/*************************************************************************** * Copyright (c) 2012 Werner Mayer * * * * 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 #include 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& 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& 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 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& 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& 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& normals); private: const Points::PointKernel& myPoints; Mesh::MeshObject& myMesh; }; } // namespace Reen #endif // REEN_SURFACETRIANGULATION_H