Measuring Reflectance of Anisotropic Materials Using Two Handheld Cameras

  • Zar Zar TunEmail author
  • Seiji Tsunezaki
  • Takashi Komuro
  • Shoji Yamamoto
  • Norimichi Tsumura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)


In this paper, we propose a method for measuring the reflectance of anisotropic materials using a simple apparatus consisting of two handheld cameras, a small LED light source, a turning table and a chessboard with markers. The system is configured to obtain the different incoming and outgoing light directions, and the brightness of pixels on the surface of the material. The anisotropic Ward BRDF (Bidirectional Reflectance Distribution Function) model is used to approximate the reflectance, and the model parameters are estimated from the incoming and outgoing angles and the brightness of pixels by using a non-linear optimization method. The initial values of the anisotropic direction are given based on the peak specular lobe on the surface, and the best-fitted one is chosen for the anisotropic direction. The optimized parameters show the well-fitted results between the observed brightness and the BRDF model for each RGB channel. It was confirmed that our system was able to measure the reflectance of different isotropic and anisotropic materials.


Anisotropic materials Reflectance measurement Ward BRDF model 


  1. 1.
    Albert, R.A., Chan, D.Y., Goldman, D.B., O’Brien, J.F.: Approximate svBRDF estimation from mobile phone video. In: Proceedings of the Eurographics Symposium on Rendering: Experimental Ideas & Implementations, SR 2018, pp. 11–22. Eurographics Association, Goslar (2018).
  2. 2.
    Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999)CrossRefGoogle Scholar
  3. 3.
    Fichet, A., Sato, I., Holzschuch, N.: Capturing spatially varying anisotropic reflectance parameters using Fourier analysis. In: Proceedings of the 42nd Graphics Interface Conference, GI 2016, Canadian Human-Computer Communications Society, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, pp. 65–73 (2016).
  4. 4.
    Filip, J., Vávra, R., Haindl, M., Zid, P., Krupicka, M., Havran, V.: BRDF slices: accurate adaptive anisotropic appearance acquisition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2013Google Scholar
  5. 5.
    Furukawa, R., Kawasaki, H., Ikeuchi, K., Sakauchi, M.: Appearance based object modeling using texture database: acquisition, compression and rendering. In: Proceedings of the 13th Eurographics Workshop on Rendering, EGRW 2002, Aire-la-Ville, Switzerland, Switzerland, pp. 257–266. Eurographics Association (2002).
  6. 6.
    Gardner, A., Tchou, C., Hawkins, T., Debevec, P.: Linear light source reflectometry. ACM Trans. Graph. 22(3), 749–758 (2003). Scholar
  7. 7.
    Garrido-Jurado, S., Muñoz Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014). Scholar
  8. 8.
    Hawkins, T., Cohen, J., Debevec, P.: A photometric approach to digitizing cultural artifacts. In: Proceedings of the 2001 Conference on Virtual Reality, Archeology, and Cultural Heritage, pp. 333–342. ACM (2001)Google Scholar
  9. 9.
    Kaplanyan, A.S., Hanika, J., Dachsbacher, C.: The natural-constraint representation of the path space for efficient light transport simulation. ACM Trans. Graph. 33(4), 102:1–102:13 (2014). Scholar
  10. 10.
    Luongo, A., et al.: Modeling the anisotropic reflectance of a surface with microstructure engineered to obtain visible contrast after rotation. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 159–165, October 2017.
  11. 11.
    Murray-Coleman, J., Smith, A.: The automated measurement of BRDFs and their application to luminaire modeling. J. Illum. Eng. Soc. 19(1), 87–99 (1990). Scholar
  12. 12.
    Ngan, A., Durand, F., Matusik, W.: Experimental analysis of BRDF models. In: Proceedings of the Eurographics Symposium on Rendering, pp. 117–226. Eurographics Association (2005)Google Scholar
  13. 13.
    Rump, M., Müller, G., Sarlette, R., Koch, D., Klein, R.: Photo-realistic rendering of metallic car paint from image-based measurements. Comput. Graph. Forum 27(2), 527–536 (2008)CrossRefGoogle Scholar
  14. 14.
    Rump, M., Sarlette, R., Klein, R.: Groundtruth data for multispectral bidirectional texture functions. In: Proceedings of the CGIV/MCS 2010, pp. 326–330. Society for Imaging Science and Technology, June 2010Google Scholar
  15. 15.
    Wang, J., Zhao, S., Tong, X., Snyder, J., Guo, B.: Modeling anisotropic surface reflectance with example-based microfacet synthesis. In: ACM SIGGRAPH 2008 Papers, SIGGRAPH 2008, pp. 41:1–41:9. ACM, New York (2008).
  16. 16.
    Ward, G.J.: Measuring and modeling anisotropic reflection. SIGGRAPH Comput. Graph. 26(2), 265–272 (1992)CrossRefGoogle Scholar
  17. 17.
    Weinmann, M., Ruiters, R., Osep, A., Schwartz, C., Klein, R.: Fusing structured light consistency and Helmholtz normals for 3D reconstruction. In: BMVC (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zar Zar Tun
    • 1
    Email author
  • Seiji Tsunezaki
    • 1
  • Takashi Komuro
    • 1
  • Shoji Yamamoto
    • 2
  • Norimichi Tsumura
    • 3
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan
  2. 2.Tokyo Metropolitan College of Industrial TechnologyTokyoJapan
  3. 3.Graduate School of Science and EngineeringChiba UniversityChibaJapan

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