// STL

 #include <iostream>

 // PCL

 #include <pcl/io/pcd_io.h>

 #include <pcl/point_types.h>

 #include <pcl/common/io.h>

 #include <pcl/keypoints/sift_keypoint.h>

 #include <pcl/features/normal_3d.h>

 #include <pcl/visualization/pcl_visualizer.h>

 #include <pcl/console/time.h>

 /* This examples shows how to estimate the SIFT points based on the

  * z gradient of the 3D points than using the Intensity gradient as

  * usually used for SIFT keypoint estimation.

  */

 namespace pcl

 {

   template<>

     struct SIFTKeypointFieldSelector<PointXYZ>

     {

       inline float

       operator () (const PointXYZ &p) const

       {

     return p.z;

       }

     };

 }

 int

 main(int, char** argv)

 {

   std::string filename = argv[];

   std::cout << "Reading " << filename << std::endl;

   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);

   if(pcl::io::loadPCDFile<pcl::PointXYZ> (filename, *cloud_xyz) == -) // load the file

   {

     PCL_ERROR ("Couldn't read file");

     return -;

   }

   std::cout << "points: " << cloud_xyz->points.size () <<std::endl;

   // Parameters for sift computation

   const float min_scale = 0.005f; //the standard deviation of the smallest scale in the scale space

   const int n_octaves = ;//the number of octaves (i.e. doublings of scale) to compute

   const int n_scales_per_octave = ;//the number of scales to compute within each octave

   const float min_contrast = 0.005f;//the minimum contrast required for detection

   pcl::console::TicToc time;

   time.tic();

   // Estimate the sift interest points using z values from xyz as the Intensity variants

   pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;

   pcl::PointCloud<pcl::PointWithScale> result;

   pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ> ());

   sift.setSearchMethod(tree);

   sift.setScales(min_scale, n_octaves, n_scales_per_octave);

   sift.setMinimumContrast(min_contrast);

   sift.setInputCloud(cloud_xyz);

   sift.compute(result);

   std::cout<<"Computing the SIFT points takes "<<time.toc()/<<"seconds"<<std::endl;

   std::cout << "No of SIFT points in the result are " << result.points.size () << std::endl;

   // Copying the pointwithscale to pointxyz so as visualize the cloud

   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_temp (new pcl::PointCloud<pcl::PointXYZ>);

   copyPointCloud(result, *cloud_temp);

   std::cout << "SIFT points in the result are " << cloud_temp->points.size () << std::endl;

   // Visualization of keypoints along with the original cloud

   pcl::visualization::PCLVisualizer viewer("PCL Viewer");

   pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler (cloud_temp, , , );

   pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_color_handler (cloud_xyz, , , );

   viewer.setBackgroundColor( 0.0, 0.0, 0.0 );

   viewer.addPointCloud(cloud_xyz, cloud_color_handler, "cloud");//add point cloud

   viewer.addPointCloud(cloud_temp, keypoints_color_handler, "keypoints");//add the keypoints

   viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, , "keypoints");

   while(!viewer.wasStopped ())

   {

     viewer.spinOnce ();

   }

   return ;

 }