A fast method for implicit surface reconstruction based on radial basis functions network from 3D scattered points

International Journal of Neural Systems
Hanbo LiuWenyi Qiang

Abstract

A method for arbitrary surface reconstruction from 3D large scattered points is proposed in this paper. According to the properties of 3D points, e.g. the non-uniform distribution and unknown topology, an implicit surface model is adopted based on radial basis functions network. And because of the property of locality of radial basis function, the method is fast and robust in surface reconstruction. Furthermore, an adapted K-Means algorithm is used to reduce reconstruction centers. For features completeness, two effective methods are introduced to extract the feature points before the adapted K-Means algorithm. Experiment results show that the presented approach is a good solution for reconstruction from 3D large scattered points.

References

May 7, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Francesco Camastra, Alessandro Verri

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Citations

Sep 5, 2009·International Journal of Neural Systems·Samanwoy Ghosh-Dastidar, Hojjat Adeli
Sep 5, 2009·International Journal of Neural Systems·R SavithaN Sundararajan
Feb 25, 2010·International Journal of Neural Systems·Jun-Fei Qiao, Hong-Gui Han
Jun 18, 2010·International Journal of Neural Systems·Junping ZhangDewang Chen
Oct 15, 2010·International Journal of Neural Systems·Panagiotis PatrinosHaralambos Sarimveis
Oct 15, 2010·International Journal of Neural Systems·X LiL Rutkowski
May 19, 2009·Neural Networks : the Official Journal of the International Neural Network Society·Samanwoy Ghosh-Dastidar, Hojjat Adeli
Jun 9, 2009·Neural Networks : the Official Journal of the International Neural Network Society·Hojjat Adeli, Ashif Panakkat
Sep 17, 2010·Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society·Mehran AhmadlouAmir Adeli
Oct 22, 2009·Neural Computation·Vilson Luiz Dalle Mole, Aluizio Fausto Ribeiro Araújo

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