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Tutorial
  • INTRO
  • Part 0 (개요)
    • README
    • 3D 영상처리
    • [별첨] PCL & PCD란 (100%)
    • chapter02 : PCL 설치 (100%)
    • chapter03 : ROS 실습 준비(100%)
  • Part 1 (초급)
    • README
    • PCL 기반 로봇 비젼
    • [별첨] 파일 생성 및 입출력 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • Open3D-Python (70%)
      • ROS 실습 (90%)
    • Filter
    • [별첨] 샘플링 (70%)
      • 다운샘플링-PCL-Cpp (70%)
      • 다운샘플링-PCL-Python (50%)
      • 업샘플링-PCL-Cpp (70%)
      • ROS 실습 (90%)
    • [별첨] 관심 영역 설정 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • [별첨] 노이즈 제거 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (50%)
      • ROS 실습 (90%)
  • Part 2 (중급)
    • README
    • Kd-Tree/Octree Search
    • Chapter03 : Sample Consensus
    • [별첨] 바닥제거 (RANSAC) (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • 군집화 (70%)
      • Euclidean-PCL-Cpp (70%)
      • Euclidean-PCL-Python (0%)
      • Conditional-Euclidean-PCL-Cpp (50%)
      • DBSCAN-PCL-Python (0%)
      • Region-Growing-PCL-Cpp (50%)
      • Region-Growing-RGB-PCL-Cpp (50%)
      • Min-Cut-PCL-Cpp (50%)
      • Model-Outlier-Removal-PCL-Cpp (50%)
      • Progressive-Morphological-Filter-PCL-Cpp (50%)
    • 포인트 탐색과 배경제거 (60%)
      • Search-Octree-PCL-Cpp (70%)
      • Search-Octree-PCL-Python (70%)
      • Search-Kdtree-PCL-Cpp (70%)
      • Search-Kdtree-PCL-Python (70%)
      • Compression-PCL-Cpp (70%)
      • DetectChanges-PCL-Cpp (50%)
      • DetectChanges-PCL-Python (50%)
    • 특징 찾기 (50%)
      • PFH-PCL-Cpp
      • FPFH-PCL-Cpp
      • Normal-PCL-Cpp (70%)
      • Normal-PCL-Python (80%)
      • Tmp
    • 분류/인식 (30%)
      • 인식-GeometricConsistencyGrouping
      • SVM-RGBD-PCL-Python (70%)
      • SVM-LIDAR-PCL-Python (0%)
      • SVM-ROS (0%)
    • 정합 (70%)
      • ICP-PCL-Cpp (70%)
      • ICP-ROS 실습 (10%)
    • 재구성 (30%)
      • Smoothig-PCL-Cpp (70%)
      • Smoothig-PCL-Python (70%)
      • Triangulation-PCL-Cpp (70%)
  • Part 3 (고급)
    • README
    • 딥러닝 기반 학습 데이터 생성 (0%)
      • PointGAN (90%)
      • AutoEncoder (0%)
    • 딥러닝 기반 샘플링 기법 (0%)
      • DenseLidarNet (50%)
      • Point Cloud Upsampling Network
      • Pseudo-LiDAR
    • 딥러닝 기반 자율주행 탐지 기술 (0%)
    • 딥러닝 기반 자율주행 분류 기술 (0%)
      • Multi3D
      • PointNet
      • VoxelNet (50%)
      • YOLO3D
      • SqueezeSeg
      • butNet
  • Snippets
    • PCL-Snippets
    • PCL-Python-Helper (10%)
    • Lidar Data Augmentation
  • Appendix
    • 시각화Code
    • 시각화툴
    • Annotation툴
    • Point Cloud Libraries (0%)
    • 데이터셋
    • Cling_PCL
    • 참고 자료
    • 작성 계획_Tips
    • 용어집
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  1. Part 2 (중급)
  2. 군집화 (70%)

Model-Outlier-Removal-PCL-Cpp (50%)

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

// Filtering a PointCloud using ModelOutlierRemoval
// http://pointclouds.org/documentation/tutorials/model_outlier_removal.php#model-outlier-removal

int
main ()
{
  // *.PCD 파일 읽기 
  // https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/sample/sphere_pointcloud_with_noise.pcd
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::io::loadPCDFile <pcl::PointXYZ> ("sphere_pointcloud_with_noise.pcd", *cloud);

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

  std::cerr << "Cloud before filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
    std::cout << "    " << cloud->points[i].x << " " << cloud->points[i].y << " " << cloud->points[i].z << std::endl;

  // 2. filter sphere:
  // 2.1 generate model:
  // modelparameter for this sphere:
  // position.x: 0, position.y: 0, position.z:0, radius: 1
  pcl::ModelCoefficients sphere_coeff;
  sphere_coeff.values.resize (4);
  sphere_coeff.values[0] = 0;
  sphere_coeff.values[1] = 0;
  sphere_coeff.values[2] = 0;
  sphere_coeff.values[3] = 1;

  pcl::ModelOutlierRemoval<pcl::PointXYZ> sphere_filter;
  sphere_filter.setModelCoefficients (sphere_coeff);
  sphere_filter.setThreshold (0.05);
  sphere_filter.setModelType (pcl::SACMODEL_SPHERE);
  sphere_filter.setInputCloud (cloud);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_sphere_filtered (new pcl::PointCloud<pcl::PointXYZ>);
  sphere_filter.filter (*cloud_sphere_filtered);

  std::cerr << "Sphere after filtering: " << std::endl;
  for (std::size_t i = 0; i < cloud_sphere_filtered->points.size (); ++i)
    std::cout << "    " << cloud_sphere_filtered->points[i].x << " " << cloud_sphere_filtered->points[i].y << " " << cloud_sphere_filtered->points[i].z
        << std::endl;

  return (0);
}
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Last updated 5 years ago

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