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Adaptive point-cloud surface interpretation
conference contributionposted on 2016-02-10, 12:46 authored by Qinggang MengQinggang Meng, Baihua LiBaihua Li, Horst Holstein
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.
- Computer Science
Published in24th Computer Graphics International Conference ADVANCES IN COMPUTER GRAPHICS, PROCEEDINGS
Pages430 - 441 (12)
CitationMENG, 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
PublisherSpringer Verlag Berlin
- NA (Not Applicable or Unknown)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper is closed access.
Book seriesLecture Notes in Computer Science;4035