PCL中点云可视化：坐标字段、随机、单一颜色、某一属性值设置颜色、带RGB数据、法向量显示

PCL中viewer添加并显示的点云过于简单，现总结常见的几种点云渲染方式，便于点云结果的显示。

（1）按照点云坐标x、y、z坐标值中字段给点云进行赋值渲染

#include 
#include 
#include 
#include 
#include 
using namespace std;
int main(int argc, char* argv[])
{
	pcl::PointCloud::Ptr Cloud(new pcl::PointCloud);
	
	pcl::io::loadPCDFile("XX.pcd", *Cloud);//读入点云数据

	pcl::visualization::PCLVisualizer viewer("点云");
	viewer.setBackgroundColor(0, 0, 0);
	
	pcl::visualization::PointCloudColorHandlerGenericField fildColor(Cloud, "z");//按照z字段进行渲染
	viewer.addPointCloud(Cloud, fildColor, "sample");//显示点云，其中fildColor为颜色显示
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample");//设置点云大小

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}


	return 0;
}

按照z高程渲染图：

按照x坐标渲染图：

（2）给点云单独赋予某一颜色

#include 
#include 
#include 
#include 
#include 
using namespace std;
int main(int argc, char* argv[])
{
	pcl::PointCloud::Ptr Cloud(new pcl::PointCloud);
	
	pcl::io::loadPCDFile("XX.pcd", *Cloud);//读入点云数据

	pcl::visualization::PCLVisualizer viewer("点云");
	viewer.setBackgroundColor(0, 0, 0);
	
	pcl::visualization::PointCloudColorHandlerCustom singleColor(Cloud, 0,255,0);//0-255  设置成绿色
	viewer.addPointCloud(Cloud, singleColor, "sample");//显示点云，其中fildColor为颜色显示
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample");//设置点云大小

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}


	return 0;
}

点云设置成绿色的结果：

（3）随机生成颜色

int main(int argc, char* argv[])
{
	pcl::PointCloud::Ptr Cloud(new pcl::PointCloud);
	
	pcl::io::loadPCDFile("xx.pcd", *Cloud);//读入点云数据

	pcl::visualization::PCLVisualizer viewer("点云");
	viewer.setBackgroundColor(0, 0, 0);
	
	pcl::visualization::PointCloudColorHandlerRandom RandomColor(Cloud);//随机给点云生成颜色
	viewer.addPointCloud(Cloud, RandomColor, "sample");//显示点云，其中fildColor为颜色显示
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample");//设置点云大小

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}


	return 0;
}

随机生成的颜色结果图：

（4）采用某一属性值进行显示

在实际中会遇到根据某一特征对点集进行分类，为便于从视觉上直接判断该特征的区别度，可以依据该特征值设置成点的颜色，进行渲染，看其效果。

如下代码是依据法向量方差特征的显示图，代码及图如下：

#include"CalculateFeatures.h"
#include
#include
#include
#include 
#include 
#include 
#include"IO.h"
using namespace std;
#define e 2.718
void main()
{

	CalculateFeatures CalExample;
	IO IOExample;

	ifstream infile("Area1.txt", ios::in);
	pcl::PointXYZ temp;
	char line[128];
	int label;
	vector AllPoints;
	vector LabelArr;
	vector UnclassPoints;//未分类点 格网化高程点特征只针对未分类的点
	vector GroundPoints;//已经分类的点，为地面点

	while (infile.getline(line, sizeof(line)))
	{
		stringstream word(line);
		word >> temp.x;
		word >> temp.y;
		word >> temp.z;
		word >> label;
		AllPoints.push_back(temp);
		LabelArr.push_back(label);
		if (label == 1)
		{
			UnclassPoints.push_back(temp);
		}
		else if (label == 2)
		{
			GroundPoints.push_back(temp);
		}
	}

	pcl::PointCloud::Ptr UnclassPointsPtr(new pcl::PointCloud);
	UnclassPointsPtr = IOExample.PointXYZ2Ptr(UnclassPoints);
	vector NormalHis;
	NormalHis = CalExample.AngleHistogramZ(UnclassPointsPtr, 60, 15, 6);
	

	pcl::PointCloud::Ptr new_cloud(new pcl::PointCloud);
	new_cloud->width = UnclassPoints.size();
	new_cloud->height = 1;
	new_cloud->is_dense = false;
	new_cloud->points.resize(new_cloud->width*new_cloud->height);


	for (int i = 0; i < UnclassPoints.size(); i++)
	{
		new_cloud->points[i].x = UnclassPoints[i].x;
		new_cloud->points[i].y = UnclassPoints[i].y;
		new_cloud->points[i].z = UnclassPoints[i].z;	
		new_cloud->points[i].rgb = -NormalHis[i];//
	}

	pcl::visualization::PointCloudColorHandlerGenericField fildColor(new_cloud, "rgb");

	pcl::visualization::PCLVisualizer viewer("点云特征");
	//pcl::visualization::PointCloudColorHandlerRGBField rgb(new_cloud);
	viewer.setBackgroundColor(0, 0, 0);
	viewer.addPointCloud(new_cloud, fildColor, "inCloud");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "inCloud");
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}

}

可以看出该类特征取值范围等

（5）带RGB值的可视化

有些点云数据的RGB值是分开的，为R、G、B，将RGB赋值给该点作为颜色进行显示，代码如下

#include"CalculateFeatures.h"
#include
#include
#include
#include 
#include 
#include 
#include 
#include"IO.h"
using namespace std;
#define e 2.718
#include"DataStruct.h"
#include"EuclideanCluster.h"
#include"NearestKD.h"
#include"stdint.h"
void main()
{

	IO IOExample;
	CalculateFeatures CalExample;
	arrayoperation ArrExample;


	ifstream infile("D:\\XX.txt", ios::in);
	struct ColorRGB
	{
		double R;
		double G;
		double B;
	};
	vector PointCluster;
	vectorRGBVec;

	pcl::PointXYZ TemPoint;
	ColorRGB TempRGB;
	char line[256];
	while (infile.getline(line, sizeof(line)))
	{
		stringstream word(line);
		word >> TemPoint.x;
		word >> TemPoint.y;
		word >> TemPoint.z;
		word >> TempRGB.R;
		word >> TempRGB.G;
		word >> TempRGB.B;
		RGBVec.push_back(TempRGB);
		PointCluster.push_back(TemPoint);
	}
	
	pcl::PointCloud::Ptr new_cloud(new pcl::PointCloud);
	new_cloud->width = PointCluster.size();
	new_cloud->height = 1;
	new_cloud->is_dense = false;
	new_cloud->points.resize(new_cloud->width*new_cloud->height);


	for (int i = 0; i < PointCluster.size(); i++)
	{
		new_cloud->points[i].x = PointCluster[i].x;
		new_cloud->points[i].y = PointCluster[i].y;
		new_cloud->points[i].z = PointCluster[i].z;
		int R = RGBVec[i].R;
		int G = RGBVec[i].G;
		int B = RGBVec[i].B;



		new_cloud->points[i].r = R;
		new_cloud->points[i].g = G;
		new_cloud->points[i].b = B;
		
	}

	pcl::visualization::PointCloudColorHandlerRGBFieldfildColor(new_cloud);

	pcl::visualization::PCLVisualizer viewer("彩色点云数据");
	viewer.setBackgroundColor(0, 0, 0);
	viewer.addPointCloud(new_cloud, fildColor, "inCloud");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "inCloud");
	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}

}

（6）法向量的颜色表示

#include 
#include 
#include 
#include 
#include 
#include 
void main()
{
	//定义一个点云cloud
	pcl::PointCloud::Ptr cloud(new pcl::PointCloud);

	pcl::io::loadPCDFile("E:\\program_study\\C++\\pcd_data\\bunny.pcd", *cloud);//读入点云数据

	// Normal estimation*
	//法向计算
	pcl::NormalEstimation n;
	pcl::PointCloud::Ptr normals(new pcl::PointCloud);
	//建立kdtree来进行近邻点集搜索
	pcl::search::KdTree::Ptr tree(new pcl::search::KdTree);
	//为kdtree添加点云数据
	tree->setInputCloud(cloud);

	n.setInputCloud(cloud);
	n.setSearchMethod(tree);
	//点云法向计算时，需要搜索的近邻点大小
	n.setKSearch(20);
	//开始进行法向计算
	n.compute(*normals);
	//* normals should not contain the point normals + surface curvatures
	
	//显示类
	pcl::visualization::PCLVisualizer viewer("Cloud Viewer");

	//设置背景色
	viewer.setBackgroundColor(0, 0, 0);

	//按照z值进行渲染点的颜色
	pcl::visualization::PointCloudColorHandlerGenericField fildColor(cloud, "z");

	//添加需要显示的点云数据
	viewer.addPointCloud(cloud, fildColor, "sample cloud");

	//设置点显示大小
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");

	//添加需要显示的点云法向。cloud为原始点云模型，normal为法向信息，1表示需要显示法向的点云间隔，即每1个点显示一次法向，0.01表示法向长度。
	viewer.addPointCloudNormals(cloud, normals, 1, 0.01, "normals");

	while (!viewer.wasStopped())
	{
		viewer.spinOnce();
	}

}

法向量估算渲染图：

附快捷键：