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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/11745

Title: Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks
Authors: Meng, Qinggang
Li, Baihua
Holstein, Horst
Liu, Yonghuai
Keywords: Surface parameterization
Point clouds
Adaptive sequential learning
Issue Date: 2013
Publisher: © Elsevier Ltd.
Citation: MENG, Q. ... et al., 2013. Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks. Pattern Recognition, 46 (8), 2013, pp. 2361–2375.
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.
Description: This article was published in the journal, Pattern Recognition [© Elsevier Ltd.] and is available at: http://www.sciencedirect.com/science/article/pii/S003132031300054X
Version: Accepted for publication
DOI: 10.1016/j.patcog.2013.01.017
URI: https://dspace.lboro.ac.uk/2134/11745
Publisher Link: http://dx.doi.org/10.1016/j.patcog.2013.01.017
ISSN: 0031-3203
Appears in Collections:Published Articles (Computer Science)

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