475 lines
18 KiB
C
475 lines
18 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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*/
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#pragma once
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#include <pcl/memory.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/point_cloud.h>
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#include <pcl/features/feature.h>
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#include <pcl/features/integral_image2D.h>
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namespace pcl
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{
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/** \brief Surface normal estimation on organized data using integral images.
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*
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* For detailed information about this method see:
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*
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* S. Holzer and R. B. Rusu and M. Dixon and S. Gedikli and N. Navab,
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* Adaptive Neighborhood Selection for Real-Time Surface Normal Estimation
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* from Organized Point Cloud Data Using Integral Images, IROS 2012.
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*
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* D. Holz, S. Holzer, R. B. Rusu, and S. Behnke (2011, July).
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* Real-Time Plane Segmentation using RGB-D Cameras. In Proceedings of
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* the 15th RoboCup International Symposium, Istanbul, Turkey.
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* http://www.ais.uni-bonn.de/~holz/papers/holz_2011_robocup.pdf
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*
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* \author Stefan Holzer
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*/
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template <typename PointInT, typename PointOutT>
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class IntegralImageNormalEstimation: public Feature<PointInT, PointOutT>
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{
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using Feature<PointInT, PointOutT>::input_;
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using Feature<PointInT, PointOutT>::feature_name_;
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using Feature<PointInT, PointOutT>::tree_;
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using Feature<PointInT, PointOutT>::k_;
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using Feature<PointInT, PointOutT>::indices_;
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public:
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using Ptr = shared_ptr<IntegralImageNormalEstimation<PointInT, PointOutT> >;
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using ConstPtr = shared_ptr<const IntegralImageNormalEstimation<PointInT, PointOutT> >;
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/** \brief Different types of border handling. */
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enum BorderPolicy
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{
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BORDER_POLICY_IGNORE,
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BORDER_POLICY_MIRROR
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};
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/** \brief Different normal estimation methods.
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* <ul>
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* <li><b>COVARIANCE_MATRIX</b> - creates 9 integral images to compute the normal for a specific point
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* from the covariance matrix of its local neighborhood.</li>
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* <li><b>AVERAGE_3D_GRADIENT</b> - creates 6 integral images to compute smoothed versions of
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* horizontal and vertical 3D gradients and computes the normals using the cross-product between these
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* two gradients.
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* <li><b>AVERAGE_DEPTH_CHANGE</b> - creates only a single integral image and computes the normals
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* from the average depth changes.
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* </ul>
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*/
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enum NormalEstimationMethod
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{
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COVARIANCE_MATRIX,
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AVERAGE_3D_GRADIENT,
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AVERAGE_DEPTH_CHANGE,
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SIMPLE_3D_GRADIENT
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};
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using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
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using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
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/** \brief Constructor */
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IntegralImageNormalEstimation ()
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: normal_estimation_method_(AVERAGE_3D_GRADIENT)
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, border_policy_ (BORDER_POLICY_IGNORE)
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, rect_width_ (0), rect_width_2_ (0), rect_width_4_ (0)
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, rect_height_ (0), rect_height_2_ (0), rect_height_4_ (0)
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, distance_threshold_ (0)
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, integral_image_DX_ (false)
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, integral_image_DY_ (false)
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, integral_image_depth_ (false)
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, integral_image_XYZ_ (true)
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, diff_x_ (nullptr)
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, diff_y_ (nullptr)
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, depth_data_ (nullptr)
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, distance_map_ (nullptr)
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, use_depth_dependent_smoothing_ (false)
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, max_depth_change_factor_ (20.0f*0.001f)
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, normal_smoothing_size_ (10.0f)
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, init_covariance_matrix_ (false)
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, init_average_3d_gradient_ (false)
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, init_simple_3d_gradient_ (false)
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, init_depth_change_ (false)
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, vpx_ (0.0f)
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, vpy_ (0.0f)
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, vpz_ (0.0f)
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, use_sensor_origin_ (true)
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{
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feature_name_ = "IntegralImagesNormalEstimation";
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tree_.reset ();
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k_ = 1;
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}
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/** \brief Destructor **/
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~IntegralImageNormalEstimation ();
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/** \brief Set the regions size which is considered for normal estimation.
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* \param[in] width the width of the search rectangle
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* \param[in] height the height of the search rectangle
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*/
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void
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setRectSize (const int width, const int height);
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/** \brief Sets the policy for handling borders.
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* \param[in] border_policy the border policy.
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*/
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void
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setBorderPolicy (const BorderPolicy border_policy)
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{
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border_policy_ = border_policy;
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}
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/** \brief Computes the normal at the specified position.
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* \param[in] pos_x x position (pixel)
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* \param[in] pos_y y position (pixel)
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* \param[in] point_index the position index of the point
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* \param[out] normal the output estimated normal
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*/
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void
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computePointNormal (const int pos_x, const int pos_y, const unsigned point_index, PointOutT &normal);
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/** \brief Computes the normal at the specified position with mirroring for border handling.
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* \param[in] pos_x x position (pixel)
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* \param[in] pos_y y position (pixel)
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* \param[in] point_index the position index of the point
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* \param[out] normal the output estimated normal
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*/
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void
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computePointNormalMirror (const int pos_x, const int pos_y, const unsigned point_index, PointOutT &normal);
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/** \brief The depth change threshold for computing object borders
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* \param[in] max_depth_change_factor the depth change threshold for computing object borders based on
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* depth changes
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*/
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void
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setMaxDepthChangeFactor (float max_depth_change_factor)
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{
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max_depth_change_factor_ = max_depth_change_factor;
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}
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/** \brief Set the normal smoothing size
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* \param[in] normal_smoothing_size factor which influences the size of the area used to smooth normals
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* (depth dependent if useDepthDependentSmoothing is true)
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*/
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void
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setNormalSmoothingSize (float normal_smoothing_size)
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{
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if (normal_smoothing_size <= 0)
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{
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PCL_ERROR ("[pcl::%s::setNormalSmoothingSize] Invalid normal smoothing size given! (%f). Allowed ranges are: 0 < N. Defaulting to %f.\n",
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feature_name_.c_str (), normal_smoothing_size, normal_smoothing_size_);
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return;
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}
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normal_smoothing_size_ = normal_smoothing_size;
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}
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/** \brief Set the normal estimation method. The current implemented algorithms are:
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* <ul>
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* <li><b>COVARIANCE_MATRIX</b> - creates 9 integral images to compute the normal for a specific point
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* from the covariance matrix of its local neighborhood.</li>
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* <li><b>AVERAGE_3D_GRADIENT</b> - creates 6 integral images to compute smoothed versions of
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* horizontal and vertical 3D gradients and computes the normals using the cross-product between these
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* two gradients.
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* <li><b>AVERAGE_DEPTH_CHANGE</b> - creates only a single integral image and computes the normals
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* from the average depth changes.
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* </ul>
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* \param[in] normal_estimation_method the method used for normal estimation
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*/
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void
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setNormalEstimationMethod (NormalEstimationMethod normal_estimation_method)
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{
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normal_estimation_method_ = normal_estimation_method;
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}
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/** \brief Set whether to use depth depending smoothing or not
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* \param[in] use_depth_dependent_smoothing decides whether the smoothing is depth dependent
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*/
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void
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setDepthDependentSmoothing (bool use_depth_dependent_smoothing)
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{
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use_depth_dependent_smoothing_ = use_depth_dependent_smoothing;
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}
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/** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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*/
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inline void
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setInputCloud (const typename PointCloudIn::ConstPtr &cloud) override
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{
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input_ = cloud;
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if (!cloud->isOrganized ())
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{
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PCL_ERROR ("[pcl::IntegralImageNormalEstimation::setInputCloud] Input dataset is not organized (height = 1).\n");
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return;
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}
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init_covariance_matrix_ = init_average_3d_gradient_ = init_depth_change_ = false;
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if (use_sensor_origin_)
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{
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vpx_ = input_->sensor_origin_.coeff (0);
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vpy_ = input_->sensor_origin_.coeff (1);
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vpz_ = input_->sensor_origin_.coeff (2);
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}
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// Initialize the correct data structure based on the normal estimation method chosen
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initData ();
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}
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/** \brief Returns a pointer to the distance map which was computed internally
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*/
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inline float*
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getDistanceMap ()
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{
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return (distance_map_);
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}
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/** \brief Set the viewpoint.
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* \param vpx the X coordinate of the viewpoint
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* \param vpy the Y coordinate of the viewpoint
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* \param vpz the Z coordinate of the viewpoint
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*/
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inline void
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setViewPoint (float vpx, float vpy, float vpz)
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{
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vpx_ = vpx;
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vpy_ = vpy;
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vpz_ = vpz;
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use_sensor_origin_ = false;
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}
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/** \brief Get the viewpoint.
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* \param [out] vpx x-coordinate of the view point
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* \param [out] vpy y-coordinate of the view point
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* \param [out] vpz z-coordinate of the view point
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* \note this method returns the currently used viewpoint for normal flipping.
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* If the viewpoint is set manually using the setViewPoint method, this method will return the set view point coordinates.
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* If an input cloud is set, it will return the sensor origin otherwise it will return the origin (0, 0, 0)
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*/
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inline void
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getViewPoint (float &vpx, float &vpy, float &vpz)
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{
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vpx = vpx_;
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vpy = vpy_;
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vpz = vpz_;
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}
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/** \brief sets whether the sensor origin or a user given viewpoint should be used. After this method, the
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* normal estimation method uses the sensor origin of the input cloud.
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* to use a user defined view point, use the method setViewPoint
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*/
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inline void
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useSensorOriginAsViewPoint ()
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{
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use_sensor_origin_ = true;
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if (input_)
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{
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vpx_ = input_->sensor_origin_.coeff (0);
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vpy_ = input_->sensor_origin_.coeff (1);
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vpz_ = input_->sensor_origin_.coeff (2);
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}
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else
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{
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vpx_ = 0;
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vpy_ = 0;
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vpz_ = 0;
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}
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}
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protected:
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/** \brief Computes the normal for the complete cloud or only \a indices_ if provided.
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* \param[out] output the resultant normals
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*/
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void
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computeFeature (PointCloudOut &output) override;
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/** \brief Computes the normal for the complete cloud.
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* \param[in] distance_map distance map
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* \param[in] bad_point constant given to invalid normal components
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* \param[out] output the resultant normals
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*/
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void
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computeFeatureFull (const float* distance_map, const float& bad_point, PointCloudOut& output);
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/** \brief Computes the normal for part of the cloud specified by \a indices_
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* \param[in] distance_map distance map
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* \param[in] bad_point constant given to invalid normal components
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* \param[out] output the resultant normals
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*/
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void
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computeFeaturePart (const float* distance_map, const float& bad_point, PointCloudOut& output);
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/** \brief Initialize the data structures, based on the normal estimation method chosen. */
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void
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initData ();
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private:
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/** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
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* \param point a given point
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* \param vp_x the X coordinate of the viewpoint
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* \param vp_y the X coordinate of the viewpoint
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* \param vp_z the X coordinate of the viewpoint
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* \param nx the resultant X component of the plane normal
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* \param ny the resultant Y component of the plane normal
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* \param nz the resultant Z component of the plane normal
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* \ingroup features
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*/
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inline void
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flipNormalTowardsViewpoint (const PointInT &point,
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float vp_x, float vp_y, float vp_z,
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float &nx, float &ny, float &nz)
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||
|
|
{
|
||
|
|
// See if we need to flip any plane normals
|
||
|
|
vp_x -= point.x;
|
||
|
|
vp_y -= point.y;
|
||
|
|
vp_z -= point.z;
|
||
|
|
|
||
|
|
// Dot product between the (viewpoint - point) and the plane normal
|
||
|
|
float cos_theta = (vp_x * nx + vp_y * ny + vp_z * nz);
|
||
|
|
|
||
|
|
// Flip the plane normal
|
||
|
|
if (cos_theta < 0)
|
||
|
|
{
|
||
|
|
nx *= -1;
|
||
|
|
ny *= -1;
|
||
|
|
nz *= -1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/** \brief The normal estimation method to use. Currently, 3 implementations are provided:
|
||
|
|
*
|
||
|
|
* - COVARIANCE_MATRIX
|
||
|
|
* - AVERAGE_3D_GRADIENT
|
||
|
|
* - AVERAGE_DEPTH_CHANGE
|
||
|
|
*/
|
||
|
|
NormalEstimationMethod normal_estimation_method_;
|
||
|
|
|
||
|
|
/** \brief The policy for handling borders. */
|
||
|
|
BorderPolicy border_policy_;
|
||
|
|
|
||
|
|
/** The width of the neighborhood region used for computing the normal. */
|
||
|
|
int rect_width_;
|
||
|
|
int rect_width_2_;
|
||
|
|
int rect_width_4_;
|
||
|
|
/** The height of the neighborhood region used for computing the normal. */
|
||
|
|
int rect_height_;
|
||
|
|
int rect_height_2_;
|
||
|
|
int rect_height_4_;
|
||
|
|
|
||
|
|
/** the threshold used to detect depth discontinuities */
|
||
|
|
float distance_threshold_;
|
||
|
|
|
||
|
|
/** integral image in x-direction */
|
||
|
|
IntegralImage2D<float, 3> integral_image_DX_;
|
||
|
|
/** integral image in y-direction */
|
||
|
|
IntegralImage2D<float, 3> integral_image_DY_;
|
||
|
|
/** integral image */
|
||
|
|
IntegralImage2D<float, 1> integral_image_depth_;
|
||
|
|
/** integral image xyz */
|
||
|
|
IntegralImage2D<float, 3> integral_image_XYZ_;
|
||
|
|
|
||
|
|
/** derivatives in x-direction */
|
||
|
|
float *diff_x_;
|
||
|
|
/** derivatives in y-direction */
|
||
|
|
float *diff_y_;
|
||
|
|
|
||
|
|
/** depth data */
|
||
|
|
float *depth_data_;
|
||
|
|
|
||
|
|
/** distance map */
|
||
|
|
float *distance_map_;
|
||
|
|
|
||
|
|
/** \brief Smooth data based on depth (true/false). */
|
||
|
|
bool use_depth_dependent_smoothing_;
|
||
|
|
|
||
|
|
/** \brief Threshold for detecting depth discontinuities */
|
||
|
|
float max_depth_change_factor_;
|
||
|
|
|
||
|
|
/** \brief */
|
||
|
|
float normal_smoothing_size_;
|
||
|
|
|
||
|
|
/** \brief True when a dataset has been received and the covariance_matrix data has been initialized. */
|
||
|
|
bool init_covariance_matrix_;
|
||
|
|
|
||
|
|
/** \brief True when a dataset has been received and the average 3d gradient data has been initialized. */
|
||
|
|
bool init_average_3d_gradient_;
|
||
|
|
|
||
|
|
/** \brief True when a dataset has been received and the simple 3d gradient data has been initialized. */
|
||
|
|
bool init_simple_3d_gradient_;
|
||
|
|
|
||
|
|
/** \brief True when a dataset has been received and the depth change data has been initialized. */
|
||
|
|
bool init_depth_change_;
|
||
|
|
|
||
|
|
/** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
|
||
|
|
* from NormalEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0. */
|
||
|
|
float vpx_, vpy_, vpz_;
|
||
|
|
|
||
|
|
/** whether the sensor origin of the input cloud or a user given viewpoint should be used.*/
|
||
|
|
bool use_sensor_origin_;
|
||
|
|
|
||
|
|
/** \brief This method should get called before starting the actual computation. */
|
||
|
|
bool
|
||
|
|
initCompute () override;
|
||
|
|
|
||
|
|
/** \brief Internal initialization method for COVARIANCE_MATRIX estimation. */
|
||
|
|
void
|
||
|
|
initCovarianceMatrixMethod ();
|
||
|
|
|
||
|
|
/** \brief Internal initialization method for AVERAGE_3D_GRADIENT estimation. */
|
||
|
|
void
|
||
|
|
initAverage3DGradientMethod ();
|
||
|
|
|
||
|
|
/** \brief Internal initialization method for AVERAGE_DEPTH_CHANGE estimation. */
|
||
|
|
void
|
||
|
|
initAverageDepthChangeMethod ();
|
||
|
|
|
||
|
|
/** \brief Internal initialization method for SIMPLE_3D_GRADIENT estimation. */
|
||
|
|
void
|
||
|
|
initSimple3DGradientMethod ();
|
||
|
|
|
||
|
|
public:
|
||
|
|
PCL_MAKE_ALIGNED_OPERATOR_NEW
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef PCL_NO_PRECOMPILE
|
||
|
|
#include <pcl/features/impl/integral_image_normal.hpp>
|
||
|
|
#endif
|