| // SPDX-License-Identifier: LGPL-2.1-or-later | |
| /*************************************************************************** | |
| * 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 * | |
| * * | |
| ***************************************************************************/ | |
| 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 | |