A Fast Probabilistic Bidirectional Texture Function Model

  • Michal Haindl
  • Jiří Filip
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3212)


The bidirectional texture function (BTF) describes rough texture appearance variations due to varying illumination and viewing conditions. Such a function consists of thousands of measurements (images) per sample. Resulted BTF size excludes its direct rendering in graphical applications and some compression of these huge BTF data spaces is obviously inevitable. In this paper we present a novel fast probabilistic model-based algorithm for realistic BTF modelling allowing such an efficient compression with possibility of direct implementation inside the graphics card. The analytical step of the algorithm starts with the BTF space segmentation and range map estimation of the BTF surface followed by the spectral and spatial factorisation of selected sub-space multispectral texture images. Single monospectral band-limited factors are independently modelled by their dedicated causal autoregressive models (CAR). During rendering the corresponding sub-space images of arbitrary size are synthesised and both multispectral and range information is combined in a bump mapping filter of the rendering hardware according to view and illumination positions. The presented model offers huge BTF compression ratio unattainable by any alternative sampling-based BTF synthesis method. Simultaneously this model can be used to reconstruct missing parts of the BTF measurement space.


Markov Chain Monte Carlo Method Texture Synthesis Colour Texture Photometric Stereo Bump Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dana, K., van Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics 18, 1–34 (1999)CrossRefGoogle Scholar
  2. 2.
    Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: Proc. Int. Conf. on Computer Vision, vol. 2, pp. 1033–1038 (1999)Google Scholar
  3. 3.
    Heeger, D., Bergen, J.: Pyramid based texture analysis/synthesis. In: Proc. SIGGRAPH 1995, pp. 229–238. ACM, New York (1995)CrossRefGoogle Scholar
  4. 4.
    Haindl, M., Havlíček, V.: Multiresolution colour texture synthesis. In: Dobrovodský, K. (ed.) Proceedings of the 7th International Workshop on Robotics in Alpe-Adria-Danube Region, ASCO Art, Bratislava, pp. 297–302 (1998)Google Scholar
  5. 5.
    Bennett, J.K.: Multispectral random field models for synthesis and analysis of color images. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 327–332 (1998)CrossRefGoogle Scholar
  6. 6.
    Haindl, M., Havlíček, V.: A multiresolution causal colour texture model. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 114–122. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Haindl, M., Havlíček, V.: A multiscale colour texture model. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.) Proceedings of the 16th International Conferenceon Pattern Recognition, pp. 255–258. IEEE Computer Society, Los Alamitos (2002)Google Scholar
  8. 8.
    Haindl, M.: Texture synthesis. CWI Quarterly 4, 305–331 (1991)zbMATHGoogle Scholar
  9. 9.
    Blinn, J.: Models of light reflection for computer synthesized pictures. In: Proceedings of Computer Graphics ACM SIGGRAPH. Annual Conference Series, pp. 192–198 (1977)Google Scholar
  10. 10.
    Woodham, R.: Analysing images of curved surface. Artificial Intelligence 17, 117–140 (1981)CrossRefGoogle Scholar
  11. 11.
    Welsch, T.: Parallax mapping with offset limiting: A per-pixel approximation of uneven surfaces. Technical Report Revision 0.3 (2004),
  12. 12.
    Haindl, M., Filip, J.: Fast btf texture modelling. In: Chandler, M. (ed.) Texture 2003, IEEE Computer Society, Los Alamitos (2003)Google Scholar
  13. 13.
    Frankot, R.T., Chellappa, R.: A method for enforcing integrability in shape from shading algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 10, 439–451 (1988)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Michal Haindl
    • 1
  • Jiří Filip
    • 1
  1. 1.Dept. of Pattern Recognition, Institute of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPragueCzech Republic

Personalised recommendations