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Updated Poisson_reconstruction_function documentation with generate_reference_manual 1.3.1

This commit is contained in:
Laurent Saboret 2010-06-20 17:43:29 +00:00
parent c76930c46a
commit 80771679b3
2 changed files with 12 additions and 9 deletions
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15
Surface_reconstruction_points_3/doc_tex/Surface_reconstruction_points_3_ref/Poisson_reconstruction_function.tex
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@ -24,7 +24,7 @@
Given a set of 3D points with oriented normals sampled on the boundary of a 3D solid, the Poisson Surface Reconstruction method \cite{Kazhdan06} 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.
\ccc{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 and uses the TAUCS sparse linear solver.
\ccc{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.
%END-AUTO(\ccDefinition)
@ -40,8 +40,7 @@ Given a set of 3D points with oriented normals sampled on the boundary of a 3D s
% The section below is automatically generated. Do not edit!
%START-AUTO(\ccParameters)
template$<$ \\
class Gt$>$ \\
template$<$class Gt$>$ \\
class \ccc{Poisson_reconstruction_function};
\ccCommentHeading{Parameters} \\
@ -132,10 +131,14 @@ Creates a Poisson implicit function from the [first, beyond) range of points.
Returns a sphere bounding the inferred surface.
}
\ccGlue
\ccMethod{bool compute_implicit_function();}
\ccMethod{template<class SparseLinearAlgebraTraits_d> bool compute_implicit_function(SparseLinearAlgebraTraits_d solver = SparseLinearAlgebraTraits_d());}
{
The function \ccc{compute_implicit_function}() must be called after each 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. \\
Returns false if the linear solver fails.
The function \ccc{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.
\ccCommentHeading{Template parameters} \\
\ccc{SparseLinearAlgebraTraits_d}: Symmetric definite positive sparse linear solver. The default solver is TAUCS Multifrontal Supernodal Cholesky Factorization.
\ccCommentHeading{Returns} false if the linear solver fails.
\ccCommentHeading{Parameters} \\
\ccc{solver}: sparse linear solver.
}
\ccGlue
\ccMethod{FT operator()(const Point& p) const;}

6
Surface_reconstruction_points_3/include/CGAL/Poisson_reconstruction_function.h
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@ -195,7 +195,7 @@ public:
/// - 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.
///
/// @heading Parameters:
/// @commentheading Template parameters:
/// @param SparseLinearAlgebraTraits_d Symmetric definite positive sparse linear solver.
/// The default solver is TAUCS Multifrontal Supernodal Cholesky Factorization.
///
@ -309,7 +309,7 @@ private:
/// Poisson reconstruction.
/// Returns false on error.
///
/// @heading Parameters:
/// @commentheading Template parameters:
/// @param SparseLinearAlgebraTraits_d Symmetric definite positive sparse linear solver.
template <class SparseLinearAlgebraTraits_d>
bool solve_poisson(
@ -713,7 +713,7 @@ private:
/// Assemble vi's row of the linear system A*X=B
///
/// @heading Parameters:
/// @commentheading Template parameters:
/// @param SparseLinearAlgebraTraits_d Symmetric definite positive sparse linear solver.
template <class SparseLinearAlgebraTraits_d>
void assemble_poisson_row(typename SparseLinearAlgebraTraits_d::Matrix& A,