// PointNormal: float x, y, znormal[3], curvature
pcl::PointCloud<pcl::PointNormal>::Ptr smoothAndNormalEstimation(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud,
const double radius)
{
// Smoothing and normal estimation based on polynomial reconstruction
// Moving Least Squares (MLS) surface reconstruction method can be used to smooth and resample noisy data
// Create a KD-Tree
pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
// Init object (second point type is for the normals, even if unused)
pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls;
// Set parameters
mls.setInputCloud(cloud);
mls.setPolynomialFit(true);
mls.setSearchMethod(kdtree);
mls.setSearchRadius(radius);
// void setPointDensity (int desired_num_points_in_radius);
// void setDilationVoxelSize (float voxel_size)
// void setDilationIterations (int iterations);
// void setSqrGaussParam (double sqr_gauss_param)
// void setPolynomialOrder (int order)
// void setPolynomialFit (bool polynomial_fit)
// Reconstruct
// PCL v1.6
#if 1
mls.setComputeNormals(true);
// Output has the PointNormal type in order to store the normals calculated by MLS
pcl::PointCloud<pcl::PointNormal>::Ptr mls_points(new pcl::PointCloud<pcl::PointNormal>());
mls.process (*mls_points);
return mls_points;
#else
mls.reconstruct(*pPoints);
// Output has the PointNormal type in order to store the normals calculated by MLS
pPointNormals = mls.getOutputNormals();
//mls.setOutputNormals(mls_points);
#endif
}
inline void smooth(boost::shared_ptr<pcl::PointCloud<PointT>> &cloud, double radius) {
// Create a KD-Tree
typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT>);
// Output has the PointNormal type in order to store the normals calculated by MLS
boost::shared_ptr<pcl::PointCloud<PointT>> mls_points (new pcl::PointCloud<PointT>(512, 424));
// Init object (second point type is for the normals, even if unused)
pcl::MovingLeastSquares<PointT, PointT> mls;
mls.setComputeNormals (true);
// Set parameters
mls.setInputCloud (cloud);
mls.setPolynomialFit (false);
mls.setSearchMethod (tree);
mls.setSearchRadius (radius);
// Reconstruct
mls.process (*mls_points);
mls_points.swap(cloud);
}