Advertisement

3D Surface Texture Synthesis Using Wavelet Coefficient Fitting

  • Muwei Jian
  • Ningbo Hao
  • Junyu Dong
  • Rong Jiang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 56)

Abstract

Texture synthesis is widely used in virtual reality and computer games and has become one of the most active research areas. Research into texture synthesis is normally concerned with generation of 2D images of texture. However, real-world surface textures comprise rough surface geometry and various reflectance properties. These surface textures are different from 2D still texture as their images can therefore vary dramatically with illumination directions. This paper presents a simple framework for 3D surface texture synthesis. Firstly, we propose a novel 2D texture synthesis algorithm based on wavelet transform that can be efficiently extended to synthesis surface representations in multi-dimensional space. The proposed texture synthesis method can avoid joint seams during synthesis by first fitting wavelet coefficients in the overlap texture images, and then performing an inverse wavelet transform to generate new textures. Then, Photometric Stereo (PS) is used to generate surface gradient and albedo maps from three synthesized surface texture images. The surface gradient maps can be further integrated to produce a surface height map (surface profile). With the albedo and height or gradient maps, new images of a Lambertian surface under arbitrary illuminant directions can be generated. Experiments show that the proposed approach can not only produce 3D surface textures under arbitrary illumination directions, but also have the ability to retain the surface geometry structure.

Keywords

Texture Synthesis Wavelet Transform 3D Surface Texture Photometric Stereo 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dong, J., Chantler, M.: Capture and synthesis of 3D surface texture. International Journal of Computer Vision (IJCV) 62(1-2), 177–194 (2005)CrossRefGoogle Scholar
  2. 2.
    Dong, J., Chantler, M.: Comparison of five 3D surface texture synthesis methods. In: Proceeding of the 3rd International Workshop on Texture Analysis & Synthesis, Nice, France, October 17 (2003)Google Scholar
  3. 3.
    Zhu, S.C., Liu, X.W., Wu, Y.N.: Exploring texture ensembles by efficient Markov chain Monte Carlo-Toward a "trichromacy" theory of texture. IEEE Transactions on Pattern Analysis & Machine Intelligence 22(6), 554–569 (2000)CrossRefGoogle Scholar
  4. 4.
    Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 40(1), 49–71 (2000)MATHCrossRefGoogle Scholar
  5. 5.
    Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: Proceedings International Conference on Image Processing (Cat. No.95CB35819), vol. 3, pp. 648–651. IEEE Comput. Soc. Press, Los Alamitos (1995)Google Scholar
  6. 6.
    De Benet, J.S.: Multiresolution sampling procedure for analysis and synthesis of texture images. In: Proceedings of Computer Graphics, SIGGRAPH 1997, pp. 361–368. ACM, New York (1997)CrossRefGoogle Scholar
  7. 7.
    Efros, A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proc. ACM Conf. Comp. Graphics (SIGGRAPH), Eugene Fiume, August 2001, pp. 341–346 (2001)Google Scholar
  8. 8.
    Wei, L., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: Computer Graphics Proceedings, SIGGRAPH 2000. Conference Proceedings. Annual Conference Series, pp. 479–488. ACM, New York (2000)Google Scholar
  9. 9.
    Zalesny, A., Van Gool, L.: A compact model for viewpoint dependent texture synthesis. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A.W. (eds.) SMILE 2000. LNCS, vol. 2018, pp. 123–143. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., Werman, M.: Texture mixing and texture movie synthesis using statistical learning. IEEE Transactions on visualization and computer graphics 7(2), 120–135 (2001)CrossRefGoogle Scholar
  11. 11.
    Van Nevel, A.: Texture Synthesis via Matching First and Second Order Statistics of a Wavelet Frame Decomposition. In: Proceedings of the 1998 IEEE International Conference on Image Processing (ICIP 1998), Chicago, Illinois, ICIP (1), pp. 72–76 (1998)Google Scholar
  12. 12.
    Xu, Y., Zhu, S.C., Guo, B., Shum, H.Y.: Asymptotically admissible texture synthesis. In: Proceedings of Second International Workshop of Statistical and Computational Theories of Vision, Vancouver, Canada, pp. 1–22 (2001)Google Scholar
  13. 13.
    Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. on Information Theory 36, 961–1005 (1990)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 674–693 (1989)MATHCrossRefGoogle Scholar
  15. 15.
    Heriot-Watt University, PhoTex texture Lab, http://www.cee.hw.ac.uk/texturelab/database/photex
  16. 16.
    Brodatz, P.: Textures: A Photographic Album for Artists & Designers. Dover, New York (1966)Google Scholar
  17. 17.
    Dong, J., Chantler, M.: Estimating Parameters of Illumination models for the synthesis of 3D surface texture. In: Proceedings of the 2004 International Conference on Computer and Information Technology (September 2004)Google Scholar
  18. 18.
    Jian, M.-W., Dong, J.-Y., Wu, J.-H.: Image capture and fusion of 3d surface texture using wavelet transform. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, November 2-4, vol. 1, pp. 338–343 (2007)Google Scholar
  19. 19.
    Dong, J., Chantler, M.: On the relations between four methods for representing 3D surface textures under multiple illumination directions. In: Proceedings of the 2004 International Conference on Computer and Information Technology (September 2004)Google Scholar
  20. 20.
    Jian, M., Liu, S., Dong, J.: Fast Texture Synthesis Using Wavelet Coefficient Fitting. In: 2008 International Symposium on Intelligent Information Technology Application Workshops, pp. 491–495 (2008)Google Scholar
  21. 21.
    Jian, M., Liu, S., Dong, J.: 3D Surface Texture Synthesis Based on Wavelet Transform. In: International Symposium on Computer Science and Computational Technology, 20-22, vol. 2, pp. 230–233 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Muwei Jian
    • 1
  • Ningbo Hao
    • 2
  • Junyu Dong
    • 3
  • Rong Jiang
    • 4
  1. 1.School of Space Science and PhysicsShandong University at WeihaiWeihaiChina
  2. 2.International CollegeHuanghuai UniversityZhumadian, HenanChina
  3. 3.Department of Computer ScienceOcean University of ChinaQingdaoChina
  4. 4.Department of Information EngineeringWeihai Vocational CollegeWeihaiChina

Personalised recommendations