曲面重建

#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>
#include "resolution.h"

int main(int argc, char** argv)
{
	// Load input file into a PointCloud<T> with an appropriate type
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PCLPointCloud2 cloud_blob;
	pcl::io::loadPCDFile(argv[1], cloud_blob);
	pcl::fromPCLPointCloud2(cloud_blob, *cloud);
	//* the data should be available in cloud

	double resolution = computeCloudResolution(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(5 * resolution);  //设置连接点之间的最大距离（最大边长）用于确定k近邻的球半径【默认值 0】
	// Set typical values for the parameters
	gp3.setMu(2.5); //设置最近邻距离的乘子，以得到每个点的最终搜索半径【默认值 0】
	gp3.setMaximumNearestNeighbors(100); //设置搜索的最近邻点的最大数量
	gp3.setMaximumSurfaceAngle(M_PI / 4); // 45 degrees（pi）最大平面角
	gp3.setMinimumAngle(M_PI / 18);  // 10 degrees 每个三角的最小角度
	gp3.setMaximumAngle(2 * M_PI / 3); // 120 degrees 每个三角的最大角度
	gp3.setNormalConsistency(false); //如果法向量一致，设置为true

	// 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();

	// show
	pcl::visualization::PCLVisualizer viewer;
	viewer.addPolygonMesh(triangles, "mesh");
	viewer.spin();

	// Finish
	return (0);
}

#include <pcl/surface/poisson.h>

pcl::Poisson<pcl::PointNormal> 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);
(其他同上)

#include <pcl/surface/marching_cubes_hoppe.h>

pcl::MarchingCubes<pcl::PointNormal>::Ptr mc(new pcl::MarchingCubesHoppe<pcl::PointNormal>);
//设置MarchingCubes对象的参数
mc->setIsoLevel(0.0f);
mc->setGridResolution(5, 5, 5);
mc->setPercentageExtendGrid(0.0f);
mc->setSearchMethod(tree2);
mc->setInputCloud(cloud_with_normals);

pcl::PolygonMesh mesh;//执行重构，结果保存在mesh中
mc->reconstruct(mesh);
(其他同上)

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/surface/mls.h>
#include <pcl/visualization/pcl_visualizer.h>
#include "resolution.h"

int main(int argc, char** argv)
{
	// Load input file into a PointCloud<T> with an appropriate type
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>());
	// Load bun0.pcd -- should be available with the PCL archive in test 
	pcl::io::loadPCDFile(argv[1], *cloud);

	double resolution = computeCloudResolution(cloud);

	// Create a KD-Tree
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);

	// Output has the PointNormal type in order to store the normals calculated by MLS
	pcl::PointCloud<pcl::PointNormal> mls_points;

	// Init object (second point type is for the normals, even if unused)
	pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls;
	mls.setInputCloud(cloud);
	mls.setComputeNormals(true); //我们都知道表面重构时需要估计点云的法向量，这里MLS提供了一种方法来估计点云法向量。（如果是true的话注意输出数据格式）。
	mls.setPolynomialFit(true); //对于法线的估计是有多项式还是仅仅依靠切线。
	mls.void setPolynomialOrder(3); //MLS拟合曲线的阶数，这个阶数在构造函数里默认是2，但是参考文献给出最好选择3或者4
	mls.setSearchMethod(tree);
	mls.setSearchRadius(10 * resolution);
	// Reconstruct
	mls.process(mls_points);
	
	// Save output
	//pcl::io::savePCDFile("bun0-mls.pcd", mls_points);
	// show
	pcl::visualization::PCLVisualizer viewer_1;
	viewer_1.addPointCloud(cloud, "cloud");
	viewer_1.spin();

	pcl::visualization::PCLVisualizer viewer_2;
	pcl::PointCloud<pcl::PointXYZ>::Ptr result(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::copyPointCloud(mls_points, *result);
	viewer_2.addPointCloud(result, "mls_points");
	viewer_2.spin();

	return 0;
}