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Adaptive point-cloud surface interpretation

conference contribution
posted on 10.02.2016, 12:46 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.

History

School

  • Science

Department

  • Computer Science

Published in

24th Computer Graphics International Conference ADVANCES IN COMPUTER GRAPHICS, PROCEEDINGS

Volume

4035

Pages

430 - 441 (12)

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

Publisher

Springer Verlag Berlin

Version

NA (Not Applicable or Unknown)

Publisher statement

This 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/

Publication date

2006

Notes

This paper is closed access.

ISBN

354035638X;9783540356394;9783540356387

ISSN

0302-9743

Book series

Lecture Notes in Computer Science;4035

Language

en

Location

Hangzhou, PEOPLES R CHINA

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