316 lines
14 KiB
C
316 lines
14 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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#ifdef __SSE__
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#include <xmmintrin.h> // for __m128
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#endif // ifdef __SSE__
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#ifdef __AVX__
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#include <immintrin.h> // for __m256
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#endif // ifdef __AVX__
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#include <pcl/sample_consensus/sac_model.h>
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#include <pcl/sample_consensus/model_types.h>
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namespace pcl
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{
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/** \brief Project a point on a planar model given by a set of normalized coefficients
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* \param[in] p the input point to project
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* \param[in] model_coefficients the coefficients of the plane (a, b, c, d) that satisfy ax+by+cz+d=0
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* \param[out] q the resultant projected point
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*/
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template <typename Point> inline void
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projectPoint (const Point &p, const Eigen::Vector4f &model_coefficients, Point &q)
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{
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// Calculate the distance from the point to the plane
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Eigen::Vector4f pp (p.x, p.y, p.z, 1);
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// use normalized coefficients to calculate the scalar projection
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float distance_to_plane = pp.dot(model_coefficients);
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//TODO: Why doesn't getVector4Map work here?
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//Eigen::Vector4f q_e = q.getVector4fMap ();
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//q_e = pp - model_coefficients * distance_to_plane;
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Eigen::Vector4f q_e = pp - distance_to_plane * model_coefficients;
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q.x = q_e[0];
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q.y = q_e[1];
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q.z = q_e[2];
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}
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/** \brief Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0
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* \param p a point
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* \param a the normalized <i>a</i> coefficient of a plane
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* \param b the normalized <i>b</i> coefficient of a plane
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* \param c the normalized <i>c</i> coefficient of a plane
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* \param d the normalized <i>d</i> coefficient of a plane
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* \ingroup sample_consensus
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*/
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template <typename Point> inline double
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pointToPlaneDistanceSigned (const Point &p, double a, double b, double c, double d)
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{
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return (a * p.x + b * p.y + c * p.z + d);
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}
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/** \brief Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0
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* \param p a point
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* \param plane_coefficients the normalized coefficients (a, b, c, d) of a plane
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* \ingroup sample_consensus
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*/
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template <typename Point> inline double
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pointToPlaneDistanceSigned (const Point &p, const Eigen::Vector4f &plane_coefficients)
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{
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return ( plane_coefficients[0] * p.x + plane_coefficients[1] * p.y + plane_coefficients[2] * p.z + plane_coefficients[3] );
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}
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/** \brief Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0
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* \param p a point
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* \param a the normalized <i>a</i> coefficient of a plane
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* \param b the normalized <i>b</i> coefficient of a plane
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* \param c the normalized <i>c</i> coefficient of a plane
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* \param d the normalized <i>d</i> coefficient of a plane
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* \ingroup sample_consensus
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*/
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template <typename Point> inline double
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pointToPlaneDistance (const Point &p, double a, double b, double c, double d)
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{
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return (std::abs (pointToPlaneDistanceSigned (p, a, b, c, d)) );
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}
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/** \brief Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0
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* \param p a point
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* \param plane_coefficients the normalized coefficients (a, b, c, d) of a plane
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* \ingroup sample_consensus
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*/
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template <typename Point> inline double
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pointToPlaneDistance (const Point &p, const Eigen::Vector4f &plane_coefficients)
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{
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return ( std::abs (pointToPlaneDistanceSigned (p, plane_coefficients)) );
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** \brief SampleConsensusModelPlane defines a model for 3D plane segmentation.
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* The model coefficients are defined as:
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* - \b a : the X coordinate of the plane's normal (normalized)
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* - \b b : the Y coordinate of the plane's normal (normalized)
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* - \b c : the Z coordinate of the plane's normal (normalized)
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* - \b d : the fourth <a href="http://mathworld.wolfram.com/HessianNormalForm.html">Hessian component</a> of the plane's equation
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*
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* \author Radu B. Rusu
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* \ingroup sample_consensus
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*/
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template <typename PointT>
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class SampleConsensusModelPlane : public SampleConsensusModel<PointT>
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{
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public:
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using SampleConsensusModel<PointT>::model_name_;
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using SampleConsensusModel<PointT>::input_;
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using SampleConsensusModel<PointT>::indices_;
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using SampleConsensusModel<PointT>::error_sqr_dists_;
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using SampleConsensusModel<PointT>::isModelValid;
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using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
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using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
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using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
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using Ptr = shared_ptr<SampleConsensusModelPlane<PointT> >;
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using ConstPtr = shared_ptr<const SampleConsensusModelPlane<PointT>>;
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/** \brief Constructor for base SampleConsensusModelPlane.
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* \param[in] cloud the input point cloud dataset
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* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
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*/
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SampleConsensusModelPlane (const PointCloudConstPtr &cloud, bool random = false)
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: SampleConsensusModel<PointT> (cloud, random)
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{
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model_name_ = "SampleConsensusModelPlane";
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sample_size_ = 3;
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model_size_ = 4;
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}
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/** \brief Constructor for base SampleConsensusModelPlane.
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* \param[in] cloud the input point cloud dataset
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* \param[in] indices a vector of point indices to be used from \a cloud
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* \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
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*/
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SampleConsensusModelPlane (const PointCloudConstPtr &cloud,
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const Indices &indices,
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bool random = false)
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: SampleConsensusModel<PointT> (cloud, indices, random)
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{
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model_name_ = "SampleConsensusModelPlane";
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sample_size_ = 3;
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model_size_ = 4;
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}
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/** \brief Empty destructor */
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~SampleConsensusModelPlane () {}
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/** \brief Check whether the given index samples can form a valid plane model, compute the model coefficients from
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* these samples and store them internally in model_coefficients_. The plane coefficients are:
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* a, b, c, d (ax+by+cz+d=0)
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* \param[in] samples the point indices found as possible good candidates for creating a valid model
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* \param[out] model_coefficients the resultant model coefficients
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*/
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bool
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computeModelCoefficients (const Indices &samples,
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Eigen::VectorXf &model_coefficients) const override;
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/** \brief Compute all distances from the cloud data to a given plane model.
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* \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
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* \param[out] distances the resultant estimated distances
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*/
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void
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getDistancesToModel (const Eigen::VectorXf &model_coefficients,
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std::vector<double> &distances) const override;
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/** \brief Select all the points which respect the given model coefficients as inliers.
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* \param[in] model_coefficients the coefficients of a plane model that we need to compute distances to
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* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
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* \param[out] inliers the resultant model inliers
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*/
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void
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selectWithinDistance (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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Indices &inliers) override;
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/** \brief Count all the points which respect the given model coefficients as inliers.
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*
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* \param[in] model_coefficients the coefficients of a model that we need to compute distances to
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* \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
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* \return the resultant number of inliers
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*/
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std::size_t
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countWithinDistance (const Eigen::VectorXf &model_coefficients,
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const double threshold) const override;
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/** \brief Recompute the plane coefficients using the given inlier set and return them to the user.
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* @note: these are the coefficients of the plane model after refinement (e.g. after SVD)
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* \param[in] inliers the data inliers found as supporting the model
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* \param[in] model_coefficients the initial guess for the model coefficients
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* \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
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*/
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void
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optimizeModelCoefficients (const Indices &inliers,
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const Eigen::VectorXf &model_coefficients,
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Eigen::VectorXf &optimized_coefficients) const override;
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/** \brief Create a new point cloud with inliers projected onto the plane model.
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* \param[in] inliers the data inliers that we want to project on the plane model
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* \param[in] model_coefficients the *normalized* coefficients of a plane model
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* \param[out] projected_points the resultant projected points
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* \param[in] copy_data_fields set to true if we need to copy the other data fields
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*/
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void
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projectPoints (const Indices &inliers,
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const Eigen::VectorXf &model_coefficients,
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PointCloud &projected_points,
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bool copy_data_fields = true) const override;
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/** \brief Verify whether a subset of indices verifies the given plane model coefficients.
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* \param[in] indices the data indices that need to be tested against the plane model
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* \param[in] model_coefficients the plane model coefficients
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* \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
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*/
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bool
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doSamplesVerifyModel (const std::set<index_t> &indices,
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const Eigen::VectorXf &model_coefficients,
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const double threshold) const override;
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/** \brief Return a unique id for this model (SACMODEL_PLANE). */
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inline pcl::SacModel
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getModelType () const override { return (SACMODEL_PLANE); }
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protected:
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using SampleConsensusModel<PointT>::sample_size_;
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using SampleConsensusModel<PointT>::model_size_;
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/** This implementation uses no SIMD instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceStandard (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
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/** This implementation uses SSE, SSE2, and SSE4.1 instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceSSE (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#endif
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#if defined (__AVX__) && defined (__AVX2__)
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/** This implementation uses AVX and AVX2 instructions. It is not intended for normal use.
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* See countWithinDistance which automatically uses the fastest implementation.
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*/
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std::size_t
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countWithinDistanceAVX (const Eigen::VectorXf &model_coefficients,
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const double threshold,
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std::size_t i = 0) const;
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#endif
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#ifdef __AVX__
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inline __m256 dist8 (const std::size_t i, const __m256 &a_vec, const __m256 &b_vec, const __m256 &c_vec, const __m256 &d_vec, const __m256 &abs_help) const;
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#endif
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#ifdef __SSE__
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inline __m128 dist4 (const std::size_t i, const __m128 &a_vec, const __m128 &b_vec, const __m128 &c_vec, const __m128 &d_vec, const __m128 &abs_help) const;
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#endif
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private:
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/** \brief Check if a sample of indices results in a good sample of points
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* indices.
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* \param[in] samples the resultant index samples
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*/
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bool
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isSampleGood (const Indices &samples) const override;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/sample_consensus/impl/sac_model_plane.hpp>
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#endif
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