结合彩色图和深度图创建点云（OpenCV+OpenNI+PCL）
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花了相当长的时间进行各种尝试后终于取得了令人满意的结果！通过OpenNI实现了对彩色图像和深度数据流的捕获，并将其转换为OpenCV支持的Mat图像格式。
对相机进行标定，获取相机的内部参数：
Optimization outcomes post-calibration (considering measurement uncertainties) are presented below, along with their corresponding confidence intervals.
Focal Length: fc = [511, 514] ± [2, 2] //焦距
Principal Point: cc = [324, 236] ± [4, 3] //主点
Skewness: αc = [±...] → Pixel Axis Angle = (9±...)° //偏斜
Distortion Coefficients: kc = [..., ..., ..., ...] //畸变
Pixel Error Vector: er = [...] //像素误差向量
设（u， v）是像素坐标系的坐标，（Xw， Yw，Zw）是世界坐标系的坐标。
则标定后可以通过（u， v）求出（Xw， Yw），公式如下：
Xw = (u - u0) * Zw / fx;
Yw = (v - v0) *Zw / fy;
其中（u0，v0）是光轴与像素平面的交点坐标。
效果图如下：
代码如下：
<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;
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