試驗了好久了，終於成功了！用OpenNI獲取彩色和深度資料流，轉化成OpenCV的Mat影象格式。

對相機進行標定，獲取相機的內部引數：

Calibration results after optimization (with uncertainties):   //優化後的引數值

Focal Length:          fc = [ 510.99171   513.71815 ] ? [ 1.99569   2.13975 ]    //焦距
Principal point:       cc = [ 324.12889   236.29232 ] ? [ 3.66214   3.41361 ]    //關鍵點
Skew:             alpha_c = [ 0.00000 ] ? [ 0.00000  ]   => angle of pixel axes = 90.00000 ? 0.00000 degrees
Distortion:            kc = [ 0.06196   -0.25035   -0.00173   0.00098  0.00000 ] ? [ 0.02152   0.08623   0.00242   0.00263  0.00000 ]
Pixel error:          err = [ 0.33426   0.40108 ]

設（u， v）是畫素座標系的座標，（Xw， Yw，Zw）是世界座標系的座標。

則標定後可以通過（u， v）求出（Xw， Yw），公式如下：

Xw = (u - u0) * Zw / fx;

Yw = (v - v0) *Zw / fy;

其中（u0，v0）是光軸與畫素平面的交點座標。

效果圖如下：

程式碼如下：

<span style="font-size:18px;">#include <pcl/visualization/cloud_viewer.h>
#include <iostream>
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <opencv2/opencv.hpp>

int user_data;
const double u0 = 319.52883;
const double v0 = 271.61749;
const double fx = 528.57523;
const double fy = 527.57387;

void viewerOneOff (pcl::visualization::PCLVisualizer& viewer)
{
    viewer.setBackgroundColor (0.0, 0.0, 0.0);
}
    
int main ()
{
	pcl::PointCloud<pcl::PointXYZRGB> cloud_a;
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);


	cv::Mat color = cv::imread("Image1.jpg");
	cv::Mat depth = cv::imread("cc.jpg");

	int rowNumber = color.rows;
	int colNumber = color.cols;

	cloud_a.height  = rowNumber;
	cloud_a.width = colNumber;
	cloud_a.points.resize(cloud_a.width * cloud_a.height);

	for (unsigned int u = 0; u < rowNumber; ++u)
	{
		for (unsigned int v = 0; v < colNumber; ++v)
		{
			unsigned int num = u*colNumber + v;
			double Xw = 0, Yw = 0, Zw = 0;

			Zw = ((double)depth.at<uchar>(u,v)) / 255.0 * 10001.0;
			Xw = (u-u0) * Zw / fx;
			Yw = (v-v0) * Zw / fy;

			cloud_a.points[num].b = color.at<cv::Vec3b>(u,v)[0];
			cloud_a.points[num].g = color.at<cv::Vec3b>(u,v)[1];
			cloud_a.points[num].r = color.at<cv::Vec3b>(u,v)[2];

			cloud_a.points[num].x = Xw;
			cloud_a.points[num].y = Yw;
			cloud_a.points[num].z = Zw;
		}
	}

	*cloud = cloud_a;

    pcl::visualization::CloudViewer viewer("Cloud Viewer");    

    viewer.showCloud(cloud);

    viewer.runOnVisualizationThreadOnce (viewerOneOff);

    while (!viewer.wasStopped ())
    {
		user_data = 9;
    }

    return 0;
}</span>