基于PCL的三维点云模型表面主曲率估计的具体实现

/* * Software License Agreement (BSD License) * * Point Cloud Library (PCL) - www.pointclouds.org * Copyright (c) 2010-2011, Willow Garage, Inc. * * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * Neither the name of Willow Garage, Inc. nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * * $Id: principal_curvatures.hpp 5026 2012-03-12 02:51:44Z rusu $ * */ #ifndef PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_ #define PCL_FEATURES_IMPL_PRINCIPAL_CURVATURES_H_ #include <pcl/features/principal_curvatures.h> ////////////////////////////////////////////////////////////////////////////////////////////// template <typename PointInT, typename PointNT, typename PointOutT> void pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures ( const pcl::PointCloud<PointNT> &normals, int p_idx, const std::vector<int> &indices, float &pcx, float &pcy, float &pcz, float &pc1, float &pc2) { EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity (); Eigen::Vector3f n_idx (normals.points[p_idx].normal[0], normals.points[p_idx].normal[1], normals.points[p_idx].normal[2]); EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane) // Project normals into the tangent plane Eigen::Vector3f normal; projected_normals_.resize (indices.size ()); xyz_centroid_.setZero (); for (size_t idx = 0; idx < indices.size(); ++idx) { normal[0] = normals.points[indices[idx]].normal[0]; normal[1] = normals.points[indices[idx]].normal[1]; normal[2] = normals.points[indices[idx]].normal[2]; projected_normals_[idx] = M * normal; xyz_centroid_ += projected_normals_[idx]; } // Estimate the XYZ centroid xyz_centroid_ /= static_cast<float> (indices.size ()); // Initialize to 0 covariance_matrix_.setZero (); double demean_xy, demean_xz, demean_yz; // For each point in the cloud for (size_t idx = 0; idx < indices.size (); ++idx) { demean_ = projected_normals_[idx] - xyz_centroid_; demean_xy = demean_[0] * demean_[1]; demean_xz = demean_[0] * demean_[2]; demean_yz = demean_[1] * demean_[2]; covariance_matrix_(0, 0) += demean_[0] * demean_[0]; covariance_matrix_(0, 1) += static_cast<float> (demean_xy); covariance_matrix_(0, 2) += static_cast<float> (demean_xz); covariance_matrix_(1, 0) += static_cast<float> (demean_xy); covariance_matrix_(1, 1) += demean_[1] * demean_[1]; covariance_matrix_(1, 2) += static_cast<float> (demean_yz); covariance_matrix_(2, 0) += static_cast<float> (demean_xz); covariance_matrix_(2, 1) += static_cast<float> (demean_yz); covariance_matrix_(2, 2) += demean_[2] * demean_[2]; } // Extract the eigenvalues and eigenvectors pcl::eigen33 (covariance_matrix_, eigenvalues_); pcl::computeCorrespondingEigenVector (covariance_matrix_, eigenvalues_ [2], eigenvector_); pcx = eigenvector_ [0]; pcy = eigenvector_ [1]; pcz = eigenvector_ [2]; float indices_size = 1.0f / static_cast<float> (indices.size ()); pc1 = eigenvalues_ [2] * indices_size; pc2 = eigenvalues_ [1] * indices_size; } ////////////////////////////////////////////////////////////////////////////////////////////// template <typename PointInT, typename PointNT, typename PointOutT> void pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output) { // Allocate enough space to hold the results // \note This resize is irrelevant for a radiusSearch (). std::vector<int> nn_indices (k_); std::vector<float> nn_dists (k_); output.is_dense = true; // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense if (input_->is_dense) { // Iterating over the entire index vector for (size_t idx = 0; idx < indices_->size (); ++idx) { if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0) { output.points[idx].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] = output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN (); output.is_dense = false; continue; } // Estimate the principal curvatures at each patch computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices, output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2], output.points[idx].pc1, output.points[idx].pc2); } } else { // Iterating over the entire index vector for (size_t idx = 0; idx < indices_->size (); ++idx) { if (!isFinite ((*input_)[(*indices_)[idx]]) || this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0) { output.points[idx].principal_curvature[0] = output.points[idx].principal_curvature[1] = output.points[idx].principal_curvature[2] = output.points[idx].pc1 = output.points[idx].pc2 = std::numeric_limits<float>::quiet_NaN (); output.is_dense = false; continue; } // Estimate the principal curvatures at each patch computePointPrincipalCurvatures (*normals_, (*indices_)[idx], nn_indices, output.points[idx].principal_curvature[0], output.points[idx].principal_curvature[1], output.points[idx].principal_curvature[2], output.points[idx].pc1, output.points[idx].pc2); } } } ////////////////////////////////////////////////////////////////////////////////////////////// template <typename PointInT, typename PointNT> void pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, Eigen::MatrixXf>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output) { // Resize the output dataset output.points.resize (indices_->size (), 5); // Allocate enough space to hold the results // \note This resize is irrelevant for a radiusSearch (). std::vector<int> nn_indices (k_); std::vector<float> nn_dists (k_); output.is_dense = true; // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense if (input_->is_dense) { // Iterating over the entire index vector for (size_t idx = 0; idx < indices_->size (); ++idx) { if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dist