Generating and Updating Textures for a Large-Scale Environment

  • Jinhui Hu
  • Suya You
  • Ulrich Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


With the rapid development of sensor and modeling technologies, it becomes increasingly feasible to model a large-scale environment. However, the acquisition and updating of textures for such a large-scale environment is still a challenging task, often demanding tedious and time-consuming manual interactions. This paper presents new techniques to generate high quality textures for given rough urban building models by automatic camera calibration and pose recovery, and to continuously update these textures in real time using videos as a texture resource. A number of static textures are generated for a university campus size model, and these textures are dynamically updated using videos in real time, which demonstrate the effectiveness of our algorithms.


Video Stream Graphic Hardware Static Texture Texture Synthesis Mosaic Image 
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.


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  1. 1.
    You, S., Hu, J., Neumann, U., Fox, P.: Urban site modeling from LiDAR, CGGM (2003)Google Scholar
  2. 2.
    Lee, S.C., Jung, S.K., Nevatia, R.: Integrating ground and aerial views for urban site modeling. In: ICPR (2002)Google Scholar
  3. 3.
    Portilla, J., Simoncelli, E.P.: A parametric texture model-based on joint statistics of complex wavelet coefficients. IJCV 40(1), 49–70 (2000)MATHCrossRefGoogle Scholar
  4. 4.
    Efros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: ICCV, pp. 1033–1038 (1999)Google Scholar
  5. 5.
    Efros, A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of SIGGRAPH, pp. 341–346 (2001)Google Scholar
  6. 6.
    Fruh, C., Zakhor, A.: Constructing 3D city models by merging aerial and ground views. CGA 23(6), 52–61 (2003)Google Scholar
  7. 7.
    Stamos, I., Allen, P.: Automatic registration of 2D with 3D imagery in urban environments. In: ICCV, pp. 731–736 (2001)Google Scholar
  8. 8.
    Schodl, A., et al.: Video textures. In: Proceedings of SIGGRAPH, pp. 489–498 (2000)Google Scholar
  9. 9.
    O’Rourke: Art gallery theorems and algorithms. Oxford Univ. Press, New York (1987)MATHGoogle Scholar
  10. 10.
    Cipolla, R., Drummond, T., Robertson, D.P.: Camera calibration from vanishing points in images of architectural scenes. In: BMVC, pp. 382–391 (1999)Google Scholar
  11. 11.
    Hu, J.: Automatic pose recovery for high-quality textures generation. USC Technique Re-port 06-874 (2006)Google Scholar
  12. 12.
    Mark, S., et al.: Fast shadows and lighting effects using texture mapping. In: Siggraph (1978)Google Scholar
  13. 13.
    Neumann, U., et al.: Augmented virtual environments (AVE): dynamic fusion of imagery and 3D models. IEEE Virtual Reality, 61–67 (2003)Google Scholar
  14. 14.
    Hu, J., You, S., Neumann, U.: Texture painting from video. Journal of WSCG 13, 119–125 (2005)Google Scholar
  15. 15.
    Debevec, P., Taylor, C., Malik, J.: Modeling and rendering architecture from photo-graphs: a hybrid geometry and image based approach. In: Proc. of SIGGRAPH (1996)Google Scholar
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jinhui Hu
    • 1
  • Suya You
    • 1
  • Ulrich Neumann
    • 1
  1. 1.University of Southern California 

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