243 lines
8.9 KiB
C
243 lines
8.9 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 <random>
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#include <pcl/point_types.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 ShapeContext3DEstimation implements the 3D shape context descriptor as
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* described in:
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* - Andrea Frome, Daniel Huber, Ravi Kolluri and Thomas Bülow, Jitendra Malik
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* Recognizing Objects in Range Data Using Regional Point Descriptors,
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* In proceedings of the 8th European Conference on Computer Vision (ECCV),
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* Prague, May 11-14, 2004
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*
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* The suggested PointOutT is pcl::ShapeContext1980
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*
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* \attention
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* The convention for a 3D shape context descriptor is:
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* - if a query point's nearest neighbors cannot be estimated, the feature descriptor will be set to NaN (not a number), and the RF to 0
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* - it is impossible to estimate a 3D shape context descriptor for a
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* point that doesn't have finite 3D coordinates. Therefore, any point
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* that contains NaN data on x, y, or z, will have its boundary feature
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* property set to NaN.
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*
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* \author Alessandro Franchi, Samuele Salti, Federico Tombari (original code)
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* \author Nizar Sallem (port to PCL)
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT = pcl::ShapeContext1980>
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class ShapeContext3DEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<ShapeContext3DEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const ShapeContext3DEstimation<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>::search_parameter_;
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using Feature<PointInT, PointOutT>::search_radius_;
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using Feature<PointInT, PointOutT>::surface_;
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using Feature<PointInT, PointOutT>::input_;
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using Feature<PointInT, PointOutT>::searchForNeighbors;
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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 Constructor.
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* \param[in] random If true the random seed is set to current time, else it is
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* set to 12345 prior to computing the descriptor (used to select X axis)
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*/
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ShapeContext3DEstimation (bool random = false) :
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radii_interval_(0),
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theta_divisions_(0),
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phi_divisions_(0),
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volume_lut_(0),
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azimuth_bins_(12),
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elevation_bins_(11),
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radius_bins_(15),
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min_radius_(0.1),
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point_density_radius_(0.2),
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descriptor_length_ (),
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rng_dist_ (0.0f, 1.0f)
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{
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feature_name_ = "ShapeContext3DEstimation";
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search_radius_ = 2.5;
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// Create a random number generator object
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if (random)
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{
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std::random_device rd;
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rng_.seed (rd());
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}
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else
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rng_.seed (12345u);
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}
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~ShapeContext3DEstimation() {}
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//inline void
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//setAzimuthBins (std::size_t bins) { azimuth_bins_ = bins; }
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/** \return the number of bins along the azimuth */
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inline std::size_t
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getAzimuthBins () { return (azimuth_bins_); }
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//inline void
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//setElevationBins (std::size_t bins) { elevation_bins_ = bins; }
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/** \return The number of bins along the elevation */
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inline std::size_t
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getElevationBins () { return (elevation_bins_); }
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//inline void
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//setRadiusBins (std::size_t bins) { radius_bins_ = bins; }
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/** \return The number of bins along the radii direction */
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inline std::size_t
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getRadiusBins () { return (radius_bins_); }
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/** \brief The minimal radius value for the search sphere (rmin) in the original paper
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* \param[in] radius the desired minimal radius
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*/
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inline void
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setMinimalRadius (double radius) { min_radius_ = radius; }
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/** \return The minimal sphere radius */
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inline double
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getMinimalRadius () { return (min_radius_); }
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/** \brief This radius is used to compute local point density
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* density = number of points within this radius
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* \param[in] radius value of the point density search radius
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*/
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inline void
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setPointDensityRadius (double radius) { point_density_radius_ = radius; }
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/** \return The point density search radius */
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inline double
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getPointDensityRadius () { return (point_density_radius_); }
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protected:
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/** \brief Initialize computation by allocating all the intervals and the volume lookup table. */
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bool
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initCompute () override;
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/** \brief Estimate a descriptor for a given point.
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* \param[in] index the index of the point to estimate a descriptor for
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* \param[in] normals a pointer to the set of normals
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* \param[in] rf the reference frame
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* \param[out] desc the resultant estimated descriptor
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* \return true if the descriptor was computed successfully, false if there was an error
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* (e.g. the nearest neighbor didn't return any neighbors)
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*/
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bool
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computePoint (std::size_t index, const pcl::PointCloud<PointNT> &normals, float rf[9], std::vector<float> &desc);
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/** \brief Estimate the actual feature.
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* \param[out] output the resultant feature
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*/
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void
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computeFeature (PointCloudOut &output) override;
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/** \brief Values of the radii interval */
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std::vector<float> radii_interval_;
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/** \brief Theta divisions interval */
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std::vector<float> theta_divisions_;
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/** \brief Phi divisions interval */
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std::vector<float> phi_divisions_;
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/** \brief Volumes look up table */
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std::vector<float> volume_lut_;
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/** \brief Bins along the azimuth dimension */
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std::size_t azimuth_bins_;
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/** \brief Bins along the elevation dimension */
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std::size_t elevation_bins_;
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/** \brief Bins along the radius dimension */
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std::size_t radius_bins_;
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/** \brief Minimal radius value */
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double min_radius_;
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/** \brief Point density radius */
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double point_density_radius_;
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/** \brief Descriptor length */
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std::size_t descriptor_length_;
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/** \brief Random number generator algorithm. */
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std::mt19937 rng_;
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/** \brief Random number generator distribution. */
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std::uniform_real_distribution<float> rng_dist_;
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/* \brief Shift computed descriptor "L" times along the azimuthal direction
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* \param[in] block_size the size of each azimuthal block
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* \param[in] desc at input desc == original descriptor and on output it contains
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* shifted descriptor resized descriptor_length_ * azimuth_bins_
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*/
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//void
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//shiftAlongAzimuth (std::size_t block_size, std::vector<float>& desc);
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/** \brief Boost-based random number generator. */
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inline float
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rnd ()
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{
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return (rng_dist_ (rng_));
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}
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/3dsc.hpp>
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#endif
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