A Unified Algorithm for BRDF Matching

  • Ron VanderfeestenEmail author
  • Jacco Bikker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)


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.


BRDF Fitting Material model 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Renhorn, I.G.E., Boreman, G.D.: Analytical fitting model for rough-surface BRDF. Opt. Express 16, 12892–12898 (2008)CrossRefGoogle Scholar
  4. 4.
    Guarnera, D., et al.: Perceptually validated cross-renderer analytical BRDF parameter remapping. IEEE Trans. Vis. Comput. Graph. 1 (2018)Google Scholar
  5. 5.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kreyszig, E., Kreyszig, H., Norminton, E.J.: Advanced Engineering Mathematics. Wiley, Hoboken (2011)zbMATHGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Matusik, W., Pfister, H., Brand, M., McMillan, L.: A data-driven reflectance model. ACM Trans. Graph. 22, 759–769 (2003)CrossRefGoogle Scholar
  9. 9.
    Montes, R., Urena, C.: An Overview of BRDF Models, February 2012Google Scholar
  10. 10.
    Ngan, A., Durand, F., Matusik, W.: Experimental analysis of BRDF models. In: Eurographics Symposium on Rendering 2005 (2005)Google Scholar
  11. 11.
    Pacanowski, R., Salazar Celis, O., Schlick, C., Granier, X., Poulin, P., Cuyt, A.: Rational BRDF. IEEE Trans. Visual. Comput. Graph. 18, 1824–1835 (2012)CrossRefGoogle Scholar
  12. 12.
    Sinha, P., Russell, R.: A perceptually based comparison of image similarity metrics. Perception 40, 1269–1281 (2011)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Yu, C., Seo, Y., Lee, S.W.: Global optimization for estimating a multiple-lobe analytical BRDF. Comput. Vis. Image Underst. 115, 1679–1688 (2011)CrossRefGoogle Scholar
  16. 16.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departement of Geometrical ComputingUniversiteit UtrechtUtrechtThe Netherlands

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