137 lines
6.2 KiB
C
137 lines
6.2 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 PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
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* principal surface curvatures for a given point cloud dataset containing points and normals.
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*
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* The recommended PointOutT is pcl::PrincipalCurvatures.
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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 NormalEstimationOMP for an example on how to extend this to parallel implementations.
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*
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* \author Radu B. Rusu, Jared Glover
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* \ingroup features
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*/
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template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
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class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
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{
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public:
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using Ptr = shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
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using ConstPtr = shared_ptr<const PrincipalCurvaturesEstimation<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 = pcl::PointCloud<PointInT>;
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/** \brief Empty constructor. */
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PrincipalCurvaturesEstimation () :
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xyz_centroid_ (Eigen::Vector3f::Zero ()),
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demean_ (Eigen::Vector3f::Zero ()),
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covariance_matrix_ (Eigen::Matrix3f::Zero ()),
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eigenvector_ (Eigen::Vector3f::Zero ()),
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eigenvalues_ (Eigen::Vector3f::Zero ())
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{
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feature_name_ = "PrincipalCurvaturesEstimation";
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};
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/** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
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* plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
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* along with both the max (pc1) and min (pc2) eigenvalues
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* \param[in] normals the point cloud normals
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* \param[in] p_idx the query point at which the least-squares plane was estimated
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* \param[in] indices the point cloud indices that need to be used
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* \param[out] pcx the principal curvature X direction
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* \param[out] pcy the principal curvature Y direction
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* \param[out] pcz the principal curvature Z direction
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* \param[out] pc1 the max eigenvalue of curvature
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* \param[out] pc2 the min eigenvalue of curvature
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*/
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void
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computePointPrincipalCurvatures (const pcl::PointCloud<PointNT> &normals,
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int p_idx, const pcl::Indices &indices,
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float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
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protected:
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/** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
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* and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
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* setSearchSurface () and the spatial locator in setSearchMethod ()
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* \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
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*/
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void
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computeFeature (PointCloudOut &output) override;
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private:
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/** \brief A pointer to the input dataset that contains the point normals of the XYZ dataset. */
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std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > projected_normals_;
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/** \brief SSE aligned placeholder for the XYZ centroid of a surface patch. */
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Eigen::Vector3f xyz_centroid_;
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/** \brief Temporary point placeholder. */
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Eigen::Vector3f demean_;
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/** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
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EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;
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/** \brief SSE aligned eigenvectors placeholder for a covariance matrix. */
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Eigen::Vector3f eigenvector_;
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/** \brief eigenvalues placeholder for a covariance matrix. */
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Eigen::Vector3f eigenvalues_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/features/impl/principal_curvatures.hpp>
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#endif
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