232 lines
8.7 KiB
C++
232 lines
8.7 KiB
C++
/*
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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/features/pfh.h>
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#include <pcl/features/pfh_tools.h> // for computePairFeatures
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#include <pcl/common/point_tests.h> // for pcl::isFinite
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> bool
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pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePairFeatures (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
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{
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pcl::computePairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
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cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
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f1, f2, f3, f4);
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePointPFHSignature (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
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{
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int h_index, h_p;
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// Clear the resultant point histogram
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pfh_histogram.setZero ();
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// Factorization constant
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float hist_incr = 100.0f / static_cast<float> (indices.size () * (indices.size () - 1) / 2);
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std::pair<int, int> key;
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bool key_found = false;
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// Iterate over all the points in the neighborhood
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for (std::size_t i_idx = 0; i_idx < indices.size (); ++i_idx)
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{
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for (std::size_t j_idx = 0; j_idx < i_idx; ++j_idx)
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{
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// If the 3D points are invalid, don't bother estimating, just continue
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if (!isFinite (cloud[indices[i_idx]]) || !isFinite (cloud[indices[j_idx]]))
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continue;
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if (use_cache_)
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{
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// In order to create the key, always use the smaller index as the first key pair member
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int p1, p2;
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// if (indices[i_idx] >= indices[j_idx])
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// {
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p1 = indices[i_idx];
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p2 = indices[j_idx];
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// }
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// else
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// {
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// p1 = indices[j_idx];
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// p2 = indices[i_idx];
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// }
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key = std::pair<int, int> (p1, p2);
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// Check to see if we already estimated this pair in the global hashmap
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std::map<std::pair<int, int>, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator<std::pair<const std::pair<int, int>, Eigen::Vector4f> > >::iterator fm_it = feature_map_.find (key);
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if (fm_it != feature_map_.end ())
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{
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pfh_tuple_ = fm_it->second;
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key_found = true;
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}
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else
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{
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// Compute the pair NNi to NNj
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if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
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pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
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continue;
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key_found = false;
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}
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}
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else
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if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
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pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
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continue;
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// Normalize the f1, f2, f3 features and push them in the histogram
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f_index_[0] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[0] + M_PI) * d_pi_)));
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if (f_index_[0] < 0) f_index_[0] = 0;
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if (f_index_[0] >= nr_split) f_index_[0] = nr_split - 1;
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f_index_[1] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[1] + 1.0) * 0.5)));
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if (f_index_[1] < 0) f_index_[1] = 0;
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if (f_index_[1] >= nr_split) f_index_[1] = nr_split - 1;
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f_index_[2] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[2] + 1.0) * 0.5)));
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if (f_index_[2] < 0) f_index_[2] = 0;
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if (f_index_[2] >= nr_split) f_index_[2] = nr_split - 1;
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// Copy into the histogram
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h_index = 0;
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h_p = 1;
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for (const int &d : f_index_)
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{
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h_index += h_p * d;
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h_p *= nr_split;
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}
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pfh_histogram[h_index] += hist_incr;
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if (use_cache_ && !key_found)
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{
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// Save the value in the hashmap
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feature_map_[key] = pfh_tuple_;
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// Use a maximum cache so that we don't go overboard on RAM usage
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key_list_.push (key);
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// Check to see if we need to remove an element due to exceeding max_size
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if (key_list_.size () > max_cache_size_)
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{
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// Remove the oldest element.
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feature_map_.erase (key_list_.front ());
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key_list_.pop ();
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}
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// Clear the feature map
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feature_map_.clear ();
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std::queue<std::pair<int, int> > empty;
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std::swap (key_list_, empty);
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pfh_histogram_.setZero (nr_subdiv_ * nr_subdiv_ * nr_subdiv_);
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// Allocate enough space to hold the results
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// \note This resize is irrelevant for a radiusSearch ().
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pcl::Indices nn_indices (k_);
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std::vector<float> nn_dists (k_);
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output.is_dense = true;
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// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
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if (input_->is_dense)
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{
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// Iterating over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
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output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// Estimate the PFH signature at each patch
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computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
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// Copy into the resultant cloud
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for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
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output[idx].histogram[d] = pfh_histogram_[d];
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}
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}
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else
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{
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// Iterating over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (!isFinite ((*input_)[(*indices_)[idx]]) ||
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this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
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output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// Estimate the PFH signature at each patch
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computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
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// Copy into the resultant cloud
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for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
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output[idx].histogram[d] = pfh_histogram_[d];
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}
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}
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}
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#define PCL_INSTANTIATE_PFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHEstimation<T,NT,OutT>;
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