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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20291

Title: Adaptive point-cloud surface interpretation
Authors: Meng, Qinggang
Li, Baihua
Holstein, Horst
Issue Date: 2006
Publisher: Springer Verlag Berlin
Citation: MENG, Q., LI, B. and HOLSTEIN, H., 2006. Adaptive point-cloud surface interpretation. IN: Nishita, T., Peng, Q. and Seidel, H.P. (eds). Advances in Computer Graphics: 24th Computer Graphics International Conference, CGI 2006, Hangzhou, China, June 26-28, 2006. Proceedings. Lecture Notes in Computer Science; 4035. Berlin: Springer-Verlag, pp.430-441
Series/Report no.: Lecture Notes in Computer Science;4035
Abstract: We present a novel adaptive radial basis function network to reconstruct smooth closed surfaces and complete meshes from nonuniformly sampled noisy range data. The network is established using a heuristic learning strategy. Neurons can be inserted, removed or updated iteratively, adapting to the complexity and distribution of the underlying data. This flexibility is particularly suited to highly variable spatial frequencies, and is conducive to data compression with network representations. In addition, a greedy neighbourhood Extended Kalman Filter learning method is investigated, leading to a significant reduction of computational cost in the training process with desired prediction accuracy. Experimental results demonstrate the performance advantages of compact network representation for surface reconstruction from large amount of non-uniformly sampled incomplete point-clouds.
Description: This paper is closed access.
Version: Closed access
DOI: 10.1007/11784203_37
URI: https://dspace.lboro.ac.uk/2134/20291
Publisher Link: http://dx.doi.org/10.1007/11784203_37
ISBN: 354035638X
ISSN: 0302-9743
Appears in Collections:Closed Access (Computer Science)

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