PCL学习（⼆）三维模型转点云obj转pcd----PCL实现#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/io/obj_io.h>
#include <pcl/PolygonMesh.h>
//#include <pcl/ros/conversions.h>//formROSMsg所属头⽂件；
#include <pcl/point_cloud.h>
#include <pcl/io/vtk_lib_io.h>//loadPolygonFileOBJ所属头⽂件；
//#include <pcl/visualization/pcl_visualizer.h>
using namespace std;
using namespace pcl;
int main()
{
pcl::PolygonMesh mesh;
pcl::io::loadPolygonFile("sofa.obj", mesh);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromPCLPointCloud2(mesh.cloud, *cloud);
pcl::io::savePCDFileASCII("result.pcd", *cloud);
cout << cloud->size() << endl;
cout << "OK!";
<();
return 0;
}
转换前的obj模型
转换成pcd点云后
提取3D模型的meshes的顶点(Vertex)坐标，对于点云来说点数不够，⽽且在3D模型存在平⾯或者是简单⽴⽅体的情况下⼏乎没有点。
所以⼜需要PCL库了，pcl_mesh_sampling可以轻松解决这个问题。
error parse new它是通过调⽤VTK(Visualization ToolKit)读取模型，在3D模型平⾯均匀地采样点然后⽣成点云，并且你可以选择需要的点数, 以及voxel grid 的采样距离。
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/io/pcd_io.h>
#include <pcl/io/vtk_lib_io.h>
#include <pcl/common/transforms.h>
#include <vtkVersion.h>
#include <vtkPLYReader.h>
#include <vtkOBJReader.h>
#include <vtkTriangle.h>
#include <vtkTriangleFilter.h>
#include <vtkPolyDataMapper.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
inline double
uniform_deviate (int seed)
{
double ran = seed * (1.0 / (RAND_MAX + 1.0));
return ran;
}
inline void
randomPointTriangle (float a1, float a2, float a3, float b1, float b2, float b3, float c1, float c2, float c3,
Eigen::Vector4f& p)
{
float r1 = static_cast<float> (uniform_deviate (rand ()));
float r2 = static_cast<float> (uniform_deviate (rand ()));
float r1sqr = std::sqrt (r1);
float OneMinR1Sqr = (1 - r1sqr);
float OneMinR2 = (1 - r2);
a1 *= OneMinR1Sqr;
a2 *= OneMinR1Sqr;
a3 *= OneMinR1Sqr;
b1 *= OneMinR2;
b2 *= OneMinR2;
b3 *= OneMinR2;
c1 = r1sqr * (r2 * c1 + b1) + a1;
c2 = r1sqr * (r2 * c2 + b2) + a2;
c3 = r1sqr * (r2 * c3 + b3) + a3;
p[0] = c1;
p[1] = c2;
p[2] = c3;
p[3] = 0;
}
inline void
randPSurface (vtkPolyData * polydata, std::vector<double> * cumulativeAreas, double totalArea, Eigen::Vector4f& p, bool calcNormal, Eigen::Vector3f& n)
{
float r = static_cast<float> (uniform_deviate (rand ()) * totalArea);
std::vector<double>::iterator low = std::lower_bound (cumulativeAreas->begin (), cumulativeAreas->end (), r);
vtkIdType el = vtkIdType (low - cumulativeAreas->begin ());
double A[3], B[3], C[3];
vtkIdType npts = 0;
vtkIdType *ptIds = NULL;
polydata->GetCellPoints (el, npts, ptIds);
polydata->GetPoint (ptIds[0], A);
polydata->GetPoint (ptIds[1], B);
polydata->GetPoint (ptIds[2], C);
if (calcNormal)
{
// OBJ: Vertices are stored in a counter-clockwise order by default
Eigen::Vector3f v1 = Eigen::Vector3f (A[0], A[1], A[2]) - Eigen::Vector3f (C[0], C[1], C[2]);
Eigen::Vector3f v2 = Eigen::Vector3f (B[0], B[1], B[2]) - Eigen::Vector3f (C[0], C[1], C[2]);
n = v1.cross (v2);
}
randomPointTriangle (float (A[0]), float (A[1]), float (A[2]),
float (B[0]), float (B[1]), float (B[2]),
float (C[0]), float (C[1]), float (C[2]), p);
}
void
uniform_sampling (vtkSmartPointer<vtkPolyData> polydata, size_t n_samples, bool calc_normal, pcl::PointCloud<pcl::PointNormal> & cloud_out) {
polydata->BuildCells ();
vtkSmartPointer<vtkCellArray> cells = polydata->GetPolys ();
double p1[3], p2[3], p3[3], totalArea = 0;
std::vector<double> cumulativeAreas (cells->GetNumberOfCells (), 0);
size_t i = 0;
vtkIdType npts = 0, *ptIds = NULL;
for (cells->InitTraversal (); cells->GetNextCell (npts, ptIds); i++)
{
polydata->GetPoint (ptIds[0], p1);
polydata->GetPoint (ptIds[1], p2);
polydata->GetPoint (ptIds[2], p3);
totalArea += vtkTriangle::TriangleArea (p1, p2, p3);
cumulativeAreas[i] = totalArea;
}
cloud_size (n_samples);
cloud_out.width = static_cast<pcl::uint32_t> (n_samples);
cloud_out.height = 1;
for (i = 0; i < n_samples; i++)
{
Eigen::Vector4f p;
Eigen::Vector3f n;
randPSurface (polydata, &cumulativeAreas, totalArea, p, calc_normal, n);
cloud_out.points[i].x = p[0];
cloud_out.points[i].y = p[1];
cloud_out.points[i].z = p[2];
if (calc_normal)
{
cloud_out.points[i].normal_x = n[0];
cloud_out.points[i].normal_y = n[1];
cloud_out.points[i].normal_z = n[2];
}
}
}
using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
const int default_number_samples = 100000;
const float default_leaf_size = 0.01f;
void
printHelp (int, char **argv)
{
print_error ("Syntax is: %s input.{ply,obj} output.pcd <options>\n", argv[0]);
print_info ("  where options are:\n");
print_info ("                    -n_samples X      = number of samples (default: ");
print_value ("%d", default_number_samples);
print_info (")\n");
print_info (
"                    -leaf_size X  = the XYZ leaf size for the VoxelGrid -- for data reduction (default: ");
print_value ("%f", default_leaf_size);
print_info (" m)\n");
print_info ("                    -write_normals = flag to write normals to the output pcd\n");
print_info (
"                    -no_vis_result = flag to stop visualizing the generated pcd\n");
}
/* ---[ */
int
main (int argc, char **argv)
{
print_info ("Convert a CAD model to a point cloud using uniform sampling. For more information, use: %s -h\n",              argv[0]);
if (argc < 3)
{
printHelp (argc, argv);
return (-1);
}
// Parse command line arguments
int SAMPLE_POINTS_ = default_number_samples;
parse_argument (argc, argv, "-n_samples", SAMPLE_POINTS_);
float leaf_size = default_leaf_size;
parse_argument (argc, argv, "-leaf_size", leaf_size);
bool vis_result = ! find_switch (argc, argv, "-no_vis_result");
const bool write_normals = find_switch (argc, argv, "-write_normals");
// Parse the command line arguments for .ply and PCD files
std::vector<int> pcd_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
if (pcd_file_indices.size () != 1)
{
print_error ("Need a single output PCD file to continue.\n");
return (-1);
}
std::vector<int> ply_file_indices = parse_file_extension_argument (argc, argv, ".ply");
std::vector<int> obj_file_indices = parse_file_extension_argument (argc, argv, ".obj");
if (ply_file_indices.size () != 1 && obj_file_indices.size () != 1)
{
print_error ("Need a single input PLY/OBJ file to continue.\n");
return (-1);
}
vtkSmartPointer<vtkPolyData> polydata1 = vtkSmartPointer<vtkPolyData>::New ();
if (ply_file_indices.size () == 1)
{
pcl::PolygonMesh mesh;
pcl::io::loadPolygonFilePLY (argv[ply_file_indices[0]], mesh);
pcl::io::mesh2vtk (mesh, polydata1);
}
else if (obj_file_indices.size () == 1)
{
vtkSmartPointer<vtkOBJReader> readerQuery = vtkSmartPointer<vtkOBJReader>::New ();
readerQuery->SetFileName (argv[obj_file_indices[0]]);
readerQuery->Update ();
polydata1 = readerQuery->GetOutput ();
}
//make sure that the polygons are triangles!
vtkSmartPointer<vtkTriangleFilter> triangleFilter = vtkSmartPointer<vtkTriangleFilter>::New ();
#if VTK_MAJOR_VERSION < 6
triangleFilter->SetInput (polydata1);
#else
triangleFilter->SetInputData (polydata1);
#endif
triangleFilter->Update ();
vtkSmartPointer<vtkPolyDataMapper> triangleMapper = vtkSmartPointer<vtkPolyDataMapper>::New ();
triangleMapper->SetInputConnection (triangleFilter->GetOutputPort ());
triangleMapper->Update ();
polydata1 = triangleMapper->GetInput ();
bool INTER_VIS = false;
if (INTER_VIS)
{
visualization::PCLVisualizer vis;
vis.addModelFromPolyData (polydata1, "mesh1", 0);
vis.setRepresentationToSurfaceForAllActors ();
vis.spin ();
}
pcl::PointCloud<pcl::PointNormal>::Ptr cloud_1 (new pcl::PointCloud<pcl::PointNormal>);
uniform_sampling (polydata1, SAMPLE_POINTS_, write_normals, *cloud_1);
if (INTER_VIS)
{
visualization::PCLVisualizer vis_sampled;
vis_sampled.addPointCloud<pcl::PointNormal> (cloud_1);
if (write_normals)
vis_sampled.addPointCloudNormals<pcl::PointNormal> (cloud_1, 1, 0.02f, "cloud_normals");    vis_sampled.spin ();
}
// Voxelgrid
VoxelGrid<PointNormal> grid_;
grid_.setInputCloud (cloud_1);
grid_.setLeafSize (leaf_size, leaf_size, leaf_size);
pcl::PointCloud<pcl::PointNormal>::Ptr voxel_cloud (new pcl::PointCloud<pcl::PointNormal>);  grid_.filter (*voxel_cloud);
if (vis_result)
{
visualization::PCLVisualizer vis3 ("VOXELIZED SAMPLES CLOUD");
vis3.addPointCloud<pcl::PointNormal> (voxel_cloud);
if (write_normals)
vis3.addPointCloudNormals<pcl::PointNormal> (voxel_cloud, 1, 0.02f, "cloud_normals");
vis3.spin ();
}
if (!write_normals)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);
// Strip uninitialized normals from cloud:
pcl::copyPointCloud (*voxel_cloud, *cloud_xyz);
savePCDFileASCII (argv[pcd_file_indices[0]], *cloud_xyz);
}
else
{
savePCDFileASCII (argv[pcd_file_indices[0]], *voxel_cloud);
}
}

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