1.opencv中3D点根据相机参数投影成2D点，直接上代码：

输入：3D坐标+旋转，平移矩阵（所谓的相机姿态）+相机内参（包括畸变矩阵）

输出：2D坐标

（1.投影函数：根据相机参数（包括畸变矩阵），把3D点投影成2D点

2.


搞清楚R和t的具体含义。

R的第i行 表示摄像机坐标系中的第i个坐标轴方向的单位向量在世界坐标系里的坐标；

R的第i列 表示世界坐标系中的第i个坐标轴方向的单位向量在摄像机坐标系里的坐标；

t 表示世界坐标系的原点在摄像机坐标系的坐标；


-R的转置 * t 表示摄像机坐标系的原点在世界坐标系的坐标

。

）

#include “opencv2/core/core.hpp”

#include “opencv2/imgproc/imgproc.hpp”

#include “opencv2/calib3d/calib3d.hpp”

#include “opencv2/highgui/highgui.hpp”

#include <iostream>

#include <string>

std::vector<cv::Point3d> Generate3DPoints();

int main(int argc, char* argv[])

{




// Read 3D points



std::vector<cv::Point3d> objectPoints = Generate3DPoints();



std::vector<cv::Point2d> imagePoints;



cv::Mat intrisicMat(3, 3, cv::DataType<double>::type); // Intrisic matrix



intrisicMat.at<double>(0, 0) = 1.6415318549788924e+003;



intrisicMat.at<double>(1, 0) = 0;



intrisicMat.at<double>(2, 0) = 0;



intrisicMat.at<double>(0, 1) = 0;



intrisicMat.at<double>(1, 1) = 1.7067753507885654e+003;



intrisicMat.at<double>(2, 1) = 0;



intrisicMat.at<double>(0, 2) = 5.3262822453148601e+002;



intrisicMat.at<double>(1, 2) = 3.8095355839052968e+002;



intrisicMat.at<double>(2, 2) = 1;



cv::Mat rVec(3, 1, cv::DataType<double>::type); // Rotation vector



rVec.at<double>(0) = -3.9277902400761393e-002;



rVec.at<double>(1) = 3.7803824407602084e-002;



rVec.at<double>(2) = 2.6445674487856268e-002;



cv::Mat tVec(3, 1, cv::DataType<double>::type); // Translation vector



tVec.at<double>(0) = 2.1158489381208221e+000;



tVec.at<double>(1) = -7.6847683212704716e+000;



tVec.at<double>(2) = 2.6169795190294256e+001;



cv::Mat distCoeffs(5, 1, cv::DataType<double>::type);   // Distortion vector



distCoeffs.at<double>(0) = -7.9134632415085826e-001;



distCoeffs.at<double>(1) = 1.5623584435644169e+000;



distCoeffs.at<double>(2) = -3.3916502741726508e-002;



distCoeffs.at<double>(3) = -1.3921577146136694e-002;



distCoeffs.at<double>(4) = 1.1430734623697941e+002;



std::cout << “Intrisic matrix: ” << intrisicMat << std::endl << std::endl;



std::cout << “Rotation vector: ” << rVec << std::endl << std::endl;



std::cout << “Translation vector: ” << tVec << std::endl << std::endl;



std::cout << “Distortion coef: ” << distCoeffs << std::endl << std::endl;



cv::projectPoints(objectPoints, rVec, tVec, intrisicMat, distCoeffs, imagePoints);



std::cout << “Press any key to exit.”;



std::cin.ignore();



std::cin.get();



return 0;

}

std::vector<cv::Point3d> Generate3DPoints()

{




std::vector<cv::Point3d> points;



double x, y, z;



x = .5; y = .5; z = -.5;



points.push_back(cv::Point3d(x, y, z));



x = .5; y = .5; z = .5;



points.push_back(cv::Point3d(x, y, z));



x = -.5; y = .5; z = .5;



points.push_back(cv::Point3d(x, y, z));



x = -.5; y = .5; z = -.5;



points.push_back(cv::Point3d(x, y, z));



x = .5; y = -.5; z = -.5;



points.push_back(cv::Point3d(x, y, z));



x = -.5; y = -.5; z = -.5;



points.push_back(cv::Point3d(x, y, z));



x = -.5; y = -.5; z = .5;



points.push_back(cv::Point3d(x, y, z));



for (unsigned int i = 0; i < points.size(); ++i)



{




std::cout << points[i] << std::endl << std::endl;



}



return points;

}

2.opencv中

solvePnP函数计算相机姿态：

http://blog.csdn.net/aptx704610875/article/details/48915149

或者http://www.cnblogs.com/gaoxiang12/p/4659805.html

输入：3D坐标，

2D坐标

+相机内参（包括畸变矩阵）

输出：

相机姿态（旋转平移矩阵）

3.

超级好的一个博客！：http://www.cnblogs.com/gaoxiang12/p/4652478.html（这个博客还包括根据RGB-D图像，点云拼接（三维重建），很详细！）

（涉及到了三维点云，除了opencv,还用到PCL）

原理

+代码实现从（2D->3D）

输入：2D坐标，相机内参（包括畸变矩阵），

相机姿态

输出：

3D坐标

（1,2,3其实就是3D，2D坐标，相机姿态（在已经知道相机参数情况下），知道其中两个可以求得另外一个！）

4.用kinect1来拍摄三维模型，很重要的一步就是先要标定相机（因为深度相机与彩色相机不是一个位置，存在偏差）：

Kinect1深度图与摄像头RGB的标定与配


准

：

http://blog.csdn.net/aichipmunk/article/details/9264703