Detail publikace

# Feature preserving mesh smoothing algorithm based on local normal covariance

ŠVUB, M. KRŠEK, P. ŠPANĚL, M. ŠTANCL, V. BARTOŇ, R. VAĎURA, J.

Originální název

Feature preserving mesh smoothing algorithm based on local normal covariance

Anglický název

Feature preserving mesh smoothing algorithm based on local normal covariance

Jazyk

en

Originální abstrakt

Our goal is to develop a smoothing algorithm, which would be feature preserving and simple to use without the need of extensive parameter tuning. Our method does the smoothing of vertices based on local neighbourhood character, which is modeled by a covariance matrix of neighbourhood triangle normals. The eigenvalues and eigenvectors of the covariance matrix are used for local weighting of the displacement vector of laplacian operator. This way the method is locally auto-tuned.

Anglický abstrakt

Our goal is to develop a smoothing algorithm, which would be feature preserving and simple to use without the need of extensive parameter tuning. Our method does the smoothing of vertices based on local neighbourhood character, which is modeled by a covariance matrix of neighbourhood triangle normals. The eigenvalues and eigenvectors of the covariance matrix are used for local weighting of the displacement vector of laplacian operator. This way the method is locally auto-tuned.

Dokumenty

BibTex

``````
@inproceedings{BUT34648,
author="Miroslav {Švub} and Přemysl {Kršek} and Michal {Španěl} and Vít {Štancl} and Radek {Bartoň} and Jiří {Vaďura}",
title="Feature preserving mesh smoothing algorithm based on local normal covariance",
annote="Our goal is to develop a smoothing algorithm, which would be feature preserving
and simple to use without the need of extensive parameter tuning. Our method does
the smoothing of vertices based on local neighbourhood character, which is
modeled by a covariance matrix of neighbourhood triangle normals. The eigenvalues
and eigenvectors of the covariance matrix are used for local weighting of the
displacement vector of laplacian operator. This way the method is locally
auto-tuned.",
address="University of West Bohemia in Pilsen",
booktitle="Proceedings of WSCG'10",
chapter="34648",
edition="17-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision",
howpublished="print",
institution="University of West Bohemia in Pilsen",
year="2010",
month="january",
pages="1--6",
publisher="University of West Bohemia in Pilsen",
type="conference paper"
}``````