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Clement Jamin 2014-11-04 22:51:10 +01:00
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Point_set_processing_3/doc/Point_set_processing_3/Point_set_processing_3.txt
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@ -143,8 +143,7 @@ one arbitrarily chosen point. This algorithm is slower than
Function `wlop_simplify_and_regularize_point_set()` not only simplifies, Function `wlop_simplify_and_regularize_point_set()` not only simplifies,
but also regularizes downsampled points. This is an implementation of but also regularizes downsampled points. This is an implementation of
the WLOP (Weighted Locally Optimal Projection) algorithm the Weighted Locally Optimal Projection (WLOP) algorithm \cgalCite{wlop-2009}.
<a href="http://web.siat.ac.cn/~huihuang/WLOP/WLOP_page.html">[Huang et al. 2009]</a>.
\subsection Point_set_processing_3Example_3 Grid simplification example \subsection Point_set_processing_3Example_3 Grid simplification example
@ -194,7 +193,8 @@ projecting each point onto a smooth parametric surface patch
Function `bilateral_smooth_point_set()` smooths the input point set by Function `bilateral_smooth_point_set()` smooths the input point set by
iteratively projecting each point onto the implicit surface patch fitted over its `k` nearest neighbors. iteratively projecting each point onto the implicit surface patch fitted over its `k` nearest neighbors.
Bilateral projection preserves sharp features according to the normal (gradient) information. Normals are thus required as input. See formula (2) and (4) in paper <a href="http://web.siat.ac.cn/~huihuang/EAR/EAR_page.html">[Huang et al. 2013]</a>. Bilateral projection preserves sharp features according to the normal (gradient) information.
Normals are thus required as input. See formula (2) and (4) in \cgalCite{ear-2013}.
\subsection Point_set_processing_3Example_5 Jet smoothing example \subsection Point_set_processing_3Example_5 Jet smoothing example