223 lines
10 KiB
C
223 lines
10 KiB
C
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/*
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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) 2010-2011, 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/point_types.h>
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#include <pcl/features/feature.h>
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#include <map>
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#include <queue> // for std::queue
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namespace pcl
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{
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/** \brief PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset
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* containing points and normals.
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*
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* A commonly used type for PointOutT is pcl::PFHSignature125.
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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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* - R.B. Rusu, N. Blodow, Z.C. Marton, M. Beetz.
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* Aligning Point Cloud Views using Persistent Feature Histograms.
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* In Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
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* Nice, France, September 22-26 2008.
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* - R.B. Rusu, Z.C. Marton, N. Blodow, M. Beetz.
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* Learning Informative Point Classes for the Acquisition of Object Model Maps.
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* In Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV),
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* Hanoi, Vietnam, December 17-20 2008.
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*
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* \attention
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* The convention for PFH features is:
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* - if a query point's nearest neighbors cannot be estimated, the PFH feature will be set to NaN
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* (not a number)
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* - it is impossible to estimate a PFH descriptor for a point that
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* doesn't have finite 3D coordinates. Therefore, any point that contains
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* NaN data on x, y, or z, will have its PFH feature property set to NaN.
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*
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* \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
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* \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
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*
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* \author Radu B. Rusu
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT = pcl::PFHSignature125>
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class PFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<PFHEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const PFHEstimation<PointInT, PointNT, PointOutT> >;
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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>::k_;
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using Feature<PointInT, PointOutT>::search_parameter_;
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using Feature<PointInT, PointOutT>::surface_;
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using Feature<PointInT, PointOutT>::input_;
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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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/** \brief Empty constructor.
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* Sets \a use_cache_ to false, \a nr_subdiv_ to 5, and the internal maximum cache size to 1GB.
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*/
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PFHEstimation () :
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nr_subdiv_ (5),
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d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI))),
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key_list_ (),
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// Default 1GB memory size. Need to set it to something more conservative.
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max_cache_size_ ((1ul*1024ul*1024ul*1024ul) / sizeof (std::pair<std::pair<int, int>, Eigen::Vector4f>)),
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use_cache_ (false)
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{
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feature_name_ = "PFHEstimation";
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};
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/** \brief Set the maximum internal cache size. Defaults to 2GB worth of entries.
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* \param[in] cache_size maximum cache size
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*/
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inline void
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setMaximumCacheSize (unsigned int cache_size)
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{
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max_cache_size_ = cache_size;
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}
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/** \brief Get the maximum internal cache size. */
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inline unsigned int
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getMaximumCacheSize ()
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{
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return (max_cache_size_);
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}
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/** \brief Set whether to use an internal cache mechanism for removing redundant calculations or not.
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*
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* \note Depending on how the point cloud is ordered and how the nearest
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* neighbors are estimated, using a cache could have a positive or a
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* negative influence. Please test with and without a cache on your
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* data, and choose whatever works best!
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*
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* See \ref setMaximumCacheSize for setting the maximum cache size
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*
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* \param[in] use_cache set to true to use the internal cache, false otherwise
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*/
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inline void
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setUseInternalCache (bool use_cache)
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{
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use_cache_ = use_cache;
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}
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/** \brief Get whether the internal cache is used or not for computing the PFH features. */
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inline bool
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getUseInternalCache ()
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{
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return (use_cache_);
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}
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/** \brief Compute the 4-tuple representation containing the three angles and one distance between two points
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* represented by Cartesian coordinates and normals.
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* \note For explanations about the features, please see the literature mentioned above (the order of the
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* features might be different).
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* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
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* \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud
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* \param[in] p_idx the index of the first point (source)
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* \param[in] q_idx the index of the second point (target)
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* \param[out] f1 the first angular feature (angle between the projection of nq_idx and u)
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* \param[out] f2 the second angular feature (angle between nq_idx and v)
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* \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
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* \param[out] f4 the distance feature (p_idx - q_idx)
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* \note For efficiency reasons, we assume that the point data passed to the method is finite.
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*/
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bool
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computePairFeatures (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4);
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/** \brief Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1, f2, f3)
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* features for a given point based on its spatial neighborhood of 3D points with normals
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* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
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* \param[in] normals the dataset containing the surface normals at each point in \a cloud
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* \param[in] indices the k-neighborhood point indices in the dataset
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* \param[in] nr_split the number of subdivisions for each angular feature interval
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* \param[out] pfh_histogram the resultant (combinatorial) PFH histogram representing the feature at the query point
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*/
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void
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computePointPFHSignature (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram);
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protected:
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/** \brief Estimate the Point Feature Histograms (PFH) descriptors 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[out] output the resultant point cloud model dataset that contains the PFH feature estimates
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*/
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void
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computeFeature (PointCloudOut &output) override;
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/** \brief The number of subdivisions for each angular feature interval. */
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int nr_subdiv_;
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/** \brief Placeholder for a point's PFH signature. */
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Eigen::VectorXf pfh_histogram_;
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/** \brief Placeholder for a PFH 4-tuple. */
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Eigen::Vector4f pfh_tuple_;
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/** \brief Placeholder for a histogram index. */
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int f_index_[3];
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/** \brief Float constant = 1.0 / (2.0 * M_PI) */
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float d_pi_;
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/** \brief Internal hashmap, used to optimize efficiency of redundant computations. */
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std::map<std::pair<int, int>, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator<std::pair<const std::pair<int, int>, Eigen::Vector4f> > > feature_map_;
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/** \brief Queue of pairs saved, used to constrain memory usage. */
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std::queue<std::pair<int, int> > key_list_;
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/** \brief Maximum size of internal cache memory. */
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unsigned int max_cache_size_;
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/** \brief Set to true to use the internal cache for removing redundant computations. */
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bool use_cache_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/pfh.hpp>
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#endif
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