1 /* 2 * Software License Agreement (BSD License) 3 * 4 * Point Cloud Library (PCL) - www.pointclouds.org 5 * Copyright (c) 2012, Yani Ioannou <yani.ioannou@gmail.com> 6 * Copyright (c) 2012-, Open Perception, Inc. 7 * 8 * All rights reserved. 9 * 10 * Redistribution and use in source and binary forms, with or without 11 * modification, are permitted provided that the following conditions 12 * are met: 13 * 14 * * Redistributions of source code must retain the above copyright 15 * notice, this list of conditions and the following disclaimer. 16 * * Redistributions in binary form must reproduce the above 17 * copyright notice, this list of conditions and the following 18 * disclaimer in the documentation and/or other materials provided 19 * with the distribution. 20 * * Neither the name of the copyright holder(s) nor the names of its 21 * contributors may be used to endorse or promote products derived 22 * from this software without specific prior written permission. 23 * 24 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 25 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 26 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 27 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 28 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 29 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 30 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 31 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 32 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 33 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 34 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 35 * POSSIBILITY OF SUCH DAMAGE. 36 * 37 */ 38 39 #pragma once 40 41 #include <pcl/features/feature.h> 42 43 namespace pcl 44 { 45 /** \brief A Difference of Normals (DoN) scale filter implementation for point cloud data. 46 * 47 * For each point in the point cloud two normals estimated with a differing search radius (sigma_s, sigma_l) 48 * are subtracted, the difference of these normals provides a scale-based feature which 49 * can be further used to filter the point cloud, somewhat like the Difference of Guassians 50 * in image processing, but instead on surfaces. Best results are had when the two search 51 * radii are related as sigma_l=10*sigma_s, the octaves between the two search radii 52 * can be though of as a filter bandwidth. For appropriate values and thresholds it 53 * can be used for surface edge extraction. 54 * 55 * \attention The input normals given by setInputNormalsSmall and setInputNormalsLarge have 56 * to match the input point cloud given by setInputCloud. This behavior is different than 57 * feature estimation methods that extend FeatureFromNormals, which match the normals 58 * with the search surface. 59 * 60 * \note For more information please see 61 * <b>Yani Ioannou. Automatic Urban Modelling using Mobile Urban LIDAR Data. 62 * Thesis (Master, Computing), Queen's University, March, 2010.</b> 63 * 64 * \author Yani Ioannou. 65 * \ingroup features 66 */ 67 template <typename PointInT, typename PointNT, typename PointOutT> 68 class DifferenceOfNormalsEstimation : public Feature<PointInT, PointOutT> 69 { 70 using Feature<PointInT, PointOutT>::getClassName; 71 using Feature<PointInT, PointOutT>::feature_name_; 72 using PCLBase<PointInT>::input_; 73 using PointCloudN = pcl::PointCloud<PointNT>; 74 using PointCloudNPtr = typename PointCloudN::Ptr; 75 using PointCloudNConstPtr = typename PointCloudN::ConstPtr; 76 using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut; 77 public: 78 using Ptr = shared_ptr<DifferenceOfNormalsEstimation<PointInT, PointNT, PointOutT> >; 79 using ConstPtr = shared_ptr<const DifferenceOfNormalsEstimation<PointInT, PointNT, PointOutT> >; 80 81 /** 82 * Creates a new Difference of Normals filter. 83 */ DifferenceOfNormalsEstimation()84 DifferenceOfNormalsEstimation () 85 { 86 feature_name_ = "DifferenceOfNormalsEstimation"; 87 } 88 ~DifferenceOfNormalsEstimation()89 ~DifferenceOfNormalsEstimation () 90 { 91 // 92 } 93 94 /** 95 * Set the normals calculated using a smaller search radius (scale) for the DoN operator. 96 * @param normals the smaller radius (scale) of the DoN filter. 97 */ 98 inline void setNormalScaleSmall(const PointCloudNConstPtr & normals)99 setNormalScaleSmall (const PointCloudNConstPtr &normals) 100 { 101 input_normals_small_ = normals; 102 } 103 104 /** 105 * Set the normals calculated using a larger search radius (scale) for the DoN operator. 106 * @param normals the larger radius (scale) of the DoN filter. 107 */ 108 inline void setNormalScaleLarge(const PointCloudNConstPtr & normals)109 setNormalScaleLarge (const PointCloudNConstPtr &normals) 110 { 111 input_normals_large_ = normals; 112 } 113 114 /** 115 * Computes the DoN vector for each point in the input point cloud and outputs the vector cloud to the given output. 116 * @param output the cloud to output the DoN vector cloud to. 117 */ 118 void 119 computeFeature (PointCloudOut &output) override; 120 121 /** 122 * Initialize for computation of features. 123 * @return true if parameters (input normals, input) are sufficient to perform computation. 124 */ 125 bool 126 initCompute () override; 127 private: 128 /** \brief Make the compute (&PointCloudOut); inaccessible from outside the class 129 * \param[out] output the output point cloud 130 */ 131 void compute(PointCloudOut &)132 compute (PointCloudOut &) {} 133 134 ///The smallest radius (scale) used in the DoN filter. 135 PointCloudNConstPtr input_normals_small_; 136 ///The largest radius (scale) used in the DoN filter. 137 PointCloudNConstPtr input_normals_large_; 138 }; 139 } 140 141 #ifdef PCL_NO_PRECOMPILE 142 #include <pcl/features/impl/don.hpp> 143 #endif 144