다운샘플링-PCL-Cpp (70%)

코드는 [이곳]에서 다운로드 가능합니다. 샘플파일은 [table_scene_lms400.pcd]을 사용하였습니다.

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>

//Downsampling a PointCloud using a VoxelGrid filter
//http://pointclouds.org/documentation/tutorials/voxel_grid.php#voxelgrid

int
main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // *.PCD 파일 읽기 (https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd)
  pcl::io::loadPCDFile<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

 // 포인트수 출력
  std::cout << "Input : " << cloud->points.size () << " (" << pcl::getFieldsList (*cloud) <<")"<< std::endl;

  // 오브젝트 생성 
  //pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  pcl::VoxelGrid<pcl::PointXYZ> sor;
  sor.setInputCloud (cloud);              //입력
  sor.setLeafSize (0.01f, 0.01f, 0.01f); //leaf size  1cm 
  sor.filter (*cloud_filtered);          //출력 

  // 생성된 포인트클라우드 수 출력 
  std::cout << "Output : " << cloud_filtered->points.size () << " (" << pcl::getFieldsList (*cloud_filtered) <<")"<< std::endl;

  // 생성된 포인트클라우드 저장 
  pcl::io::savePCDFile<pcl::PointXYZ>("table_scene_lms400_downsampled.pcd", *cloud_filtered);
  return (0);
}

실행 & 결과

$ Input : 460400 (x y z)
$ Output : 41049 (x y z)

시각화 & 결과

$ pcl_viewer table_scene_lms400.pcd 
$ pcl_viewer table_scene_lms400_downsampled.pcd

원본	원본 확대
결과	결과 확대

UniformSampling

Uniform sampling은 동작 방법은 동일하지만 결과값으로 인덱스(indices)를 출력하여 이후 식별자 정보등으로 활용 가능

The UniformSampling class creates a 3D voxel grid (think about a voxel grid as a set of tiny 3D boxes in space) over the input point cloud data. Then, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with their centroid. This approach is a bit slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately.

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/uniform_sampling.h>


//https://github.com/PointCloudLibrary/pcl/blob/master/test/filters/test_uniform_sampling.cpp
//https://blog.csdn.net/qq_34839823/article/details/103843021
//The UniformSampling class creates a 3D voxel grid (think about a voxel grid as a set of tiny 3D boxes in space) over the input point cloud data. Then, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with their centroid. This approach is a bit slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately.
//http://docs.pointclouds.org/1.8.1/classpcl_1_1_uniform_sampling.html#details
//https://github.com/PointCloudLibrary/pcl/blob/master/tools/uniform_sampling.cpp

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

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // *.PCD 파일 읽기 (https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd)
  pcl::io::loadPCDFile<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

  // 포인트수 출력
  std::cout << "Loaded :" << cloud->width * cloud->height  << std::endl;

  // 오프젝트 생성
  pcl::UniformSampling<pcl::PointXYZ> filter ; 
  filter.setInputCloud (cloud) ;     // 입력 
  filter.setRadiusSearch (0.01F) ;   // 탐색 범위 0.01F
  filter.filter (*cloud_filtered) ;  // 필터 적용 

  // 포인트수 출력  
  std::cout << "Filtered :" << cloud_filtered->width * cloud_filtered->height  << std::endl;  

  // 생성된 포인트클라우드 저장 
  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("uniform_sampling.pcd", *cloud_filtered, false);

  return (0);
}

다운샘플링은 구조체(PCLPointCloud2)에서만 진행 하여야 하나??

Convert

  pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>)

  // Convert to the templated PointCloud
  pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered);