基于SIFT的点云关键点提取

这篇博文主要介绍SIFT算法在提取点云图像关键点时的具体用法。

尺度不变特征转换(Scale-invariant feature transform,SIFT)是David Lowe在1999年发表，2004年总结完善。其应用范围包括物体辨识，机器人地图感知与导航、3D模型建立、手势辨识、影像追踪和动作对比。此算法已经申请专利，专利拥有者属于英属哥伦比亚大学。SIFT算法在3D数据上的应用由Flint等在2007年实现。这里所讲的提取点云关键点的算法便是由Flint等人实现的SIFT3D算法。

其实现代如下：

 // 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 ;

 }

运行结果如下图所示：