LS-RBF network based 3D surface reconstruction method

We propose a new method for surface reconstruction from scattered point set based on least square radial basis function network in this paper. The RBF network is trained by fewer samples and we can get the weights of this network. Then an implicit continuous function is constructed to represent a 3D...

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Bibliographic Details
Main Authors: Wen, P.Z., Wu, X.J., Zhu, Y., Peng, X.W.
Format: Conference Proceeding
Language:English
Subjects:
Binary trees
Electronic mail
Filtering
Interpolation
Least squares methods
Neural network
Neural networks
point filtering
Radial basis function
Radial basis function networks
Reconstruction algorithms
Scattering
Surface reconstruction
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Summary:We propose a new method for surface reconstruction from scattered point set based on least square radial basis function network in this paper. The RBF network is trained by fewer samples and we can get the weights of this network. Then an implicit continuous function is constructed to represent a 3D model. In this method, a binary tree is used to efficiently traversal the data set. Our scheme can overcome the numerical ill-conditioning of coefficient matrix and over-fitting problem. Some examples are presented to show the effectiveness of out algorithm in 2D and 3D cases. The numerical experiment shows high efficiency and satisfactory visual quality.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2009.5195232