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    • README
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  • Part 2 (중급)
    • README
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    • Chapter03 : Sample Consensus
    • [별첨] 바닥제거 (RANSAC) (70%)
      • PCL-Cpp (70%)
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    • 군집화 (70%)
      • Euclidean-PCL-Cpp (70%)
      • Euclidean-PCL-Python (0%)
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      • Region-Growing-PCL-Cpp (50%)
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      • Model-Outlier-Removal-PCL-Cpp (50%)
      • Progressive-Morphological-Filter-PCL-Cpp (50%)
    • 포인트 탐색과 배경제거 (60%)
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      • Search-Octree-PCL-Python (70%)
      • Search-Kdtree-PCL-Cpp (70%)
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      • Tmp
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      • 인식-GeometricConsistencyGrouping
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      • ICP-ROS 실습 (10%)
    • 재구성 (30%)
      • Smoothig-PCL-Cpp (70%)
      • Smoothig-PCL-Python (70%)
      • Triangulation-PCL-Cpp (70%)
  • Part 3 (고급)
    • README
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      • DenseLidarNet (50%)
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      • Pseudo-LiDAR
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      • Multi3D
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      • 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. 재구성 (30%)

Triangulation-PCL-Cpp (70%)

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Last updated 5 years ago

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코드는 에서 다운로드 가능합니다. 샘플파일은 을 사용하였습니다.

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/surface/gp3.h>
#include <pcl/io/vtk_io.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 both the points and the normals.
    pcl::PointCloud<pcl::PointNormal>::Ptr cloudNormals(new pcl::PointCloud<pcl::PointNormal>);

    // Read a PCD file from disk.
    pcl::io::loadPCDFile<pcl::PointXYZ>("bunny.pcd", *cloud);


    // Normal estimation.
    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);

    // The triangulation object requires the points and normals to be stored in the same structure.
    pcl::concatenateFields(*cloud, *normals, *cloudNormals);
    // Tree object for searches in this new object.
    pcl::search::KdTree<pcl::PointNormal>::Ptr kdtree2(new pcl::search::KdTree<pcl::PointNormal>);
    kdtree2->setInputCloud(cloudNormals);

    // Triangulation object.
    pcl::GreedyProjectionTriangulation<pcl::PointNormal> triangulation;
    // Output object, containing the mesh.
    pcl::PolygonMesh triangles;
    // Maximum distance between connected points (maximum edge length).
    triangulation.setSearchRadius(0.025);
    // Maximum acceptable distance for a point to be considered,
    // relative to the distance of the nearest point.
    triangulation.setMu(2.5);
    // How many neighbors are searched for.
    triangulation.setMaximumNearestNeighbors(100);
    // Points will not be connected to the current point
    // if their normals deviate more than the specified angle.
    triangulation.setMaximumSurfaceAngle(M_PI / 4); // 45 degrees.
    // If false, the direction of normals will not be taken into account
    // when computing the angle between them.
    triangulation.setNormalConsistency(false);
    // Minimum and maximum angle there can be in a triangle.
    // The first is not guaranteed, the second is.
    triangulation.setMinimumAngle(M_PI / 18); // 10 degrees.
    triangulation.setMaximumAngle(2 * M_PI / 3); // 120 degrees.

    // Triangulate the cloud.
    triangulation.setInputCloud(cloudNormals);
    triangulation.setSearchMethod(kdtree2);
    triangulation.reconstruct(triangles);

    // Save to disk.
    pcl::io::saveVTKFile("bunny-mesh.vtk", triangles);
}

원본

결

[이곳]
[bunny.pcd]