214 lines
7.4 KiB
C++
214 lines
7.4 KiB
C++
/***************************************************************************
|
|
* Copyright (c) 2012 Werner Mayer <wmayer[at]users.sourceforge.net> *
|
|
* *
|
|
* This file is part of the FreeCAD CAx development system. *
|
|
* *
|
|
* This library is free software; you can redistribute it and/or *
|
|
* modify it under the terms of the GNU Library General Public *
|
|
* License as published by the Free Software Foundation; either *
|
|
* version 2 of the License, or (at your option) any later version. *
|
|
* *
|
|
* This library is distributed in the hope that it will be useful, *
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
|
|
* GNU Library General Public License for more details. *
|
|
* *
|
|
* You should have received a copy of the GNU Library General Public *
|
|
* License along with this library; see the file COPYING.LIB. If not, *
|
|
* write to the Free Software Foundation, Inc., 59 Temple Place, *
|
|
* Suite 330, Boston, MA 02111-1307, USA *
|
|
* *
|
|
***************************************************************************/
|
|
|
|
#ifndef REEN_SURFACETRIANGULATION_H
|
|
#define REEN_SURFACETRIANGULATION_H
|
|
|
|
#include <vector>
|
|
|
|
#include <Base/Vector3D.h>
|
|
|
|
|
|
namespace Points
|
|
{
|
|
class PointKernel;
|
|
}
|
|
namespace Mesh
|
|
{
|
|
class MeshObject;
|
|
}
|
|
namespace pcl
|
|
{
|
|
struct PolygonMesh;
|
|
}
|
|
|
|
namespace Reen
|
|
{
|
|
|
|
class MeshConversion
|
|
{
|
|
public:
|
|
static void convert(const pcl::PolygonMesh&, Mesh::MeshObject&);
|
|
};
|
|
|
|
class SurfaceTriangulation
|
|
{
|
|
public:
|
|
SurfaceTriangulation(const Points::PointKernel&, Mesh::MeshObject&);
|
|
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
|
|
* \param[in] k the number of k-nearest neighbors
|
|
*/
|
|
void perform(int ksearch);
|
|
/** \brief Pass the normals to the points given in the constructor.
|
|
* \param[in] normals the normals to the given points.
|
|
*/
|
|
void perform(const std::vector<Base::Vector3f>& normals);
|
|
|
|
/** \brief Set the multiplier of the nearest neighbor distance to obtain the final search radius
|
|
* for each point (this will make the algorithm adapt to different point densities in the
|
|
* cloud). \param[in] mu the multiplier
|
|
*/
|
|
inline void setMu(double mu)
|
|
{
|
|
this->mu = mu;
|
|
}
|
|
|
|
/** \brief Set the sphere radius that is to be used for determining the k-nearest neighbors used
|
|
* for triangulating. \param[in] radius the sphere radius that is to contain all k-nearest
|
|
* neighbors \note This distance limits the maximum edge length!
|
|
*/
|
|
inline void setSearchRadius(double radius)
|
|
{
|
|
this->searchRadius = radius;
|
|
}
|
|
|
|
private:
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
double mu;
|
|
double searchRadius;
|
|
};
|
|
|
|
class PoissonReconstruction
|
|
{
|
|
public:
|
|
PoissonReconstruction(const Points::PointKernel&, Mesh::MeshObject&);
|
|
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
|
|
* \param[in] k the number of k-nearest neighbors
|
|
*/
|
|
void perform(int ksearch = 5);
|
|
/** \brief Pass the normals to the points given in the constructor.
|
|
* \param[in] normals the normals to the given points.
|
|
*/
|
|
void perform(const std::vector<Base::Vector3f>& normals);
|
|
|
|
/** \brief Set the maximum depth of the tree that will be used for surface reconstruction.
|
|
* \note Running at depth d corresponds to solving on a voxel grid whose resolution is no larger
|
|
* than 2^d x 2^d x 2^d. Note that since the reconstructor adapts the octree to the sampling
|
|
* density, the specified reconstruction depth is only an upper bound. \param[in] depth the
|
|
* depth parameter
|
|
*/
|
|
inline void setDepth(int depth)
|
|
{
|
|
this->depth = depth;
|
|
}
|
|
|
|
/** \brief Set the depth at which a block Gauss-Seidel solver is used to solve the Laplacian
|
|
* equation \note Using this parameter helps reduce the memory overhead at the cost of a small
|
|
* increase in reconstruction time. (In practice, we have found that for reconstructions of
|
|
* depth 9 or higher a subdivide depth of 7 or 8 can greatly reduce the memory usage.)
|
|
* \param[in] solver_divide the given parameter value
|
|
*/
|
|
inline void setSolverDivide(int solverDivide)
|
|
{
|
|
this->solverDivide = solverDivide;
|
|
}
|
|
|
|
/** \brief Set the minimum number of sample points that should fall within an octree node as the
|
|
* octree construction is adapted to sampling density \note For noise-free samples, small values
|
|
* in the range [1.0 - 5.0] can be used. For more noisy samples, larger values in the range
|
|
* [15.0 - 20.0] may be needed to provide a smoother, noise-reduced, reconstruction. \param[in]
|
|
* samples_per_node the given parameter value
|
|
*/
|
|
inline void setSamplesPerNode(float samplesPerNode)
|
|
{
|
|
this->samplesPerNode = samplesPerNode;
|
|
}
|
|
|
|
private:
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
int depth;
|
|
int solverDivide;
|
|
float samplesPerNode;
|
|
};
|
|
|
|
class GridReconstruction
|
|
{
|
|
public:
|
|
GridReconstruction(const Points::PointKernel&, Mesh::MeshObject&);
|
|
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
|
|
* \param[in] k the number of k-nearest neighbors
|
|
*/
|
|
void perform(int ksearch = 5);
|
|
/** \brief Pass the normals to the points given in the constructor.
|
|
* \param[in] normals the normals to the given points.
|
|
*/
|
|
void perform(const std::vector<Base::Vector3f>& normals);
|
|
|
|
private:
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
};
|
|
|
|
class ImageTriangulation
|
|
{
|
|
public:
|
|
ImageTriangulation(int width, int height, const Points::PointKernel&, Mesh::MeshObject&);
|
|
void perform();
|
|
|
|
private:
|
|
int width, height;
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
};
|
|
|
|
class MarchingCubesRBF
|
|
{
|
|
public:
|
|
MarchingCubesRBF(const Points::PointKernel&, Mesh::MeshObject&);
|
|
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
|
|
* \param[in] k the number of k-nearest neighbors
|
|
*/
|
|
void perform(int ksearch = 5);
|
|
/** \brief Pass the normals to the points given in the constructor.
|
|
* \param[in] normals the normals to the given points.
|
|
*/
|
|
void perform(const std::vector<Base::Vector3f>& normals);
|
|
|
|
private:
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
};
|
|
|
|
class MarchingCubesHoppe
|
|
{
|
|
public:
|
|
MarchingCubesHoppe(const Points::PointKernel&, Mesh::MeshObject&);
|
|
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
|
|
* \param[in] k the number of k-nearest neighbors
|
|
*/
|
|
void perform(int ksearch = 5);
|
|
/** \brief Pass the normals to the points given in the constructor.
|
|
* \param[in] normals the normals to the given points.
|
|
*/
|
|
void perform(const std::vector<Base::Vector3f>& normals);
|
|
|
|
private:
|
|
const Points::PointKernel& myPoints;
|
|
Mesh::MeshObject& myMesh;
|
|
};
|
|
|
|
} // namespace Reen
|
|
|
|
#endif // REEN_SURFACETRIANGULATION_H
|