A New Coefficient for a Two-Scale Microfacet Reflectance Model

  • Hongbin YangEmail author
  • Mingxue Liao
  • Changwen Zheng
  • Pin Lv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)


Reflectance properties express how objects in a virtual scene interact with light; they control the appearance of the object: whether it looks shiny or not, whether it has a metallic or plastic appearance. Having a good reflectance model is essential for the production of photorealistic pictures. A considerate reflectance model needs to consider both specular peak and wavelength dependency. So a model combining reflection and diffraction is a reasonable idea. Holzschuch and Pacanowski proposed a two-scale microfacet model combining reflection and diffraction. However, the coefficient that connects the two parts of this model is not very physically-based since reflection part gets close to zero for a wide angle range while the surface roughness is excessive. In this paper, we design a new coefficient which controls the two parts of this model in a more reasonable range. As a result, the improved model produces a good approximation to measured reflectance. Moreover, we compute the integral of the diffraction part with higher efficiency by using Monte Carlo integration. Finally we use piecewise sampling to fit the parameters, and we can get model parameters faster.


Microfacet model Diffraction model Monte Carlo integration 


  1. 1.
    Cook, R.L., Torrance, K.E.: A reflectance model for computer graphics. ACM Trans. Graph. 1(1), 7–24 (1982)CrossRefGoogle Scholar
  2. 2.
    Torrance, K.E., Sparrow, E.M.: Theory for off-specular reflection from roughened surface. J. Opt. Soc. Am. 57(9), 1105–1114 (1967)CrossRefGoogle Scholar
  3. 3.
    Ngan, A., Durand, F., Matusik, W.: Experimental analysis of BRDF models. In: Eurographics Symposium on Rendering, pp. 117–226 (2005)Google Scholar
  4. 4.
    Bagher, M.M., Soler, C., Holzschuch, N.: Accurate fitting of measured reflectances using a shifted gamma micro-facet distribution. Comput. Graph. Forum 31(4), 1509–1518 (2012)CrossRefGoogle Scholar
  5. 5.
    L\(\ddot{o}\)w, J., Kronander, J., Ynnerman, A., Unger, J.: BRDF models for accurate and efficient rendering of glossy surfaces. ACM Trans. Graph 31(1), 9:1–9:14 (2012)Google Scholar
  6. 6.
    Holzschuch, N., Pacanowski, R.: A two-scale microfacet reflectance model combining reflection and diffraction. ACM Trans. Graph 36(4), 66:1–66:12 (2017)CrossRefGoogle Scholar
  7. 7.
    Walter, B., Marschner, S., Li, H., Torrance, K.E.: Microfacet models for refraction through rough surfaces. In: Eurographics Symposium on Rendering, pp. 195–206 (2007)Google Scholar
  8. 8.
    Burley, B.: Physically-based shading at Disney. In: Hill, S., McAuley, S. (eds.) Siggraph Course: Practical Physically Based Shading in Film and Game Production. ACM (2012)Google Scholar
  9. 9.
    Brady, A., Lawrence, J., Peers, P., Weimer, W.: genBRDF: discovering new analytic BRDFs with genetic programming. ACM Trans. Graph 33(4), 114:1–114:11 (2014)CrossRefGoogle Scholar
  10. 10.
    Smith, B.: Geometrical shadowing of a random rough surface. IEEE Trans. Antennas Propag. 15(5), 668–671 (1967)CrossRefGoogle Scholar
  11. 11.
    Heitz, E.: Understanding the masking-shadowing function in microfacet-based BRDFs. J. Comput. Graph. Tech. 3(2), 32–91 (2014)Google Scholar
  12. 12.
    Larson, G.J.W.: Measuring and modeling anisotropic reflection. In: Computer Graphics (Proceedings of SIGGRAPH 1992), pp. 265–272 (1992)Google Scholar
  13. 13.
    Walter, B.: Notes on the ward BRDF. Technical report PCG-05-06, Cornell Program of Computer Graphics (2005)Google Scholar
  14. 14.
    Lawrence, J., Rusinkiewicz, S., Ramamoorthi, R.: Efficient BRDF importance sampling using a factored representation. ACM Trans. Graph. 23(3), 496–505 (2004)CrossRefGoogle Scholar
  15. 15.
    Schlick, C.: An inexpensive BRDF model for physically-based rendering. Comput. Graph. Forum 13(3), 233–246 (1994)CrossRefGoogle Scholar
  16. 16.
    Harvey, J.E.: Light-scattering characteristics of optical surface. Ph.D. thesis, University of Arizona. Adviser: R.V. Shack (1975)Google Scholar
  17. 17.
    Krywonos, A.: Predicting surface scatter using a linear systems formulation of non-paraxial scalar diffraction. Ph.D. thesis, University of Central Florida. Adviser: J.E. Harvey (2006)Google Scholar
  18. 18.
    Matusik, W., Pfister, H., Brand, M., McMillan, L.: A data-driven reflectance model. ACM Trans. Graph. 22(3), 759–769 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hongbin Yang
    • 1
    • 2
    Email author
  • Mingxue Liao
    • 1
  • Changwen Zheng
    • 1
  • Pin Lv
    • 1
  1. 1.Science and Technology on Integrated Infomation System Laboratory, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations