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User manual

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Simon Giraudot 2021-05-10 13:44:20 +02:00
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18
Point_set_processing_3/doc/Point_set_processing_3/Point_set_processing_3.txt
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@ -654,13 +654,15 @@ Four simplification functions are devised to reduce an input point set.
Function `random_simplify_point_set()` randomly deletes a
user-specified fraction of points from the input point set. This
algorithm is fast.
algorithm is the fastest.
Function `grid_simplify_point_set()` considers a regular grid
covering the bounding box of the input point set, and clusters all
points sharing the same cell of the grid by picking as representant
one arbitrarily chosen point. This algorithm is slower than
`random_simplify_point_set()`.
Function `grid_simplify_point_set()` considers a regular grid covering
the bounding box of the input point set, and clusters all points
sharing the same cell of the grid by picking as representant one
arbitrarily chosen point: representants may only be retained in cells
that have more than a user-defined minimum number of points in order
to also filter out low density areas and outliers. This algorithm is
still fast, although slower than `random_simplify_point_set()`.
Function `hierarchy_simplify_point_set()` provides an adaptive
simplification of the point set through local clusters
@ -675,7 +677,9 @@ the Weighted Locally Optimal Projection (WLOP) algorithm \cgalCite{wlop-2009}.
\subsection Point_set_processing_3Example_grid_simplification Grid Simplification Example
The following example reads a point set and simplifies it by clustering.
The following example reads a point set and simplifies it by
clustering. To filter out outliers, at least 3 points must lie in a
cell so that a point from this cell is kept.
\cgalExample{Point_set_processing_3/grid_simplification_example.cpp}
\cgalFigureBegin{Point_set_processing_3figgrid_simplification,grid_simplification.jpg}