Point Cloud Library PCL learning --SIFT key points

Class SIFTKeypoint after the two-dimensional image to SIFT algorithm implemented SIFT transplanted adjusted operator 3D space. Input point cloud with the XYZ coordinate values and intensity, the output for the SIFT key points in the point cloud.

SIFTKeypoint class
void	setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
	And scale parameters, setting the search min_scale ; standard deviation of the voxel cloud point scale space, the standard deviation is the smallest scale space dimensions, the point cloud corresponding to the minimum size of the voxel in the voxel grid nr_octaves when the key is detected number of voxels spatial scales, the number of Gaussian pyramid group; nr_scales_ per _ Octave is Gaussian spatial scale is calculated at each voxel spatial scales required parameters, the number of dimensions (the Octave) calculated for each group.
void	setMinimumContrast (float min contrast)
	Key set limits detection threshold
void	compute(PointCloudOut &output)
	Are calculated and stored in the key point to the output

#pragma warning(disable:4996)
#include <vtkAutoInit.h>
VTK_MODULE_INIT(vtkRenderingOpenGL);
VTK_MODULE_INIT(vtkInteractionStyle);
#include <iostream>
#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>
using namespace std;

namespace pcl
{
	template<>
	struct SIFTKeypointFieldSelector<PointXYZ>
	{
		inline float
			operator () (const PointXYZ &p) const
		{
			return p.z;
		}
	};
}

int
main(int argc, char *argv[])
{

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::io::loadPCDFile("F:\\C++project\\pig.pcd", *cloud_xyz);

	const float min_scale = 0.01;             //设置尺度空间中最小尺度的标准偏差          
	const int n_octaves = 6;               //设置高斯金字塔组（octave）的数目            
	const int n_scales_per_octave =4;     //设置每组（octave）计算的尺度  
	const float min_contrast = 0.01;          //设置限制关键点检测的阈值       

	pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;//创建sift关键点检测对象
	pcl::PointCloud<pcl::PointWithScale> result;
	sift.setInputCloud(cloud_xyz);//设置输入点云
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
	sift.setSearchMethod(tree);//创建一个空的kd树对象tree，并把它传递给sift检测对象
	sift.setScales(min_scale, n_octaves, n_scales_per_octave);//指定搜索关键点的尺度范围
	sift.setMinimumContrast(min_contrast);//设置限制关键点检测的阈值
	sift.compute(result);//执行sift关键点检测，保存结果在result

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_temp(new pcl::PointCloud<pcl::PointXYZ>);
	copyPointCloud(result, *cloud_temp);//将点类型pcl::PointWithScale的数据转换为点类型pcl::PointXYZ的数据

	//可视化输入点云和关键点
	pcl::visualization::PCLVisualizer viewer("Sift keypoint");
	viewer.setBackgroundColor(255, 255, 255);
	viewer.addPointCloud(cloud_xyz, "cloud");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 0, 0, "cloud");
	viewer.addPointCloud(cloud_temp, "keypoints");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 9, "keypoints");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 0, 255, "keypoints");

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}
	return 0;

}

Results are as follows:

Point Cloud Library PCL learning --SIFT key points

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