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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 HolsteinWe 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.
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