#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!";
cin.get();
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。

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);
n.normalize ();
}
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_out.points.resize (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);
}
}

  

PCL学习(二)三维模型转点云 obj转pcd----PCL实现的更多相关文章

  1. PCL学习(四)点云转换为网格

    Remove needless points compute normals surface reconstruction get texture(param 4096 basic) save pro ...

  2. 点云库PCL学习

    1. 点云的提取 点云的获取:RGBD获取 点云的获取:图像匹配获取(通过摄影测量提取点云数据) 点云的获取:三维激光扫描仪 2. PCL简介 PCL是Point Cloud Library的简称,是 ...

  3. PCL学习(一)从PLY文件读入点云数据

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

  4. 从零开始一起学习SLAM | 你好,点云

    本文提纲 先热热身点云是啥你知道点云优缺点吗?点云库PCL:开发者的福音PCL安装指北炒鸡简单的PCL实践留个作业再走先热热身 小白:hi,师兄,好久不见师兄:师妹好,上周单应矩阵作业做了吗?小白:嗯 ...

  5. PCL学习八叉树

    建立空间索引在点云数据处理中有着广泛的应用,常见的空间索引一般 是自顶而下逐级划分空间的各种空间索引结构,比较有代表性的包括BSP树,KD树,KDB树,R树,四叉树,八叉树等索引结构,而这些结构中,K ...

  6. SpringCloud学习(二):微服务入门实战项目搭建

    一.开始使用Spring Cloud实战微服务 1.SpringCloud是什么? 云计算的解决方案?不是 SpringCloud是一个在SpringBoot的基础上构建的一个快速构建分布式系统的工具 ...

  7. DjangoRestFramework学习二之序列化组件、视图组件 serializer modelserializer

      DjangoRestFramework学习二之序列化组件.视图组件   本节目录 一 序列化组件 二 视图组件 三 xxx 四 xxx 五 xxx 六 xxx 七 xxx 八 xxx 一 序列化组 ...

  8. PCL学习笔记1

    先贴一段代码,从别处抄来的 #include <iostream> #include <pcl/io/pcd_io.h> #include <pcl/point_type ...

  9. Android JNI学习(二)——实战JNI之“hello world”

    本系列文章如下: Android JNI(一)——NDK与JNI基础 Android JNI学习(二)——实战JNI之“hello world” Android JNI学习(三)——Java与Nati ...

随机推荐

  1. 【概率论】5-2:伯努利和二项分布(The Bernoulli and Binomial Distributions)

    title: [概率论]5-2:伯努利和二项分布(The Bernoulli and Binomial Distributions) categories: - Mathematic - Probab ...

  2. CSPS模拟94

    我好菜啊...... %%%迪神AK 虽然考试成绩不太好,但至少能想到正解了,也不会菜到打不出暴力. T1:想了半天不会,发现直接打高精可以拿到80分,就赶紧码完扔了,结果正解是利用double避免了 ...

  3. python 监视和控制鼠标键盘的输入(使用pynput 而非pyhook)

    百度上搜到的文章大多基于pyhook, pip不能直接安装,托管在sourceForge上的代码仓库也找不到. google上发现可以使用pynput,貌似控制更为简单,而且可以直接使用pip安装 示 ...

  4. SpringMVC 捕获参数绑定失败时的异常

    SpringMVC配置数据验证(JSR-303)中提到了用String类型的域来绑定Ajax中的非法类型的参数. 这样做的目的是一旦发生一种情况,后端可以返回一个自定类的返回值,而不是返回Spring ...

  5. Spring boot + mybatis 只读取到一个jar包中的mapper配置文件

    采用spring boot  开发了一个多模块项目,有多个模块中都有mapper配置文件. 采用如下的方式配置,制度去到了一个模块jar包中配置文件: @Bean(name = "sqlSe ...

  6. BigDecimal 相关

    一.BigDecimal 精度设置 BigDecimal setScale(int newScale, int roundingMode): newScale:小数位数, RoundingMode是一 ...

  7. linux listen()

    listen(等待连接) 相关函数 socket,bind,accept,connect表头文件 #include<sys/socket.h>定义函数 int listen(int s,i ...

  8. Centos7 安装svn步骤(宝塔面板)

    1.运行以下命令安装SVN yum install subversion 2.运行以下命令创建目录 mkdir /var/svn   3.依次运行以下命令创建版本库 cd /var/svn svnad ...

  9. OpenTK学习笔记(2)-工作窗口的三种方法创建方法(winfrom窗体控件形式创建)

    参考资料: https://social.msdn.microsoft.com/Forums/zh-TW/1b781685-c670-4338-953d-1957a8f24a66/opentkglco ...

  10. Windows 下 把EXE 程序变成服务运行

    1. 下载  instsrv.exe 和 srvany.exe   我下载的地址  [点击打开链接][https://www.cr173.com/soft/64394.html] 2. cmd  cd ...