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)


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.


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


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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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