Calculation Methods for Digital Model Creation Based on Integrated Shape, Color and Angular Reflectivity Measurement

  • Robert Sitnik
  • Grzegorz Mączkowski
  • Jakub Krzesłowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6436)

Abstract

The paper presents a complete methodology for processing sets of data registered by the means of a measurement system providing integrated 3D shape, multispectral color and angular reflectance characteristic. The data comprise of clouds of points representing the shape of the measured object, a set of intensity responses as a function of wavelength of incident light used for color calculation and a set of distributions of reflected intensity as a function of illumination and observation angles. Presented approach allows to create a complete 3D model of the measured object which preserves the object’s shape, color and reflectivity properties. It is developed specifically for application in the digitization of cultural heritage objects for storing and visualization purposes, as well as duplication by the means of 3D printing technology.

Keywords

cultural heritage digitization structured light projection multispectral color BRDF cloud of points triangle mesh texture calculation methods 

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References

  1. 1.
    Pavlidis, G., Koutsoudis, A., Arnaoutoglou, F., Tsioukas, V., Chamzas, C.: Methods for 3D digitization of Cultural Heritage. Journal of Cultural Heritage 8(1), 93–98 (2007)CrossRefGoogle Scholar
  2. 2.
    Imai, F.H., Rosen, M.R., Berns, R.S.: Multi-spectral imaging of Van Gogh’s Self-portrait at the National Gallery of Art. In: Proceedings of IS&T PICS Conference, Washington, D.C, pp. 185–189. IS&T, Springfield, VA (2001)Google Scholar
  3. 3.
    Imai, F.H., Rosen, M.R., Berns, R.S.: Comparison of Spectrally Narrow-Band capture versus wide-band with a priori sample analysis for spectral reflectance estimation. In: Proceedings of IS&T’s, pp. 234–241 (2000)Google Scholar
  4. 4.
    Conde, J., Haneishi, H., Yamaguchi, M., Ohyama, N., Baez, J.: Spectral Reflectance Estimation of Ancient Mexican Codices, Multispectral Images Approach. Revista Mexicana de Fisica 50, 484–489 (2004)Google Scholar
  5. 5.
    Georghiades, A.S.: Recovering 3-D Shape and Reflectance from a Small Number of Photographs. ACM International Conference Proceeding Series, vol. 44, pp. 230–240 (2003)Google Scholar
  6. 6.
    Sitnik, R., Mączkowski, G., Krzesłowski, J.: Integrated Shape, Color, and Reflectivity Measurement Method for 3D Digitization of Cultural Heritage Objects. In: Proceedings of SPIE, vol. 7526, p. 75260Q (2010)Google Scholar
  7. 7.
    Sitnik, R.: New Method of Structure Light Measurement System Calibration Based on Adaptive and Effective Evaluation of 3D-Phase Distribution. In: Proceedings of SPIE, vol. 5856, p. 109 (2005)Google Scholar
  8. 8.
    Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  9. 9.
    Phong, B.T.: Illumination for Computer Generated Pictures. Communications of the ACM 18, 311–317 (1975)CrossRefGoogle Scholar
  10. 10.
    Sitnik, R., Kujawińska, M., Woźnicki, J.: Digital Fringe Projection System for Large-Volume 360-deg Shape Measurement. Optical Engineering 41, 443–449 (2002)CrossRefGoogle Scholar
  11. 11.
    Sitnik, R., Kujawińska, M., Załuski, W.: 3DMADMAC System: Optical 3D Shape Acquisition and Processing Path for VR Applications. In: Proceedings of SPIE, vol. 5857, pp. 106–117 (2005)Google Scholar
  12. 12.
    Sitnik, R., Kujawińska, M.: From Reality to Virtual Reality: 3D Object Imaging Techniques and Algorithms. In: Proceedings of SPIE, vol. 5146, pp. 54–61 (2003)Google Scholar
  13. 13.
    Sitnik, R., Kujawińska, M.: From Cloud of Point Co-ordinates to 3D Virtual Environment: The Data Conversion System. Optical Engineering 41(2), 416–427 (2002)CrossRefGoogle Scholar
  14. 14.
    Sitnik, R., Karaszewski, M.: Optimized Point Cloud Triangulation for 3D Scanning Systems. Machine Graphics & Vision 17, 349–371 (2008)Google Scholar
  15. 15.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  16. 16.
    Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley & Sons, New York (2000)Google Scholar
  17. 17.
    Malacara, D.: Color Vision and Colorimetry: Theory and Applications. SPIE Press, Bellingham (2002)Google Scholar
  18. 18.
    Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsber, I.W., Limperis, T.: Geometrical Considerations and Nomenclature for Reflectance, NBS Monograph 160, U. S. Dept. of Commerce (1977)Google Scholar
  19. 19.
    Rusinkiewicz, S.: A New Change of Variables for Efficient BRDF Representation. In: Drettakis, G., Max, N. (eds.) Rendering Techniques 1998 (Proceedings of Eurographics Rendering Workshop 1998), pp. 11–22. Springer, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert Sitnik
    • 1
  • Grzegorz Mączkowski
    • 1
  • Jakub Krzesłowski
    • 1
  1. 1.Institute of Micromechanics and PhotonicsWUTWarsawPoland

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