/* * Software License Agreement (BSD License) * * Copyright (c) 2010, Willow Garage, Inc. * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of Willow Garage, Inc. nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * */ #pragma once #include #include // for OctreePointCloudSearch namespace pcl { /** \brief ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given * point cloud. This class is based on a particular implementation made by Federico * Tombari and Samuele Salti and it has been explicitly adapted to PCL. * * For more information about the original ISS detector, see: * *\par * Yu Zhong, “Intrinsic shape signatures: A shape descriptor for 3D object recognition,” * Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on , * vol., no., pp.689-696, Sept. 27 2009-Oct. 4 2009 * * Code example: * * \code * pcl::PointCloud::Ptr model (new pcl::PointCloud ());; * pcl::PointCloud::Ptr model_keypoints (new pcl::PointCloud ()); * pcl::search::KdTree::Ptr tree (new pcl::search::KdTree ()); * * // Fill in the model cloud * * double model_resolution; * * // Compute model_resolution * * pcl::ISSKeypoint3D iss_detector; * * iss_detector.setSearchMethod (tree); * iss_detector.setSalientRadius (6 * model_resolution); * iss_detector.setNonMaxRadius (4 * model_resolution); * iss_detector.setThreshold21 (0.975); * iss_detector.setThreshold32 (0.975); * iss_detector.setMinNeighbors (5); * iss_detector.setNumberOfThreads (4); * iss_detector.setInputCloud (model); * iss_detector.compute (*model_keypoints); * \endcode * * \author Gioia Ballin * \ingroup keypoints */ template class ISSKeypoint3D : public Keypoint { public: using Ptr = shared_ptr >; using ConstPtr = shared_ptr >; using PointCloudIn = typename Keypoint::PointCloudIn; using PointCloudOut = typename Keypoint::PointCloudOut; using PointCloudN = pcl::PointCloud; using PointCloudNPtr = typename PointCloudN::Ptr; using PointCloudNConstPtr = typename PointCloudN::ConstPtr; using OctreeSearchIn = pcl::octree::OctreePointCloudSearch; using OctreeSearchInPtr = typename OctreeSearchIn::Ptr; using Keypoint::name_; using Keypoint::input_; using Keypoint::surface_; using Keypoint::tree_; using Keypoint::search_radius_; using Keypoint::search_parameter_; using Keypoint::keypoints_indices_; /** \brief Constructor. * \param[in] salient_radius the radius of the spherical neighborhood used to compute the scatter matrix. */ ISSKeypoint3D (double salient_radius = 0.0001) : salient_radius_ (salient_radius) , non_max_radius_ (0.0) , normal_radius_ (0.0) , border_radius_ (0.0) , gamma_21_ (0.975) , gamma_32_ (0.975) , third_eigen_value_ (nullptr) , edge_points_ (nullptr) , min_neighbors_ (5) , normals_ (new pcl::PointCloud) , angle_threshold_ (static_cast (M_PI) / 2.0f) , threads_ (0) { name_ = "ISSKeypoint3D"; search_radius_ = salient_radius_; } /** \brief Destructor. */ ~ISSKeypoint3D () { delete[] third_eigen_value_; delete[] edge_points_; } /** \brief Set the radius of the spherical neighborhood used to compute the scatter matrix. * \param[in] salient_radius the radius of the spherical neighborhood */ void setSalientRadius (double salient_radius); /** \brief Set the radius for the application of the non maxima supression algorithm. * \param[in] non_max_radius the non maxima suppression radius */ void setNonMaxRadius (double non_max_radius); /** \brief Set the radius used for the estimation of the surface normals of the input cloud. If the radius is * too large, the temporal performances of the detector may degrade significantly. * \param[in] normal_radius the radius used to estimate surface normals */ void setNormalRadius (double normal_radius); /** \brief Set the radius used for the estimation of the boundary points. If the radius is too large, * the temporal performances of the detector may degrade significantly. * \param[in] border_radius the radius used to compute the boundary points */ void setBorderRadius (double border_radius); /** \brief Set the upper bound on the ratio between the second and the first eigenvalue. * \param[in] gamma_21 the upper bound on the ratio between the second and the first eigenvalue */ void setThreshold21 (double gamma_21); /** \brief Set the upper bound on the ratio between the third and the second eigenvalue. * \param[in] gamma_32 the upper bound on the ratio between the third and the second eigenvalue */ void setThreshold32 (double gamma_32); /** \brief Set the minimum number of neighbors that has to be found while applying the non maxima suppression algorithm. * \param[in] min_neighbors the minimum number of neighbors required */ void setMinNeighbors (int min_neighbors); /** \brief Set the normals if pre-calculated normals are available. * \param[in] normals the given cloud of normals */ void setNormals (const PointCloudNConstPtr &normals); /** \brief Set the decision boundary (angle threshold) that marks points as boundary or regular. * (default \f$\pi / 2.0\f$) * \param[in] angle the angle threshold */ inline void setAngleThreshold (float angle) { angle_threshold_ = angle; } /** \brief Initialize the scheduler and set the number of threads to use. * \param[in] nr_threads the number of hardware threads to use (0 sets the value back to automatic) */ inline void setNumberOfThreads (unsigned int nr_threads = 0) { threads_ = nr_threads; } protected: /** \brief Compute the boundary points for the given input cloud. * \param[in] input the input cloud * \param[in] border_radius the radius used to compute the boundary points * \param[in] angle_threshold the decision boundary that marks the points as boundary * \return the vector of boolean values in which the information about the boundary points is stored */ bool* getBoundaryPoints (PointCloudIn &input, double border_radius, float angle_threshold); /** \brief Compute the scatter matrix for a point index. * \param[in] current_index the index of the point * \param[out] cov_m the point scatter matrix */ void getScatterMatrix (const int ¤t_index, Eigen::Matrix3d &cov_m); /** \brief Perform the initial checks before computing the keypoints. * \return true if all the checks are passed, false otherwise */ bool initCompute () override; /** \brief Detect the keypoints by performing the EVD of the scatter matrix. * \param[out] output the resultant cloud of keypoints */ void detectKeypoints (PointCloudOut &output) override; /** \brief The radius of the spherical neighborhood used to compute the scatter matrix.*/ double salient_radius_; /** \brief The non maxima suppression radius. */ double non_max_radius_; /** \brief The radius used to compute the normals of the input cloud. */ double normal_radius_; /** \brief The radius used to compute the boundary points of the input cloud. */ double border_radius_; /** \brief The upper bound on the ratio between the second and the first eigenvalue returned by the EVD. */ double gamma_21_; /** \brief The upper bound on the ratio between the third and the second eigenvalue returned by the EVD. */ double gamma_32_; /** \brief Store the third eigen value associated to each point in the input cloud. */ double *third_eigen_value_; /** \brief Store the information about the boundary points of the input cloud. */ bool *edge_points_; /** \brief Minimum number of neighbors that has to be found while applying the non maxima suppression algorithm. */ int min_neighbors_; /** \brief The cloud of normals related to the input surface. */ PointCloudNConstPtr normals_; /** \brief The decision boundary (angle threshold) that marks points as boundary or regular. (default \f$\pi / 2.0\f$) */ float angle_threshold_; /** \brief The number of threads that has to be used by the scheduler. */ unsigned int threads_; }; } #include