基于PCL三维曲面重建

前言

曲面重建可以用于逆向工程、数据可视化、自动化建模等领域。本文希望能够将曲面重建运用在点云分割后的显示上，以增强点云分割后的可视化效果。
PCL中目前实现了多种基于点云的曲面重建算法，如：泊松曲面重建、贪婪投影三角化、移动立方体、EarClipping等算法。下面我将对泊松曲面重建算法和贪婪投影三角化算法进行介绍，具体算法的原理这里就不过多介绍了，只将代码和实验效果贴出共大家交流学习。
代码来源：这里写链接内容
http://pointclouds.org/documentation/tutorials/greedy_projection.php#greedy-triangulation

贪婪投影三角化算法

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/features/normal_3d.h>
#include <pcl/surface/gp3.h>
#include <pcl/visualization/pcl_visualizer.h>

int
main(/*int argc, char** argv*/)
{
    // Load input file into a PointCloud with an appropriate type
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PCLPointCloud2 cloud_blob;
    pcl::io::loadPCDFile("bun0.pcd", cloud_blob);
    pcl::fromPCLPointCloud2(cloud_blob, *cloud);
    //* the data should be available in cloud

    // Normal estimation*
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> n;
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
    tree->setInputCloud(cloud);
    n.setInputCloud(cloud);
    n.setSearchMethod(tree);
    n.setKSearch(20);
    n.compute(*normals);
    //* normals should not contain the point normals + surface curvatures

    // Concatenate the XYZ and normal fields*
    pcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_normals(new pcl::PointCloud<pcl::PointNormal>);
    pcl::concatenateFields(*cloud, *normals, *cloud_with_normals);
    //* cloud_with_normals = cloud + normals

    // Create search tree*
    pcl::search::KdTree<pcl::PointNormal>::Ptr tree2(new pcl::search::KdTree<pcl::PointNormal>);
    tree2->setInputCloud(cloud_with_normals);

    // Initialize objects
    pcl::GreedyProjectionTriangulation<pcl::PointNormal> gp3;
    pcl::PolygonMesh triangles;

    // Set the maximum distance between connected points (maximum edge length)
    gp3.setSearchRadius(0.025);

    // Set typical values for the parameters
    gp3.setMu(2.5);
    gp3.setMaximumNearestNeighbors(100);
    gp3.setMaximumSurfaceAngle(M_PI / 4); // 45 degrees
    gp3.setMinimumAngle(M_PI / 18); // 10 degrees
    gp3.setMaximumAngle(2 * M_PI / 3); // 120 degrees
    gp3.setNormalConsistency(false);

    // Get result
    gp3.setInputCloud(cloud_with_normals);
    gp3.setSearchMethod(tree2);
    gp3.reconstruct(triangles);

    // Additional vertex information
    std::vector<int> parts = gp3.getPartIDs();
    std::vector<int> states = gp3.getPointStates();

    boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
    viewer->setBackgroundColor(0, 0, 0);

    viewer->addPolygonMesh(triangles, "triangles");

    viewer->addCoordinateSystem(1.0);
    viewer->initCameraParameters();
    while (!viewer->wasStopped()){
        viewer->spinOnce(100);
        boost::this_thread::sleep(boost::posix_time::microseconds(100000));
    }


    // Finish
    return (0);
}

如果想要只显示三角化后的网格，添加下面代码：

viewer->addPolylineFromPolygonMesh(triangles);

只显示三角网：

泊松曲面重建算法

    pcl::Poisson pn;
    pn.setConfidence(false);
    pn.setDegree(2);
    pn.setDepth(8);
    pn.setIsoDivide(8);
    pn.setManifold(false);
    pn.setOutputPolygons(false);
    pn.setSamplesPerNode(3.0);
    pn.setScale(1.25);
    pn.setSolverDivide(8);
    pn.setSearchMethod(tree2);
    pn.setInputCloud(cloud_with_normals);
    pcl::PolygonMesh mesh;
    pn.performReconstruction(mesh);

只显示三角网：