222 lines
11 KiB
C
222 lines
11 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/features/feature.h>
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namespace pcl
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{
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/** \brief FPFHEstimation estimates the <b>Fast Point Feature Histogram (FPFH)</b> descriptor for a given point
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* cloud dataset containing points and normals.
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*
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* A commonly used type for PointOutT is pcl::FPFHSignature33.
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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, M. Beetz.
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* Fast Point Feature Histograms (FPFH) for 3D Registration.
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* In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),
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* Kobe, Japan, May 12-17 2009.
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* - R.B. Rusu, A. Holzbach, N. Blodow, M. Beetz.
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* Fast Geometric Point Labeling using Conditional Random Fields.
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* In Proceedings of the 22nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
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* St. Louis, MO, USA, October 11-15 2009.
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*
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* \attention
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* The convention for FPFH features is:
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* - if a query point's nearest neighbors cannot be estimated, the FPFH feature will be set to NaN
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* (not a number)
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* - it is impossible to estimate a FPFH 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 FPFH 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::FPFHSignature33>
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class FPFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<FPFHEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const FPFHEstimation<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>::input_;
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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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/** \brief Empty constructor. */
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FPFHEstimation () :
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nr_bins_f1_ (11), nr_bins_f2_ (11), nr_bins_f3_ (11),
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d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI)))
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{
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feature_name_ = "FPFHEstimation";
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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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*/
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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 SPFH (Simple Point Feature Histograms) individual signatures of the three angular
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* (f1, f2, f3) 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] p_idx the index of the query point (source)
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* \param[in] row the index row in feature histogramms
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* \param[in] indices the k-neighborhood point indices in the dataset
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* \param[out] hist_f1 the resultant SPFH histogram for feature f1
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* \param[out] hist_f2 the resultant SPFH histogram for feature f2
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* \param[out] hist_f3 the resultant SPFH histogram for feature f3
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*/
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void
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computePointSPFHSignature (const pcl::PointCloud<PointInT> &cloud,
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const pcl::PointCloud<PointNT> &normals, pcl::index_t p_idx, int row,
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const pcl::Indices &indices,
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Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
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/** \brief Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH
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* (Fast Point Feature Histogram) for a given point based on its 3D spatial neighborhood
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* \param[in] hist_f1 the histogram feature vector of \a f1 values over the given patch
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* \param[in] hist_f2 the histogram feature vector of \a f2 values over the given patch
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* \param[in] hist_f3 the histogram feature vector of \a f3 values over the given patch
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* \param[in] indices the point indices of p_idx's k-neighborhood in the point cloud
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* \param[in] dists the distances from p_idx to all its k-neighbors
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* \param[out] fpfh_histogram the resultant FPFH histogram representing the feature at the query point
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*/
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void
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weightPointSPFHSignature (const Eigen::MatrixXf &hist_f1,
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const Eigen::MatrixXf &hist_f2,
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const Eigen::MatrixXf &hist_f3,
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const pcl::Indices &indices,
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const std::vector<float> &dists,
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Eigen::VectorXf &fpfh_histogram);
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/** \brief Set the number of subdivisions for each angular feature interval.
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* \param[in] nr_bins_f1 number of subdivisions for the first angular feature
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* \param[in] nr_bins_f2 number of subdivisions for the second angular feature
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* \param[in] nr_bins_f3 number of subdivisions for the third angular feature
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*/
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inline void
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setNrSubdivisions (int nr_bins_f1, int nr_bins_f2, int nr_bins_f3)
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{
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nr_bins_f1_ = nr_bins_f1;
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nr_bins_f2_ = nr_bins_f2;
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nr_bins_f3_ = nr_bins_f3;
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}
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/** \brief Get the number of subdivisions for each angular feature interval.
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* \param[out] nr_bins_f1 number of subdivisions for the first angular feature
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* \param[out] nr_bins_f2 number of subdivisions for the second angular feature
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* \param[out] nr_bins_f3 number of subdivisions for the third angular feature
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*/
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inline void
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getNrSubdivisions (int &nr_bins_f1, int &nr_bins_f2, int &nr_bins_f3)
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{
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nr_bins_f1 = nr_bins_f1_;
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nr_bins_f2 = nr_bins_f2_;
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nr_bins_f3 = nr_bins_f3_;
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}
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protected:
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/** \brief Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud
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* \param[out] spf_hist_lookup a lookup table for all the SPF feature indices
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* \param[out] hist_f1 the resultant SPFH histogram for feature f1
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* \param[out] hist_f2 the resultant SPFH histogram for feature f2
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* \param[out] hist_f3 the resultant SPFH histogram for feature f3
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*/
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void
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computeSPFHSignatures (std::vector<int> &spf_hist_lookup,
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Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
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/** \brief Estimate the Fast Point Feature Histograms (FPFH) 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 FPFH 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_bins_f1_, nr_bins_f2_, nr_bins_f3_;
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/** \brief Placeholder for the f1 histogram. */
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Eigen::MatrixXf hist_f1_;
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/** \brief Placeholder for the f2 histogram. */
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Eigen::MatrixXf hist_f2_;
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/** \brief Placeholder for the f3 histogram. */
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Eigen::MatrixXf hist_f3_;
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/** \brief Placeholder for a point's FPFH signature. */
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Eigen::VectorXf fpfh_histogram_;
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/** \brief Float constant = 1.0 / (2.0 * M_PI) */
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float d_pi_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/fpfh.hpp>
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#endif
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