238 lines
11 KiB
C++
238 lines
11 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2009, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#pragma once
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#include <pcl/memory.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/features/feature.h>
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namespace pcl
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{
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/** \brief Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>).
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* Can be used to transform the 2D histograms obtained in \ref RSDEstimation into a point cloud.
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* @note The template parameter N should be (greater or) equal to the product of the number of rows and columns.
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* \param[in] histograms2D the list of neighborhood 2D histograms
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* \param[out] histogramsPC the dataset containing the linearized matrices
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* \ingroup features
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*/
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template <int N> void
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getFeaturePointCloud (const std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > &histograms2D, PointCloud<Histogram<N> > &histogramsPC)
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{
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histogramsPC.resize (histograms2D.size ());
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histogramsPC.width = histograms2D.size ();
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histogramsPC.height = 1;
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histogramsPC.is_dense = true;
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const int rows = histograms2D.at(0).rows();
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const int cols = histograms2D.at(0).cols();
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typename PointCloud<Histogram<N> >::VectorType::iterator it = histogramsPC.begin ();
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for (const Eigen::MatrixXf& h : histograms2D)
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{
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Eigen::Map<Eigen::MatrixXf> histogram (&(it->histogram[0]), rows, cols);
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histogram = h;
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++it;
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}
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}
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/** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
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* \param[in] surface the dataset containing the XYZ points
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* \param[in] normals the dataset containing the surface normals at each point in the dataset
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* \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
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* \param[in] max_dist the upper bound for the considered distance interval
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* \param[in] nr_subdiv the number of subdivisions for the considered distance interval
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* \param[in] plane_radius maximum radius, above which everything can be considered planar
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* \param[in] radii the output point of a type that should have r_min and r_max fields
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* \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT> Eigen::MatrixXf
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computeRSD (const pcl::PointCloud<PointInT> &surface, const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices, double max_dist,
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int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
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/** \brief Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals
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* \param[in] normals the dataset containing the surface normals at each point in the dataset
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* \param[in] indices the neighborhood point indices in the dataset (first point is used as the reference)
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* \param[in] sqr_dists the squared distances from the first to all points in the neighborhood
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* \param[in] max_dist the upper bound for the considered distance interval
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* \param[in] nr_subdiv the number of subdivisions for the considered distance interval
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* \param[in] plane_radius maximum radius, above which everything can be considered planar
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* \param[in] radii the output point of a type that should have r_min and r_max fields
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* \param[in] compute_histogram if not false, the full neighborhood histogram is provided, usable as a point signature
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* \ingroup features
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*/
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template <typename PointNT, typename PointOutT> Eigen::MatrixXf
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computeRSD (const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices, const std::vector<float> &sqr_dists, double max_dist,
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int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram = false);
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/** \brief @b RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves)
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* for a given point cloud dataset containing points and normals.
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*
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* @note If you use this code in any academic work, please cite:
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*
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* <ul>
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* <li> Z.C. Marton , D. Pangercic , N. Blodow , J. Kleinehellefort, M. Beetz
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* General 3D Modelling of Novel Objects from a Single View
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* In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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* Taipei, Taiwan, October 18-22, 2010
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* </li>
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* <li> Z.C. Marton, D. Pangercic, N. Blodow, Michael Beetz.
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* Combined 2D-3D Categorization and Classification for Multimodal Perception Systems.
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* In The International Journal of Robotics Research, Sage Publications
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* pages 1378--1402, Volume 30, Number 11, September 2011.
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* </li>
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* </ul>
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*
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* @note The code is stateful as we do not expect this class to be multicore parallelized.
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* \author Zoltan-Csaba Marton
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT>
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class RSDEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Feature<PointInT, PointOutT>::feature_name_;
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using Feature<PointInT, PointOutT>::getClassName;
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using Feature<PointInT, PointOutT>::indices_;
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using Feature<PointInT, PointOutT>::search_radius_;
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using Feature<PointInT, PointOutT>::search_parameter_;
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using Feature<PointInT, PointOutT>::surface_;
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using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
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using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
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using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
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using Ptr = shared_ptr<RSDEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const RSDEstimation<PointInT, PointNT, PointOutT> >;
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/** \brief Empty constructor. */
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RSDEstimation () : nr_subdiv_ (5), plane_radius_ (0.2), save_histograms_ (false)
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{
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feature_name_ = "RadiusSurfaceDescriptor";
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};
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/** \brief Set the number of subdivisions for the considered distance interval.
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* \param[in] nr_subdiv the number of subdivisions
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*/
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inline void
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setNrSubdivisions (int nr_subdiv) { nr_subdiv_ = nr_subdiv; }
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/** \brief Get the number of subdivisions for the considered distance interval.
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* \return the number of subdivisions
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*/
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inline int
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getNrSubdivisions () const { return (nr_subdiv_); }
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/** \brief Set the maximum radius, above which everything can be considered planar.
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* \note the order of magnitude should be around 10-20 times the search radius (0.2 works well for typical datasets).
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* \note on accurate 3D data (e.g. openni sernsors) a search radius as low as 0.01 still gives good results.
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* \param[in] plane_radius the new plane radius
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*/
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inline void
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setPlaneRadius (double plane_radius) { plane_radius_ = plane_radius; }
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/** \brief Get the maximum radius, above which everything can be considered planar.
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* \return the plane_radius used
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*/
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inline double
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getPlaneRadius () const { return (plane_radius_); }
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/** \brief Disables the setting of the number of k nearest neighbors to use for the feature estimation. */
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inline void
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setKSearch (int)
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{
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PCL_ERROR ("[pcl::%s::setKSearch] RSD does not work with k nearest neighbor search. Use setRadiusSearch() instead!\n", getClassName ().c_str ());
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}
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/** \brief Set whether the full distance-angle histograms should be saved.
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* @note Obtain the list of histograms by getHistograms ()
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* \param[in] save set to true if histograms should be saved
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*/
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inline void
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setSaveHistograms (bool save) { save_histograms_ = save; }
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/** \brief Returns whether the full distance-angle histograms are being saved.
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* \return true if the histograms are being be saved
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*/
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inline bool
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getSaveHistograms () const { return (save_histograms_); }
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/** \brief Returns a pointer to the list of full distance-angle histograms for all points.
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* \return the histogram being saved when computing RSD
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*/
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inline shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > >
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getHistograms () const { return (histograms_); }
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protected:
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/** \brief Estimate the estimates the Radius-based Surface Descriptor (RSD) at a set of points given by
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* <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
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* setSearchMethod ()
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* \param output the resultant point cloud model dataset that contains the RSD feature estimates (r_min and r_max values)
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*/
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void
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computeFeature (PointCloudOut &output) override;
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/** \brief The list of full distance-angle histograms for all points. */
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shared_ptr<std::vector<Eigen::MatrixXf, Eigen::aligned_allocator<Eigen::MatrixXf> > > histograms_;
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private:
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/** \brief The number of subdivisions for the considered distance interval. */
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int nr_subdiv_;
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/** \brief The maximum radius, above which everything can be considered planar. */
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double plane_radius_;
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/** \brief Signals whether the full distance-angle histograms are being saved. */
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bool save_histograms_;
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/rsd.hpp>
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#endif
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