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