3D Object Surface Reconstruction Using Growing Self-organised Networks

  • Carmen Alonso-Montes
  • Manuel Francisco González Penedo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

This paper studies the adaptation of growing self-organised neural networks for 3D object surface reconstruction. Nowadays, input devices and filtering techniques obtain 3D point positions from the object surface without connectivity information. Growing self-organised networks can obtain the implicit surface mesh by means of a clustering process over the input data space maintaining at the same time the spatial-topology relations. The influence of using additional point features (e.g. gradient direction) as well as the methodology characterized in this paper have been studied to improve the obtained surface mesh.

Keywords

Neural networks Self-organised networks Growing cellstructures Growing neural gas 3D surface reconstruction Gradient direction 

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References

  1. 1.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface Reconstruction from Unorganized Points. ACM SIGGRAPH Computer Graphics 26, 71–78 (1992)CrossRefGoogle Scholar
  2. 2.
    Lorensen, W.E., Cline, H.E.: Marching Cubes: A High Resolution 3D Surface Construction Algorithm. ACM SIGGRAPH Computer Graphics 21, 163–169 (1987)CrossRefGoogle Scholar
  3. 3.
    Amenta, N., Bern, M.: Surface Reconstruction by Voronoi filtering. In: Proceedings of the 14th Annual Symposium on Computational Geometry, pp. 39–48 (1998)Google Scholar
  4. 4.
    Ivrissimtzis, I.P., Jeong, W.K., Seidel, H.P.: Using Growing Cell Structures for Surface Reconstruction. In: Proceedings of the Shape Modeling International 2003, pp. 78–86. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: The Self-organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  6. 6.
    Fritzke, B.: Kohonen Feature Maps and Growing Cell Structures–a Performance Comparison. In: Hanson, S.J., Cowan, J.D., Giles, C.L. (eds.) Advances in Neural Information Processing Systems 5, NIPS 1992, Denver, vol. 5, pp. 115–122 (1993)Google Scholar
  7. 7.
    Fritzke, B.: Growing Self-organizing Networks – Why? In: ESANN, European Symposium on Artificial Neural Networks, pp. 61–72 (1996)Google Scholar
  8. 8.
    Fritzke, B.: A Growing Neural Gas Network Learns Topology. Advances in Neural Information Processing Systems 7, 864–869 (1995)Google Scholar
  9. 9.
    Fritzke, B.: Growing Cell Structures – a Self-organizing Network for Unsupervised and Supervised Learning. Neural Networks 7, 1441–1460 (1994)CrossRefGoogle Scholar
  10. 10.
    Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision, 1st edn. Prentice Hall, Englewood Cliffs (1998)Google Scholar
  11. 11.
    Sato, Y., Nakajima, S., Nobuyuki, S., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional Multi-scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images. Medical Image Analysis 2, 143–168 (1998)CrossRefGoogle Scholar
  12. 12.
    Koller, M., Gerig, G., Székely, G., Dettwiler, D.: Multiscale Detection of Curvilinear Structures in 2-D and 3-D Image Data. In: 5th International Conference on Computer Vision, pp. 864–869 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Carmen Alonso-Montes
    • 1
  • Manuel Francisco González Penedo
    • 1
  1. 1.Dpto. de ComputaciónUniversidade da CoruñaA CoruñaSpain

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