Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010, Willow Garage, Inc. 00006 * Copyright (c) 2012-, Open Perception, Inc. 00007 * 00008 * All rights reserved. 00009 * 00010 * Redistribution and use in source and binary forms, with or without 00011 * modification, are permitted provided that the following conditions 00012 * are met: 00013 * 00014 * * Redistributions of source code must retain the above copyright 00015 * notice, this list of conditions and the following disclaimer. 00016 * * Redistributions in binary form must reproduce the above 00017 * copyright notice, this list of conditions and the following 00018 * disclaimer in the documentation and/or other materials provided 00019 * with the distribution. 00020 * * Neither the name of the copyright holder(s) nor the names of its 00021 * contributors may be used to endorse or promote products derived 00022 * from this software without specific prior written permission. 00023 * 00024 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00025 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00026 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00027 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00028 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00029 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00030 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00031 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00032 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00033 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00034 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00035 * POSSIBILITY OF SUCH DAMAGE. 00036 * 00037 */ 00038 00039 #ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_ 00040 #define PCL_SURFACE_IMPL_MARCHING_CUBES_RBF_H_ 00041 00042 #include <pcl/surface/marching_cubes_rbf.h> 00043 #include <pcl/common/common.h> 00044 #include <pcl/common/vector_average.h> 00045 #include <pcl/Vertices.h> 00046 #include <pcl/kdtree/kdtree_flann.h> 00047 00048 ////////////////////////////////////////////////////////////////////////////////////////////// 00049 template <typename PointNT> 00050 pcl::MarchingCubesRBF<PointNT>::MarchingCubesRBF () 00051 : MarchingCubes<PointNT> (), 00052 off_surface_epsilon_ (0.1f) 00053 { 00054 } 00055 00056 ////////////////////////////////////////////////////////////////////////////////////////////// 00057 template <typename PointNT> 00058 pcl::MarchingCubesRBF<PointNT>::~MarchingCubesRBF () 00059 { 00060 } 00061 00062 ////////////////////////////////////////////////////////////////////////////////////////////// 00063 template <typename PointNT> void 00064 pcl::MarchingCubesRBF<PointNT>::voxelizeData () 00065 { 00066 // Initialize data structures 00067 unsigned int N = static_cast<unsigned int> (input_->size ()); 00068 Eigen::MatrixXd M (2*N, 2*N), 00069 d (2*N, 1); 00070 00071 for (unsigned int row_i = 0; row_i < 2*N; ++row_i) 00072 { 00073 // boolean variable to determine whether we are in the off_surface domain for the rows 00074 bool row_off = (row_i >= N) ? 1 : 0; 00075 for (unsigned int col_i = 0; col_i < 2*N; ++col_i) 00076 { 00077 // boolean variable to determine whether we are in the off_surface domain for the columns 00078 bool col_off = (col_i >= N) ? 1 : 0; 00079 M (row_i, col_i) = kernel (Eigen::Vector3f (input_->points[col_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[col_i%N].getNormalVector3fMap ()).cast<double> () * col_off * off_surface_epsilon_, 00080 Eigen::Vector3f (input_->points[row_i%N].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[row_i%N].getNormalVector3fMap ()).cast<double> () * row_off * off_surface_epsilon_); 00081 } 00082 00083 d (row_i, 0) = row_off * off_surface_epsilon_; 00084 } 00085 00086 // Solve for the weights 00087 Eigen::MatrixXd w (2*N, 1); 00088 00089 // Solve_linear_system (M, d, w); 00090 w = M.fullPivLu ().solve (d); 00091 00092 std::vector<double> weights (2*N); 00093 std::vector<Eigen::Vector3d> centers (2*N); 00094 for (unsigned int i = 0; i < N; ++i) 00095 { 00096 centers[i] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> (); 00097 centers[i + N] = Eigen::Vector3f (input_->points[i].getVector3fMap ()).cast<double> () + Eigen::Vector3f (input_->points[i].getNormalVector3fMap ()).cast<double> () * off_surface_epsilon_; 00098 weights[i] = w (i, 0); 00099 weights[i + N] = w (i + N, 0); 00100 } 00101 00102 for (int x = 0; x < res_x_; ++x) 00103 for (int y = 0; y < res_y_; ++y) 00104 for (int z = 0; z < res_z_; ++z) 00105 { 00106 Eigen::Vector3d point; 00107 point[0] = min_p_[0] + (max_p_[0] - min_p_[0]) * float (x) / float (res_x_); 00108 point[1] = min_p_[1] + (max_p_[1] - min_p_[1]) * float (y) / float (res_y_); 00109 point[2] = min_p_[2] + (max_p_[2] - min_p_[2]) * float (z) / float (res_z_); 00110 00111 double f = 0.0; 00112 std::vector<double>::const_iterator w_it (weights.begin()); 00113 for (std::vector<Eigen::Vector3d>::const_iterator c_it = centers.begin (); 00114 c_it != centers.end (); ++c_it, ++w_it) 00115 f += *w_it * kernel (*c_it, point); 00116 00117 grid_[x * res_y_*res_z_ + y * res_z_ + z] = float (f); 00118 } 00119 } 00120 00121 ////////////////////////////////////////////////////////////////////////////////////////////// 00122 template <typename PointNT> double 00123 pcl::MarchingCubesRBF<PointNT>::kernel (Eigen::Vector3d c, Eigen::Vector3d x) 00124 { 00125 double r = (x - c).norm (); 00126 return (r * r * r); 00127 } 00128 00129 #define PCL_INSTANTIATE_MarchingCubesRBF(T) template class PCL_EXPORTS pcl::MarchingCubesRBF<T>; 00130 00131 #endif // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_ 00132