1.基于区域生长的分割
算法的输出是一个聚类集合，每个聚类集合被认为是同一光滑表面的一部分。首先依据点的曲率值对点进行排序，区域生长算法是从曲率最小的点开始生长的，这个点就是初始种子点，初始种子点所在区域就是最平滑的区域，一般场景中平面区域较大，这样从最平滑的区域开始生长可减少分割区域的总数，提高效率。
算法流程：

2.代码

#include <iostream>
#include <vector>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <windows.h>
#include <stdio.h>
#include <psapi.h>
using namespace pcl::console;
void PrintMemoryInfo()
{
	HANDLE hProcess;
	PROCESS_MEMORY_COUNTERS pmc;

	hProcess = GetCurrentProcess();
	printf("\nProcess ID: %u\n", hProcess);

	//输出进程使用的内存信息
	if (NULL == hProcess)
		return;

	if (GetProcessMemoryInfo(hProcess, &pmc, sizeof(pmc)))
	{
		printf("\tPageFaultCount: 0x%08X\n", pmc.PageFaultCount);
		printf("\tPeakWorkingSetSize: 0x%08X\n",
			pmc.PeakWorkingSetSize);
		printf("\tWorkingSetSize: 0x%08X\n", pmc.WorkingSetSize);
		printf("\tQuotaPeakPagedPoolUsage: 0x%08X\n",
			pmc.QuotaPeakPagedPoolUsage);
		printf("\tQuotaPagedPoolUsage: 0x%08X\n",
			pmc.QuotaPagedPoolUsage);
		printf("\tQuotaPeakNonPagedPoolUsage: 0x%08X\n",
			pmc.QuotaPeakNonPagedPoolUsage);
		printf("\tQuotaNonPagedPoolUsage: 0x%08X\n",
			pmc.QuotaNonPagedPoolUsage);
		printf("\tPagefileUsage: 0x%08X\n", pmc.PagefileUsage);
		printf("\tPeakPagefileUsage: 0x%08X\n",
			pmc.PeakPagefileUsage);
	}
	CloseHandle(hProcess);
}

int main(int argc, char** argv)
{
	//如果输入参数小于1个，输出提示
	if (argc < 2)
	{
		std::cout << ".exe xx.pcd -kn 50 -bc 0 -fc 10.0 -nc 0 -st 30 -ct 0.05" << endl;
		return 0;
	}
	time_t start, end, diff[5], option;
	start = time(0);
	int KN_normal = 50; //设置默认输入参数
	bool Bool_Cuting = false;//设置默认输入参数
	float far_cuting = 10, near_cuting = 0, SmoothnessThreshold = 30.0, CurvatureThreshold = 0.05;//设置默认输入参数
	parse_argument(argc, argv, "-kn", KN_normal);
	parse_argument(argc, argv, "-bc", Bool_Cuting);
	parse_argument(argc, argv, "-fc", far_cuting);
	parse_argument(argc, argv, "-nc", near_cuting);
	parse_argument(argc, argv, "-st", SmoothnessThreshold);
	parse_argument(argc, argv, "-ct", CurvatureThreshold);//设置输入参数方式

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	// 加载输入点云数据
	if (pcl::io::loadPCDFile <pcl::PointXYZ>(argv[1], *cloud) == -1)
	{
		std::cout << "Cloud reading failed." << std::endl;
		return (-1);
	}
	end = time(0);
	diff[0] = difftime(end, start);
	PCL_INFO("\Loading pcd file takes(seconds): %d\n", diff[0]);
	//Noraml estimation step(1 parameter)
	pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> >(new pcl::search::KdTree<pcl::PointXYZ>);//创建一个指向kd树搜索对象的共享指针
	pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;//创建法线估计对象
	normal_estimator.setSearchMethod(tree);//设置搜索方法
	normal_estimator.setInputCloud(cloud);//设置法线估计对象输入点集
	normal_estimator.setKSearch(KN_normal);// 设置用于法向量估计的k近邻数目
	normal_estimator.compute(*normals);//计算并输出法向量
	end = time(0);
	diff[1] = difftime(end, start) - diff[0];
	PCL_INFO("\Estimating normal takes(seconds): %d\n", diff[1]);
	//optional step: cutting the part are far from the orignal point in Z direction.2 parameters
	pcl::IndicesPtr indices(new std::vector <int>);//创建一组索引
	if (Bool_Cuting)//判断是否需要直通滤波
	{

		pcl::PassThrough<pcl::PointXYZ> pass;//设置直通滤波器对象
		pass.setInputCloud(cloud);//设置输入点云
		pass.setFilterFieldName("z");//设置指定过滤的维度
		pass.setFilterLimits(near_cuting, far_cuting);//设置指定纬度过滤的范围
		pass.filter(*indices);//执行滤波，保存滤波结果在上述索引中
	}


	// 区域生长算法的5个参数
	pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;//创建区域生长分割对象
	reg.setMinClusterSize(50);//设置一个聚类需要的最小点数
	reg.setMaxClusterSize(1000000);//设置一个聚类需要的最大点数
	reg.setSearchMethod(tree);//设置搜索方法
	reg.setNumberOfNeighbours(30);//设置搜索的临近点数目
	reg.setInputCloud(cloud);//设置输入点云
	if (Bool_Cuting)reg.setIndices(indices);//通过输入参数设置，确定是否输入点云索引
	reg.setInputNormals(normals);//设置输入点云的法向量
	reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//设置平滑阈值，小于阈值是同一类
	reg.setCurvatureThreshold(CurvatureThreshold);//设置曲率阈值

	std::vector <pcl::PointIndices> clusters;
	reg.extract(clusters);//获取聚类的结果，分割结果保存在点云索引的向量中。
	end = time(0);
	diff[2] = difftime(end, start) - diff[0] - diff[1];
	PCL_INFO("\Region growing takes(seconds): %d\n", diff[2]);

	std::cout << "Number of clusters is equal to " << clusters.size() << std::endl;//输出聚类的数量
	std::cout << "First cluster has " << clusters[0].indices.size() << " points." << endl;//输出第一个聚类的数量
	std::cout << "These are the indices of the points of the initial" <<
		std::endl << "cloud that belong to the first cluster:" << std::endl;
	/* int counter = 0;
	while (counter < clusters[0].indices.size ())
	{
	std::cout << clusters[0].indices[counter] << ", ";
	counter++;
	if (counter % 10 == 0)
	std::cout << std::endl;
	}
	std::cout << std::endl;
	*/
	PrintMemoryInfo();
	pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud();
	pcl::visualization::CloudViewer viewer("区域增长分割方法");
	viewer.showCloud(colored_cloud);
	while (!viewer.wasStopped())
	{
	}//进行可视化

	return (0);
}

3.显示

其它颜色的点，说明这些本属于聚类的点数量过多或过少，不存在设定的最大聚类和最小聚类数目之间。