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) 2010-2011, 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 * $Id$ 00038 * 00039 */ 00040 00041 #ifndef PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_ 00042 #define PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_ 00043 00044 #include <pcl/features/normal_3d_omp.h> 00045 00046 /////////////////////////////////////////////////////////////////////////////////////////// 00047 template <typename PointInT, typename PointOutT> void 00048 pcl::NormalEstimationOMP<PointInT, PointOutT>::computeFeature (PointCloudOut &output) 00049 { 00050 // Allocate enough space to hold the results 00051 // \note This resize is irrelevant for a radiusSearch (). 00052 std::vector<int> nn_indices (k_); 00053 std::vector<float> nn_dists (k_); 00054 00055 output.is_dense = true; 00056 00057 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense 00058 if (input_->is_dense) 00059 { 00060 #ifdef _OPENMP 00061 #pragma omp parallel for shared (output) private (nn_indices, nn_dists) num_threads(threads_) 00062 #endif 00063 // Iterating over the entire index vector 00064 for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx) 00065 { 00066 if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0) 00067 { 00068 output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN (); 00069 00070 output.is_dense = false; 00071 continue; 00072 } 00073 00074 Eigen::Vector4f n; 00075 pcl::computePointNormal<PointInT> (*surface_, nn_indices, 00076 n, 00077 output.points[idx].curvature); 00078 00079 output.points[idx].normal_x = n[0]; 00080 output.points[idx].normal_y = n[1]; 00081 output.points[idx].normal_z = n[2]; 00082 00083 flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_, 00084 output.points[idx].normal[0], 00085 output.points[idx].normal[1], 00086 output.points[idx].normal[2]); 00087 } 00088 } 00089 else 00090 { 00091 #ifdef _OPENMP 00092 #pragma omp parallel for shared (output) private (nn_indices, nn_dists) num_threads(threads_) 00093 #endif 00094 // Iterating over the entire index vector 00095 for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx) 00096 { 00097 if (!isFinite ((*input_)[(*indices_)[idx]]) || 00098 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0) 00099 { 00100 output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN (); 00101 00102 output.is_dense = false; 00103 continue; 00104 } 00105 00106 Eigen::Vector4f n; 00107 pcl::computePointNormal<PointInT> (*surface_, nn_indices, 00108 n, 00109 output.points[idx].curvature); 00110 00111 output.points[idx].normal_x = n[0]; 00112 output.points[idx].normal_y = n[1]; 00113 output.points[idx].normal_z = n[2]; 00114 00115 flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_, 00116 output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2]); 00117 } 00118 } 00119 } 00120 00121 #define PCL_INSTANTIATE_NormalEstimationOMP(T,NT) template class PCL_EXPORTS pcl::NormalEstimationOMP<T,NT>; 00122 00123 #endif // PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_ 00124