#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/pfh.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>);
	// Object for storing the PFH descriptors for each point.
	pcl::PointCloud<pcl::PFHSignature125>::Ptr descriptors(new pcl::PointCloud<pcl::PFHSignature125>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// PFH estimation object.
	pcl::PFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::PFHSignature125> pfh;
	pfh.setInputCloud(cloud);
	pfh.setInputNormals(normals);
	pfh.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. Note: the value given here has to be
	// larger than the radius used to estimate the normals.
	pfh.setRadiusSearch(0.05);

	pfh.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/fpfh.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>);
	// Object for storing the FPFH descriptors for each point.
	pcl::PointCloud<pcl::FPFHSignature33>::Ptr descriptors(new pcl::PointCloud<pcl::FPFHSignature33>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// FPFH estimation object.
	pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
	fpfh.setInputCloud(cloud);
	fpfh.setInputNormals(normals);
	fpfh.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. Note: the value given here has to be
	// larger than the radius used to estimate the normals.
	fpfh.setRadiusSearch(0.05);

	fpfh.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/rsd.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>);
	// Object for storing the RSD descriptors for each point.
	pcl::PointCloud<pcl::PrincipalRadiiRSD>::Ptr descriptors(new pcl::PointCloud<pcl::PrincipalRadiiRSD>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// RSD estimation object.
	RSDEstimation<PointXYZ, Normal, PrincipalRadiiRSD> rsd;
	rsd.setInputCloud(cloud);
	rsd.setInputNormals(normals);
	rsd.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. Note: the value given here has to be
	// larger than the radius used to estimate the normals.
	rsd.setRadiusSearch(0.05);
	// Plane radius. Any radius larger than this is considered infinite (a plane).
	rsd.setPlaneRadius(0.1);
	// Do we want to save the full distance-angle histograms?
	rsd.setSaveHistograms(false);
	
	rsd.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/3dsc.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>);
	// Object for storing the 3DSC descriptors for each point.
	pcl::PointCloud<pcl::ShapeContext1980>::Ptr descriptors(new pcl::PointCloud<pcl::ShapeContext1980>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// 3DSC estimation object.
	pcl::ShapeContext3DEstimation<pcl::PointXYZ, pcl::Normal, pcl::ShapeContext1980> sc3d;
	sc3d.setInputCloud(cloud);
	sc3d.setInputNormals(normals);
	sc3d.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. It will also be the radius of the support sphere.
	sc3d.setRadiusSearch(0.05);
	// The minimal radius value for the search sphere, to avoid being too sensitive
	// in bins close to the center of the sphere.
	sc3d.setMinimalRadius(0.05 / 10.0);
	// Radius used to compute the local point density for the neighbors
	// (the density is the number of points within that radius).
	sc3d.setPointDensityRadius(0.05 / 5.0);

	sc3d.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/usc.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 USC descriptors for each point.
	pcl::PointCloud<pcl::UniqueShapeContext1960>::Ptr descriptors(new pcl::PointCloud<pcl::UniqueShapeContext1960>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// USC estimation object.
	pcl::UniqueShapeContext<pcl::PointXYZ, pcl::UniqueShapeContext1960, pcl::ReferenceFrame> usc;
	usc.setInputCloud(cloud);
	// Search radius, to look for neighbors. It will also be the radius of the support sphere.
	usc.setRadiusSearch(0.05);
	// The minimal radius value for the search sphere, to avoid being too sensitive
	// in bins close to the center of the sphere.
	usc.setMinimalRadius(0.05 / 10.0);
	// Radius used to compute the local point density for the neighbors
	// (the density is the number of points within that radius).
	usc.setPointDensityRadius(0.05 / 5.0);
	// Set the radius to compute the Local Reference Frame.
	usc.setLocalRadius(0.05);

	usc.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/shot.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>);
	// Object for storing the SHOT descriptors for each point.
	pcl::PointCloud<pcl::SHOT352>::Ptr descriptors(new pcl::PointCloud<pcl::SHOT352>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// SHOT estimation object.
	pcl::SHOTEstimation<pcl::PointXYZ, pcl::Normal, pcl::SHOT352> shot;
	shot.setInputCloud(cloud);
	shot.setInputNormals(normals);
	// The radius that defines which of the keypoint's neighbors are described.
	// If too large, there may be clutter, and if too small, not enough points may be found.
	shot.setRadiusSearch(0.02);

	shot.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/spin_image.h>

// A handy typedef.
typedef pcl::Histogram<153> SpinImage;

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>);
	// Object for storing the spin image for each point.
	pcl::PointCloud<SpinImage>::Ptr descriptors(new pcl::PointCloud<SpinImage>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// Spin image estimation object.
	pcl::SpinImageEstimation<pcl::PointXYZ, pcl::Normal, SpinImage> si;
	si.setInputCloud(cloud);
	si.setInputNormals(normals);
	// Radius of the support cylinder.
	si.setRadiusSearch(0.02);
	// Set the resolution of the spin image (the number of bins along one dimension).
	// Note: you must change the output histogram size to reflect this.
	si.setImageWidth(8);

	si.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/point_types_conversion.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/intensity_gradient.h>
#include <pcl/features/rift.h>

// A handy typedef.
typedef pcl::Histogram<32> RIFT32;

int
main(int argc, char** argv)
{
	// Object for storing the point cloud with color information.
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudColor(new pcl::PointCloud<pcl::PointXYZRGB>);
	// Object for storing the point cloud with intensity value.
	pcl::PointCloud<pcl::PointXYZI>::Ptr cloudIntensity(new pcl::PointCloud<pcl::PointXYZI>);
	// Object for storing the intensity gradients.
	pcl::PointCloud<pcl::IntensityGradient>::Ptr gradients(new pcl::PointCloud<pcl::IntensityGradient>);
	// Object for storing the normals.
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	// Object for storing the RIFT descriptor for each point.
	pcl::PointCloud<RIFT32>::Ptr descriptors(new pcl::PointCloud<RIFT32>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZRGB>(argv[1], *cloudColor) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Convert the RGB to intensity.
	pcl::PointCloudXYZRGBtoXYZI(*cloudColor, *cloudIntensity);

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZI, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloudIntensity);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZI>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZI>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// Compute the intensity gradients.
	pcl::IntensityGradientEstimation < pcl::PointXYZI, pcl::Normal, pcl::IntensityGradient,
		pcl::common::IntensityFieldAccessor<pcl::PointXYZI> > ge;
	ge.setInputCloud(cloudIntensity);
	ge.setInputNormals(normals);
	ge.setRadiusSearch(0.03);
	ge.compute(*gradients);

	// RIFT estimation object.
	pcl::RIFTEstimation<pcl::PointXYZI, pcl::IntensityGradient, RIFT32> rift;
	rift.setInputCloud(cloudIntensity);
	rift.setSearchMethod(kdtree);
	// Set the intensity gradients to use.
	rift.setInputGradient(gradients);
	// Radius, to get all neighbors within.
	rift.setRadiusSearch(0.02);
	// Set the number of bins to use in the distance dimension.
	rift.setNrDistanceBins(4);
	// Set the number of bins to use in the gradient orientation dimension.
	rift.setNrGradientBins(8);
	// Note: you must change the output histogram size to reflect the previous values.

	rift.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/range_image/range_image.h>
#include <pcl/visualization/range_image_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>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Parameters needed by the range image object:

	// Angular resolution is the angular distance between pixels.
	// Kinect: 57° horizontal FOV, 43° vertical FOV, 640x480 (chosen here).
	// Xtion: 58° horizontal FOV, 45° vertical FOV, 640x480.
	float angularResolutionX = (float)(57.0f / 640.0f * (M_PI / 180.0f));
	float angularResolutionY = (float)(43.0f / 480.0f * (M_PI / 180.0f));
	// Maximum horizontal and vertical angles. For example, for a full panoramic scan,
	// the first would be 360º. Choosing values that adjust to the real sensor will
	// decrease the time it takes, but don't worry. If the values are bigger than
	// the real ones, the image will be automatically cropped to discard empty zones.
	float maxAngleX = (float)(60.0f * (M_PI / 180.0f));
	float maxAngleY = (float)(50.0f * (M_PI / 180.0f));
	// Sensor pose. Thankfully, the cloud includes the data.
	Eigen::Affine3f sensorPose = Eigen::Affine3f(Eigen::Translation3f(cloud->sensor_origin_[0],
								 cloud->sensor_origin_[1],
								 cloud->sensor_origin_[2])) *
								 Eigen::Affine3f(cloud->sensor_orientation_);
	// Noise level. If greater than 0, values of neighboring points will be averaged.
	// This would set the search radius (e.g., 0.03 == 3cm).
	float noiseLevel = 0.0f;
	// Minimum range. If set, any point closer to the sensor than this will be ignored.
	float minimumRange = 0.0f;
	// Border size. If greater than 0, a border of "unobserved" points will be left
	// in the image when it is cropped.
	int borderSize = 1;

	// Range image object.
	pcl::RangeImage rangeImage;
	rangeImage.createFromPointCloud(*cloud, angularResolutionX, angularResolutionY,
									maxAngleX, maxAngleY, sensorPose, pcl::RangeImage::CAMERA_FRAME,
									noiseLevel, minimumRange, borderSize);

	// Visualize the image.
	pcl::visualization::RangeImageVisualizer viewer("Range image");
	viewer.showRangeImage(rangeImage);
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
		// Sleep 100ms to go easy on the CPU.
		pcl_sleep(0.1);
	}
}

#include <pcl/io/pcd_io.h>
#include <pcl/range_image/range_image_planar.h>
#include <pcl/visualization/range_image_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>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Parameters needed by the planar range image object:

	// Image size. Both Kinect and Xtion work at 640x480.
	int imageSizeX = 640;
	int imageSizeY = 480;
	// Center of projection. here, we choose the middle of the image.
	float centerX = 640.0f / 2.0f;
	float centerY = 480.0f / 2.0f;
	// Focal length. The value seen here has been taken from the original depth images.
	// It is safe to use the same value vertically and horizontally.
	float focalLengthX = 525.0f, focalLengthY = focalLengthX;
	// Sensor pose. Thankfully, the cloud includes the data.
	Eigen::Affine3f sensorPose = Eigen::Affine3f(Eigen::Translation3f(cloud->sensor_origin_[0],
								 cloud->sensor_origin_[1],
								 cloud->sensor_origin_[2])) *
								 Eigen::Affine3f(cloud->sensor_orientation_);
	// Noise level. If greater than 0, values of neighboring points will be averaged.
	// This would set the search radius (e.g., 0.03 == 3cm).
	float noiseLevel = 0.0f;
	// Minimum range. If set, any point closer to the sensor than this will be ignored.
	float minimumRange = 0.0f;

	// Planar range image object.
	pcl::RangeImagePlanar rangeImagePlanar;
	rangeImagePlanar.createFromPointCloudWithFixedSize(*cloud, imageSizeX, imageSizeY,
			centerX, centerY, focalLengthX, focalLengthX,
			sensorPose, pcl::RangeImage::CAMERA_FRAME,
			noiseLevel, minimumRange);

	// Visualize the image.
	pcl::visualization::RangeImageVisualizer viewer("Planar range image");
	viewer.showRangeImage(rangeImagePlanar);
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
		// Sleep 100ms to go easy on the CPU.
		pcl_sleep(0.1);
	}
}

#include <pcl/io/pcd_io.h>
#include <pcl/range_image/range_image_planar.h>
#include <pcl/features/range_image_border_extractor.h>
#include <pcl/visualization/range_image_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 borders.
	pcl::PointCloud<pcl::BorderDescription>::Ptr borders(new pcl::PointCloud<pcl::BorderDescription>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Convert the cloud to range image.
	int imageSizeX = 640, imageSizeY = 480;
	float centerX = (640.0f / 2.0f), centerY = (480.0f / 2.0f);
	float focalLengthX = 525.0f, focalLengthY = focalLengthX;
	Eigen::Affine3f sensorPose = Eigen::Affine3f(Eigen::Translation3f(cloud->sensor_origin_[0],
								 cloud->sensor_origin_[1],
								 cloud->sensor_origin_[2])) *
								 Eigen::Affine3f(cloud->sensor_orientation_);
	float noiseLevel = 0.0f, minimumRange = 0.0f;
	pcl::RangeImagePlanar rangeImage;
	rangeImage.createFromPointCloudWithFixedSize(*cloud, imageSizeX, imageSizeY,
			centerX, centerY, focalLengthX, focalLengthX,
			sensorPose, pcl::RangeImage::CAMERA_FRAME,
			noiseLevel, minimumRange);

	// Border extractor object.
	pcl::RangeImageBorderExtractor borderExtractor(&rangeImage);

	borderExtractor.compute(*borders);

	// Visualize the borders.
	pcl::visualization::RangeImageVisualizer* viewer = NULL;
	viewer = pcl::visualization::RangeImageVisualizer::getRangeImageBordersWidget(rangeImage,
			 -std::numeric_limits<float>::infinity(),
			 std::numeric_limits<float>::infinity(),
			 false, *borders, "Borders");

	while (!viewer->wasStopped())
	{
		viewer->spinOnce();
		// Sleep 100ms to go easy on the CPU.
		pcl_sleep(0.1);
	}
}

#include <pcl/io/pcd_io.h>
#include <pcl/range_image/range_image_planar.h>
#include <pcl/features/range_image_border_extractor.h>
#include <pcl/keypoints/narf_keypoint.h>
#include <pcl/visualization/range_image_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 keypoints' indices.
	pcl::PointCloud<int>::Ptr keypoints(new pcl::PointCloud<int>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Convert the cloud to range image.
	int imageSizeX = 640, imageSizeY = 480;
	float centerX = (640.0f / 2.0f), centerY = (480.0f / 2.0f);
	float focalLengthX = 525.0f, focalLengthY = focalLengthX;
	Eigen::Affine3f sensorPose = Eigen::Affine3f(Eigen::Translation3f(cloud->sensor_origin_[0],
								 cloud->sensor_origin_[1],
								 cloud->sensor_origin_[2])) *
								 Eigen::Affine3f(cloud->sensor_orientation_);
	float noiseLevel = 0.0f, minimumRange = 0.0f;
	pcl::RangeImagePlanar rangeImage;
	rangeImage.createFromPointCloudWithFixedSize(*cloud, imageSizeX, imageSizeY,
			centerX, centerY, focalLengthX, focalLengthX,
			sensorPose, pcl::RangeImage::CAMERA_FRAME,
			noiseLevel, minimumRange);

	pcl::RangeImageBorderExtractor borderExtractor;
	// Keypoint detection object.
	pcl::NarfKeypoint detector(&borderExtractor);
	detector.setRangeImage(&rangeImage);
	// The support size influences how big the surface of interest will be,
	// when finding keypoints from the border information.
	detector.getParameters().support_size = 0.2f;

	detector.compute(*keypoints);

	// Visualize the keypoints.
	pcl::visualization::RangeImageVisualizer viewer("NARF keypoints");
	viewer.showRangeImage(rangeImage);
	for (size_t i = 0; i < keypoints->points.size(); ++i)
	{
		viewer.markPoint(keypoints->points[i] % rangeImage.width,
						 keypoints->points[i] / rangeImage.width,
						 // Set the color of the pixel to red (the background
						 // circle is already that color). All other parameters
						 // are left untouched, check the API for more options.
						 pcl::visualization::Vector3ub(1.0f, 0.0f, 0.0f));
	}

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
		// Sleep 100ms to go easy on the CPU.
		pcl_sleep(0.1);
	}
}

#include <pcl/io/pcd_io.h>
#include <pcl/range_image/range_image_planar.h>
#include <pcl/features/range_image_border_extractor.h>
#include <pcl/keypoints/narf_keypoint.h>
#include <pcl/features/narf_descriptor.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 keypoints' indices.
	pcl::PointCloud<int>::Ptr keypoints(new pcl::PointCloud<int>);
	// Object for storing the NARF descriptors.
	pcl::PointCloud<pcl::Narf36>::Ptr descriptors(new pcl::PointCloud<pcl::Narf36>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Convert the cloud to range image.
	int imageSizeX = 640, imageSizeY = 480;
	float centerX = (640.0f / 2.0f), centerY = (480.0f / 2.0f);
	float focalLengthX = 525.0f, focalLengthY = focalLengthX;
	Eigen::Affine3f sensorPose = Eigen::Affine3f(Eigen::Translation3f(cloud->sensor_origin_[0],
								 cloud->sensor_origin_[1],
								 cloud->sensor_origin_[2])) *
								 Eigen::Affine3f(cloud->sensor_orientation_);
	float noiseLevel = 0.0f, minimumRange = 0.0f;
	pcl::RangeImagePlanar rangeImage;
	rangeImage.createFromPointCloudWithFixedSize(*cloud, imageSizeX, imageSizeY,
			centerX, centerY, focalLengthX, focalLengthX,
			sensorPose, pcl::RangeImage::CAMERA_FRAME,
			noiseLevel, minimumRange);

	// Extract the keypoints.
	pcl::RangeImageBorderExtractor borderExtractor;
	pcl::NarfKeypoint detector(&borderExtractor);
	detector.setRangeImage(&rangeImage);
	detector.getParameters().support_size = 0.2f;
	detector.compute(*keypoints);

	// The NARF estimator needs the indices in a vector, not a cloud.
	std::vector<int> keypoints2;
	keypoints2.resize(keypoints->points.size());
	for (unsigned int i = 0; i < keypoints->size(); ++i)
		keypoints2[i] = keypoints->points[i];
	// NARF estimation object.
	pcl::NarfDescriptor narf(&rangeImage, &keypoints2);
	// Support size: choose the same value you used for keypoint extraction.
	narf.getParameters().support_size = 0.2f;
	// If true, the rotation invariant version of NARF will be used. The histogram
	// will be shifted according to the dominant orientation to provide robustness to
	// rotations around the normal.
	narf.getParameters().rotation_invariant = true;

	narf.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/vfh.h>

int
main(int argc, char** argv)
{
	// Cloud for storing the object.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	// Object for storing the normals.
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	// Object for storing the VFH descriptor.
	pcl::PointCloud<pcl::VFHSignature308>::Ptr descriptor(new pcl::PointCloud<pcl::VFHSignature308>);

	// Note: you should have performed preprocessing to cluster out the object
	// from the cloud, and save it to this individual file.

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(object);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// VFH estimation object.
	pcl::VFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> vfh;
	vfh.setInputCloud(object);
	vfh.setInputNormals(normals);
	vfh.setSearchMethod(kdtree);
	// Optionally, we can normalize the bins of the resulting histogram,
	// using the total number of points.
	vfh.setNormalizeBins(true);
	// Also, we can normalize the SDC with the maximum size found between
	// the centroid and any of the cluster's points.
	vfh.setNormalizeDistance(false);

	vfh.compute(*descriptor);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/cvfh.h>

int
main(int argc, char** argv)
{
	// Cloud for storing the object.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	// Object for storing the normals.
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	// Object for storing the CVFH descriptors.
	pcl::PointCloud<pcl::VFHSignature308>::Ptr descriptors(new pcl::PointCloud<pcl::VFHSignature308>);

	// Note: you should have performed preprocessing to cluster out the object
	// from the cloud, and save it to this individual file.

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(object);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// CVFH estimation object.
	pcl::CVFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> cvfh;
	cvfh.setInputCloud(object);
	cvfh.setInputNormals(normals);
	cvfh.setSearchMethod(kdtree);
	// Set the maximum allowable deviation of the normals,
	// for the region segmentation step.
	cvfh.setEPSAngleThreshold(5.0 / 180.0 * M_PI); // 5 degrees.
	// Set the curvature threshold (maximum disparity between curvatures),
	// for the region segmentation step.
	cvfh.setCurvatureThreshold(1.0);
	// Set to true to normalize the bins of the resulting histogram,
	// using the total number of points. Note: enabling it will make CVFH
	// invariant to scale just like VFH, but the authors encourage the opposite.
	cvfh.setNormalizeBins(false);

	cvfh.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/our_cvfh.h>

int
main(int argc, char** argv)
{
	// Cloud for storing the object.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	// Object for storing the normals.
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	// Object for storing the OUR-CVFH descriptors.
	pcl::PointCloud<pcl::VFHSignature308>::Ptr descriptors(new pcl::PointCloud<pcl::VFHSignature308>);

	// Note: you should have performed preprocessing to cluster out the object
	// from the cloud, and save it to this individual file.

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(object);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// OUR-CVFH estimation object.
	pcl::OURCVFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> ourcvfh;
	ourcvfh.setInputCloud(object);
	ourcvfh.setInputNormals(normals);
	ourcvfh.setSearchMethod(kdtree);
	ourcvfh.setEPSAngleThreshold(5.0 / 180.0 * M_PI); // 5 degrees.
	ourcvfh.setCurvatureThreshold(1.0);
	ourcvfh.setNormalizeBins(false);
	// Set the minimum axis ratio between the SGURF axes. At the disambiguation phase,
	// this will decide if additional Reference Frames need to be created, if ambiguous.
	ourcvfh.setAxisRatio(0.8);

	ourcvfh.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/esf.h>

int
main(int argc, char** argv)
{
	// Cloud for storing the object.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	// Object for storing the ESF descriptor.
	pcl::PointCloud<pcl::ESFSignature640>::Ptr descriptor(new pcl::PointCloud<pcl::ESFSignature640>);

	// Note: you should have performed preprocessing to cluster out the object
	// from the cloud, and save it to this individual file.

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// ESF estimation object.
	pcl::ESFEstimation<pcl::PointXYZ, pcl::ESFSignature640> esf;
	esf.setInputCloud(object);

	esf.compute(*descriptor);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/gfpfh.h>

int
main(int argc, char** argv)
{
	// Cloud for storing the object.
	pcl::PointCloud<pcl::PointXYZL>::Ptr object(new pcl::PointCloud<pcl::PointXYZL>);
	// Object for storing the GFPFH descriptor.
	pcl::PointCloud<pcl::GFPFHSignature16>::Ptr descriptor(new pcl::PointCloud<pcl::GFPFHSignature16>);

	// Note: you should have performed preprocessing to cluster out the object
	// from the cloud, and save it to this individual file.

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZL>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Note: you should now perform classification on the cloud's points. See the
	// original paper for more details. For this example, we will now consider 4
	// different classes, and randomly label each point as one of them.
	for (size_t i = 0; i < object->points.size(); ++i)
	{
		object->points[i].label = 1 + i % 4;
	}

	// ESF estimation object.
	pcl::GFPFHEstimation<pcl::PointXYZL, pcl::PointXYZL, pcl::GFPFHSignature16> gfpfh;
	gfpfh.setInputCloud(object);
	// Set the object that contains the labels for each point. Thanks to the
	// PointXYZL type, we can use the same object we store the cloud in.
	gfpfh.setInputLabels(object);
	// Set the size of the octree leaves to 1cm (cubic).
	gfpfh.setOctreeLeafSize(0.01);
	// Set the number of classes the cloud has been labelled with (default is 16).
	gfpfh.setNumberOfClasses(4);

	gfpfh.compute(*descriptor);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/grsd.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>);
	// Object for storing the GRSD descriptors for each point.
	pcl::PointCloud<pcl::GRSDSignature21>::Ptr descriptors(new pcl::PointCloud<pcl::GRSDSignature21>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// GRSD estimation object.
	GRSDEstimation<PointXYZ, Normal, GRSDSignature21> grsd;
	grsd.setInputCloud(cloud);
	grsd.setInputNormals(normals);
	grsd.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. Note: the value given here has to be
	// larger than the radius used to estimate the normals.
	grsd.setRadiusSearch(0.05);
	
	grsd.compute(*descriptors);
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/vfh.h>
#include<pcl/visualization/histogram_visualizer.h>

int
main(int argc, char** argv)
{
	// Clouds for storing everything.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	pcl::PointCloud<pcl::VFHSignature308>::Ptr descriptor(new pcl::PointCloud<pcl::VFHSignature308>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(object);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// Estimate VFH descriptor.
	pcl::VFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> vfh;
	vfh.setInputCloud(object);
	vfh.setInputNormals(normals);
	vfh.setSearchMethod(kdtree);
	vfh.setNormalizeBins(true);
	vfh.setNormalizeDistance(false);
	vfh.compute(*descriptor);

	// Plotter object.
	pcl::visualization::PCLHistogramVisualizer viewer;
	// We need to set the size of the descriptor beforehand.
	viewer.addFeatureHistogram(*descriptor, 308);

	viewer.spin();
}

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/vfh.h>
#include<pcl/visualization/pcl_plotter.h>

int
main(int argc, char** argv)
{
	// Clouds for storing everything.
	pcl::PointCloud<pcl::PointXYZ>::Ptr object(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	pcl::PointCloud<pcl::VFHSignature308>::Ptr descriptor(new pcl::PointCloud<pcl::VFHSignature308>);

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *object) != 0)
	{
		return -1;
	}

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(object);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// Estimate VFH descriptor.
	pcl::VFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> vfh;
	vfh.setInputCloud(object);
	vfh.setInputNormals(normals);
	vfh.setSearchMethod(kdtree);
	vfh.setNormalizeBins(true);
	vfh.setNormalizeDistance(false);
	vfh.compute(*descriptor);

	// Plotter object.
	pcl::visualization::PCLPlotter plotter;
	// We need to set the size of the descriptor beforehand.
	plotter.addFeatureHistogram(*descriptor, 308);

	plotter.plot();
}

pcl_viewer <descriptor_file>