Advertisement

Estimating Material Parameters Using Light Scattering Model and Polarization

  • Hadi A. DahlanEmail author
  • Edwin R. Hancock
  • William A. P. Smith
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 603)

Abstract

When rendering the 3D graphics object in the computer vision and graphics fields, the parameter values, such as the surface roughness, refractive index, and light absorption are used for modeling realistic light scattering rendering behavior of a material. Most of these values were usually obtained from different literature or online sources. In general, a specific measuring tool is needed when acquiring the measurement, but, what if we could use an alternative method of measuring when the tools are not available? The idea is to use the light scattering model to estimate the parameter measurement values of material by doing the inverse rendering of the capture polarization image of the object’s light scattering. In this paper, we investigate whether we can estimate four different measurements, using ARLLS model. We captured the object’s degree of polarization (DOP) to be fitted and compare with the model to investigate the relationship between the two by doing the correlation test between the object measurement DOP and the model parameters to see whether the estimations are in agreement with each other (e.g. by reference or by the practical characteristics of the object). Furthermore, the test will be conducted using materials with varying degree of properties (e.g. very rough surface; highly chromatic).

Keywords

Light scattering model Measurement estimation Light polarization Correlation test 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Montes, R., & Urea, C. (2012). An overview of BRDF models. University of Grenada, Technical Report LSI-2012, 1.Google Scholar
  2. 2.
    Chen, C. H. (2015). Handbook of pattern recognition and computer vision. World Scientific.Google Scholar
  3. 3.
    Wynn, C. (2000). An introduction to BRDF-based lighting. Nvidia Corporation.Google Scholar
  4. 4.
    Thompson, W., Fleming, R., Creem-Regehr, S., & Stefanucci, J. K. (2016). Visual perception from a computer graphics perspective. AK Peters/CRC Press.Google Scholar
  5. 5.
    Kurt, M., & Edwards, D. (2009). A survey of BRDF models for computer graphics. ACM SIGGRAPH Computer Graphics, 43(2), 4.Google Scholar
  6. 6.
    Wolff, L. B. (1994). Diffuse-reflectance model for smooth dielectric surfaces. JOSA A, 11(11), 2956-2968.Google Scholar
  7. 7.
    Ragheb, H., & Hancock, E. R. (2006). Testing new variants of the Beckmann Kirchhoff model against radiance data. Computer Vision and Image Understanding, 102(2), 145-168.Google Scholar
  8. 8.
    Guarnera, D., Guarnera, G. C., Ghosh, A., Denk, C., & Glencross, M. (2016, May). BRDF representation and acquisition. In Computer Graphics Forum (Vol. 35, No. 2, pp. 625-650).Google Scholar
  9. 9.
    Ragheb, H., & Hancock, E. R. (2007). The modified Beckmann Kirchhoff scattering theory for rough surface analysis. Pattern Recognition, 40(7), 2004-2020.Google Scholar
  10. 10.
    Ragheb, Hossein, and Edwin R. Hancock. “A light scattering model for layered dielectrics with rough surface boundaries.” International Journal of Computer Vision 79.2 (2008): 179-207.Google Scholar
  11. 11.
    Dahlan, Hadi A., and Edwin R. Hancock. “Absorptive scattering model for rough laminar surfaces.” Pattern Recognition (ICPR), 2016 23rd International Conference on. IEEE, 2016.Google Scholar
  12. 12.
    Konnen, G. P., & Knnen, G. P. (1985). Polarized light in nature. CUP Archive.Google Scholar
  13. 13.
    Barron, L. D. (2009). Molecular light scattering and optical activity. Cambridge University Press.Google Scholar
  14. 14.
    Perez, J. J. G., & Ossikovski, R. (2016). Polarized light and the Mueller matrix approach. CRC press.Google Scholar
  15. 15.
    Kerker, M. (2016). The Scattering of Light and Other Electromagnetic Radiation. Elsevier.Google Scholar
  16. 16.
    Goldstein, D. H. (2017). Polarized Light. CRC Press.Google Scholar
  17. 17.
    Bohren, C. F., & Huffman, D. R. (2008). Absorption and scattering of light by small particles. John Wiley & Sons.Google Scholar
  18. 18.
    Zhang, L., & Hancock, E. R. (2012, November). A comprehensive polarisation model for surface orientation recovery. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 3791-3794). IEEE.Google Scholar
  19. 19.
    Ma, Wan-Chun, et al. “Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination.” Proceedings of the 18th Eurographics conference on Rendering Techniques. Eurographics Association, 2007.Google Scholar
  20. 20.
    Dutta, Abhishek. “Face Shape and Reflectance Acquisition using a Multispectral Light Stage.” Diss. University of York, 2010.Google Scholar
  21. 21.
    Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.Google Scholar
  22. 22.
    Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. John Wiley & Sons.Google Scholar
  23. 23.
    Govaerts, Y. M., Jacquemoud, S., Verstraete, M. M., & Ustin, S. L. (1996). Three-dimensional radiation transfer modeling in a dicotyledon leaf. Applied Optics, 35(33), 6585-6598.Google Scholar
  24. 24.
    Stimson, H. C., Breshears, D. D., Ustin, S. L., & Kefauver, S. C. (2005). Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment, 96(1), 108-118.Google Scholar
  25. 25.
    Zhang, L., & Hancock, E. R. (2013). Robust estimation of shape and polarisation using blind source separation. Pattern Recognition Letters, 34(8), 856-862.Google Scholar
  26. 26.
    Atkinson, G. A., & Hancock, E. R. (2006). Recovery of surface orientation from diffuse polarization. IEEE transactions on image processing, 15(6), 1653-1664.Google Scholar
  27. 27.
    McPherson, A. T., & Cummings, A. D. (1935). Refractive index of rubber. Rubber Chemistry and Technology, 8(3), 421-429.Google Scholar
  28. 28.
    Mikhail Polyanskiy. RefractiveIndex.INFO - Refractive index database. https://refractiveindex.info, 2018. [Online; accessed March 26, 2018].
  29. 29.
    Index of Refraction. http://hyperphysics.phy-astr.gsu.edu/hbase/Tables/indrf.html, 2018. [Online; accessed March 26, 2018].
  30. 30.
    Mukherjee, S. (2013). Physical Properties of Clay and Soil Mechanics. In The Science of Clays (pp. 54-68). Springer, Dordrecht.Google Scholar
  31. 31.
    Perry, D. R., Appleyard, H. M., Cartridge, G., Cobb, P. G. W., Coop, G. E., Lomas, B., … & Farnfield, C. A. (1985). Identification of textile materials.Google Scholar
  32. 32.
    Woolley, J. T. (1971). Reflectance and transmittance of light by leaves. Plant physiology, 47(5), 656-662.Google Scholar
  33. 33.
    Forest B. H. Brown. (1920). The Refraction of Light in Plant Tissues. Bulletin of the Torrey Botanical Club, 47(6), 243-260.  https://doi.org/10.2307/2480396
  34. 34.
    Gausman, H. W., Allen, W. A., & Escobar, D. E. (1974). Refractive index of plant cell walls. Applied optics, 13(1), 109-111.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hadi A. Dahlan
    • 1
    Email author
  • Edwin R. Hancock
    • 2
  • William A. P. Smith
    • 2
  1. 1.National University of MalaysiaBangiMalaysia
  2. 2.University of YorkYorkUK

Personalised recommendations