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Class

CGAL::Poisson_reconstruction_function<GeomTraits>

Definition

Given a set of 3D points with oriented normals sampled on the boundary of a 3D solid, the Poisson Surface Reconstruction method [KBH06] solves for an approximate indicator function of the inferred solid, whose gradient best matches the input normals. The output scalar function, represented in an adaptive octree, is then iso-contoured using an adaptive marching cubes.

Poisson_reconstruction_function implements a variant of this algorithm which solves for a piecewise linear function on a 3D Delaunay triangulation instead of an adaptive octree.

#include <CGAL/Poisson_reconstruction_function.h>

Parameters

template<class Gt>
class Poisson_reconstruction_function;

Parameters


Gt: Geometric traits class.

Is Model for the Concepts

Model of the ImplicitFunction concept.

Types

Poisson_reconstruction_function<GeomTraits>::Geom_traits
Geometric traits class.

Poisson_reconstruction_function<GeomTraits>::FT
typedef to Geom_traits::FT

Poisson_reconstruction_function<GeomTraits>::Point
typedef to Geom_traits::Point_3

Poisson_reconstruction_function<GeomTraits>::Vector
typedef to Geom_traits::Vector_3

Poisson_reconstruction_function<GeomTraits>::Sphere
typedef to Geom_traits::Sphere_3

Creation

template<typename InputIterator, typename PointPMap, typename NormalPMap>
Poisson_reconstruction_function<GeomTraits> fct ( InputIterator first,
InputIterator beyond,
PointPMap point_pmap,
NormalPMap normal_pmap);
Creates a Poisson implicit function from the [first, beyond) range of points.
Template Parameters: 
InputIterator: iterator over input points. PointPMap: is a model of boost::ReadablePropertyMap with a value_type = Point_3. It can be omitted if InputIterator value_type is convertible to Point_3. NormalPMap: is a model of boost::ReadablePropertyMap with a value_type = Vector_3.
Parameters: 
first: iterator over the first input point. beyond: past-the-end iterator over the input points. point_pmap: property map to access the position of an input point. normal_pmap: property map to access the oriented normal of an input point.

Operations

Sphere fct.bounding_sphere () const Returns a sphere bounding the inferred surface.
template<class SparseLinearAlgebraTraits_d>
bool
fct.compute_implicit_function ( SparseLinearAlgebraTraits_d solver = SparseLinearAlgebraTraits_d())
The function compute_implicit_function() must be called after the insertion of oriented points. It computes the piecewise linear scalar function operator() by: applying Delaunay refinement, solving for operator() at each vertex of the triangulation with a sparse linear solver, and shifting and orienting operator() such that it is 0 at all input points and negative inside the inferred surface.
Template parameters: 
SparseLinearAlgebraTraits_d: Symmetric definite positive sparse linear solver. If Eigen 3.1 (or greater) is available and CGAL_EIGEN3_ENABLED is defined, the default solver is Eigen::ConjugateGradient, otherwise, it is TAUCS Multifrontal Supernodal Cholesky Factorization.
Returns:  false if the linear solver fails.
Parameters: 
solver: sparse linear solver.
FT fct.operator() ( const Point& p) const
ImplicitFunction interface: evaluates the implicit function at a given 3D query point. The function compute_implicit_function must be called before the first call to operator().
Point fct.get_inner_point () const Returns an arbitray point located inside the inferred surface.

Example

See Surface_reconstruction_points_3/poisson_reconstruction_example.cpp.

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CGAL Open Source Project. Release 4.0. 11 March 2012.