Title
Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks
Abstract
We propose a self-organizing Radial Basis Function (RBF) neural network method for parameterization of freeform surfaces from larger, noisy and unoriented point clouds. In particular, an adaptive sequential learning algorithm is presented for network construction from a single instance of point set. The adaptive learning allows neurons to be dynamically inserted and fully adjusted (e.g. their locations, widths and weights), according to mapping residuals and data point novelty associated to underlying geometry. Pseudo-neurons, exhibiting very limited contributions, can be removed through a pruning procedure. Additionally, a neighborhood extended Kalman filter (NEKF) was developed to significantly accelerate parameterization. Experimental results show that this adaptive learning enables effective capture of global low-frequency variations while preserving sharp local details, ultimately leading to accurate and compact parameterization, as characterized by a small number of neurons. Parameterization using the proposed RBF network provides simple, low cost and low storage solutions to many problems such as surface construction, re-sampling, hole filling, multiple level-of-detail meshing and data compression from unstructured and incomplete range data. Performance results are also presented for comparison.
Year
DOI
Venue
2013
10.1016/j.patcog.2013.01.017
Pattern Recognition
Keywords
Field
DocType
point-cloud freeform,unoriented point cloud,adaptive learning,proposed rbf network,incomplete range data,data compression,neural network method,network construction,compact parameterization,point set,adaptive sequential,point clouds
Extended Kalman filter,Radial basis function,Parametrization,Artificial intelligence,Point cloud,Data compression,Artificial neural network,Sequence learning,Adaptive learning,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
46
8
0031-3203
Citations 
PageRank 
References 
2
0.37
36
Authors
4
Name
Order
Citations
PageRank
Qinggang Meng127323.54
Baihua Li217621.71
H. Holstein3798.21
Yonghuai Liu467561.65