在 OpenCV C++ 中估计基本矩阵之前对相应点进行归一化的正确方法是什么?

我正在尝试手动实现相应点的基本矩阵估计函数(基于两个图像之间的相似性(。经过ORB特征检测、提取、匹配和比率测试后得到相应的点。

关于这个主题的良好来源有很多文献。但是，它们似乎都没有给出一个好的伪代码来执行该过程。我浏览了多视图几何书的各个章节;以及许多在线资源。

这个来源似乎给出了一个进行规范化的公式，我遵循了这个来源第 13 页上提到的公式。

基于这个公式，我创建了以下算法。我不确定我是否以正确的方式做！

Normalization.hpp

class Normalization {
typedef std::vector <cv::Point2f> intercepts;
typedef std::vector<cv::Mat> matVec;
public:
Normalization () {}
~Normalization () {}
void makeAverage(intercepts pointsVec);
std::tuple <cv::Mat, cv::Mat> normalize(intercepts pointsVec);
matVec getNormalizedPoints(intercepts pointsVec);
private:
double xAvg = 0;
double yAvg = 0;
double count = 0;
matVec normalizedPts;
double distance = 0;
matVec matVecData;
cv::Mat forwardTransform;
cv::Mat reverseTransform;
};

规范化.cpp

#include "Normalization.hpp"
typedef std::vector <cv::Point2f> intercepts;
typedef std::vector<cv::Mat> matVec;
/*******
*@brief  : The makeAverage function receives the input 2D coordinates (x, y)
*          and creates the average of x and y
*@params : The input parameter is a set of all matches (x, y pairs) in image A
************/
void Normalization::makeAverage(intercepts pointsVec) {
count = pointsVec.size();
for (auto& member : pointsVec) {
xAvg = xAvg + member.x;
yAvg = yAvg + member.y;
}
xAvg = xAvg / count;
yAvg = yAvg / count;
}
/*******
*@brief  : The normalize function accesses the average distance calculated
*          in the previous step and calculates the forward and inverse transformation
*          matrices
*@params : The input to this function is a vector of corresponding points in given image
*@return : The returned data is a tuple of forward and inverse transformation matrices
*************/
std::tuple <cv::Mat, cv::Mat> Normalization::normalize(intercepts pointsVec) {
for (auto& member : pointsVec) {
//  Accumulate the distance for every point
distance += ((1 / (count * std::sqrt(2))) *
(std::sqrt(std::pow((member.x - xAvg), 2)
+ std::pow((member.y - yAvg), 2))));
}
forwardTransform = (cv::Mat_<double>(3, 3) << (1 / distance), 
0, -(xAvg / distance), 0, (1 / distance), 
-(yAvg / distance), 0, 0, 1);
reverseTransform = (cv::Mat_<double>(3, 3) << distance, 0, xAvg, 
0, distance, yAvg, 0, 0, 1);
return std::make_tuple(forwardTransform, reverseTransform);
}
/*******
*@brief  : The getNormalizedPoints function trannsforms the raw image coordinates into
*          transformed coordinates using the forwardTransform matrix estimated in previous step
*@params : The input to this function is a vector of corresponding points in given image
*@return : The returned data is vector of transformed coordinates
*************/
matVec Normalization::getNormalizedPoints(intercepts pointsVec) {
cv::Mat triplet;
for (auto& member : pointsVec) {
triplet = (cv::Mat_<double>(3, 1) << member.x, member.y, 1);
matVecData.emplace_back(forwardTransform * triplet);
}
return matVecData;
}

这是正确的方法吗？还有其他规范化方法吗？