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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Alonso-Montes, C., González Penedo, M.F. (2004). 3D Object Surface Reconstruction Using Growing Self-organised Networks. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_20
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DOI: https://doi.org/10.1007/978-3-540-30463-0_20
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