Normal-PCL-Cpp (70%)

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <boost/thread/thread.hpp>
#include <pcl/visualization/pcl_visualizer.h>

int
main(int argc, char** argv)
{
    // Object for storing the point cloud.
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    // Object for storing the normals.
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);

    // Read a PCD file from disk.
    pcl::io::loadPCDFile<pcl::PointXYZ>("lobby.pcd", *cloud);

    // Object for normal estimation.
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
    normalEstimation.setInputCloud(cloud);
    // For every point, use all neighbors in a radius of 3cm.
    normalEstimation.setRadiusSearch(0.03);
    // A kd-tree is a data structure that makes searches efficient. More about it later.
    // The normal estimation object will use it to find nearest neighbors.
    pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
    normalEstimation.setSearchMethod(kdtree);

    // Calculate the normals.
    normalEstimation.compute(*normals);

    // Visualize them.
    boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("Normals"));
    viewer->addPointCloud<pcl::PointXYZ>(cloud, "cloud");
    // Display one normal out of 20, as a line of length 3cm.
    viewer->addPointCloudNormals<pcl::PointXYZ, pcl::Normal>(cloud, normals, 20, 0.03, "normals");
    while (!viewer->wasStopped())
    {
        viewer->spinOnce(100);
        boost::this_thread::sleep(boost::posix_time::microseconds(100000));
    }
}

normals are stored in "PointCloud" objects

setRadiusSearch(): setKSearch(int K): point's K nearest neighbors to compute the normal

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