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/