, O., Schlick, C., Granier, X., Poulin, P., Cuyt, A.: Rational BRDF. IEEE Trans. Visual. Comput. Graph. 18, 1824–1835 (2012)
Sinha, P., Russell, R.: A perceptually based comparison of image similarity metrics. Perception 40, 1269–1281 (2011)
Walter, B., Marschner, S.R., Li, H., Torrance, K.E.: Microfacet models for refraction through rough surfaces. In: the 18th Eurographics Conference on Rendering Techniques, EGSR 2007, pp. 195–206 (2007)
Wang, Q., Zhao, J., Gong, Y., Hao, Q., Peng, Z.: Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model. Appl. Opt. 56, 9165–9170 (2017)
Yu, C., Seo, Y., Lee, S.W.: Global optimization for estimating a multiple-lobe analytical BRDF. Comput. Vis. Image Underst. 115, 1679–1688 (2011)
Yu, J., Tu, W., Wang, Z.: A BP training fitting method about multivariate BRDF based on B-spline function. In: 2012 Fifth International Conference on Information and Computing Science (ICIC), pp. 30–32 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vanderfeesten, R., Bikker, J. (2019). A Unified Algorithm for BRDF Matching. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-22514-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22513-1
Online ISBN: 978-3-030-22514-8
eBook Packages: Computer ScienceComputer Science (R0)