ICIP 2006, Atlanta, GA

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Paper Detail

Session:Stereoscopic and 3-D Processing
Time:Tuesday, October 10, 14:20 - 17:00
Presentation: Poster
Topic: Stereoscopic and 3-D Processing: 3D modeling & synthesis
Authors: B. Li; Manchester Metropolitan University 
 Q. Meng; University of Wales, Aberystwyth 
 H. Holstein; University of Wales, Aberystwyth 
Abstract: Range images of complex geometry presented by large point data sets almost always yield surface reconstruction imperfections. We propose a novel compact and complete mesh representation for non-uniformly sampled noisy range image data using an adaptive Radial Basis Function network. 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. Experiments confirm the performance advantages of the network when applied to 3D point-cloud surface reconstruction.