Inferring and Enforcing Geometrical Constraints on a 3D Model for Building Reconstruction

  • Franck Taillandier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

We present a method for inferring and enforcing geometrical constraints on an approximate 3D model for building reconstruction applications. Compared to previous works, this approach requires no user intervention for constraints definition, is inherently compliant with the detected constraints and handles over-constrained configurations. An iterative minimization is applied to search for the model subject to geometric constraints that minimizes the distance to the initial approximate model. Results on real data show the gain obtained by this algorithm.

Keywords

Geometrical Constraint Dependence Graph Aerial Image Horizontal Edge Constraint Graph 
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.

References

  1. 1.
    Shum, H.Y., Han, M., Szeliski, R.: Interactive construction of 3d models from panoramic mosaics. In: Conference on Computer Vision and Pattern Recognition, pp. 427–433 (1998)Google Scholar
  2. 2.
    Taillandier, F., Deriche, R.: Automatic buildings reconstruction from aerial images: a generic bayesian framework. In: Proceedings of the XXth ISPRS Congress, Istanbul, Turkey (2004)Google Scholar
  3. 3.
    Debevec, P., Taylor, C., Malik, J.: Modeling and rendering architecture from photographs: a hybrid geometry-and image-based approach. In: SIGGRAPH 1996, pp. 11–20 (1996)Google Scholar
  4. 4.
    Suveg, I., Vosselman, G.: 3D reconstruction of building models. In: Proceedings of the XIXth ISPRS Congress, Amsterdam, vol. 33, B3, pp. 538–545 (2000)Google Scholar
  5. 5.
    Fischer, A., Kolbe, T., Lang, F., Cremers, A., Förstner, W., Plümer, L., Steinhage, V.: Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D. Computer Vision and Image Understanding 72, 163–185 (1998)CrossRefGoogle Scholar
  6. 6.
    Jaynes, C., Riseman, E., Hanson, A.: Recognition and reconstruction of buldings from multiple aerial images. Computer Vision and Image Understanding 90, 68–98 (2003)CrossRefGoogle Scholar
  7. 7.
    Noronha, S., Nevatia, R.: Detection and modeling of buildings from multiple aerial images. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 501–518 (2001)CrossRefGoogle Scholar
  8. 8.
    Kim, Z., Huertas, A., Nevatia, R.: Automatic description of buildings with complex rooftops from multiple images. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 272–279 (2001)Google Scholar
  9. 9.
    Grossmann, E., Santos-Victor, J.: Maximum likelihood 3d reconstruction from one or more images under geometric constraints. In: British Machine Vision Conference (2002)Google Scholar
  10. 10.
    Bondyfalat, D.: Interaction entre Symbolique et Numérique; Application à la Vision Artificielle. Thèse de doctorat, Univ de Nice Sophia-Antipolis (2000)Google Scholar
  11. 11.
    van den Heuvel, F.A.: A line-photogrammetric mathematical model for the reconstruction of polyhedral objects. In: Videometrics VI, SPIE, vol. 3641, pp. 60–71 (1999)Google Scholar
  12. 12.
    Ameri, B., Fritsch, D.: Automatic 3D building reconstruction using plane-roof structures. In: ASPRS, Washington DC (2000)Google Scholar
  13. 13.
    Hoffmann, C., Vermeer, P.: Geometric constraint solving in R 2 and R 3. In: Computing in Euclidean Geometry, 2nd edn., pp. 266–298. World Scientific Publishing, Singapore (1995)Google Scholar
  14. 14.
    Grossmann, E.: Maximum Likelihood 3D Reconstruction From One or More Uncalibrated Views Under Geometric Constraints. PhD thesis, Universidade Tecnica de Lisboa (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Franck Taillandier
    • 1
  1. 1.Institut Géographique NationalSaint-MandéFrance

Personalised recommendations