如何判断单应性矩阵是否可以接受？ [英] How can you tell if a homography matrix is acceptable or not?

问题描述

当使用OpenCV的 findHomography 函数估计两组点之间的单应性时，从不同的图像，你有时会得到一个坏的单应性由于离群值在你的输入点，即使你使用RANSAC或LMEDS。

  // opencv java示例：
 Mat H = Calib3d.findHomography ，dst_points，Calib3d.RANSAC，10）; 
  

如何判断产生的3x3单应性矩阵是否可以接受？

我在Stackoverflow和Google找到了一个答案，无法找到它。

我发现这篇文章，但对我来说有点神秘：

单应性的几何误差

解决方案

1-拍摄一张图片的点并使用计算的单应性重新投影。

  //对于一个3D点，这将是投影
 px'= H * px; 
 py'= H * py; 
 pz'= H * pz; 
  

2-计算欧氏距离。

重新投影错误。 p是投影点，q是真实点。

3-建立决定重投影错误是否可以接受的阈值。 b

例如，大于一个像素的错误对于许多跟踪应用程序是不可接受的。

When using OpenCV's findHomography function to estimate an homography between two sets of points, from different images, you will sometimes get a bad homography due to outliers within your input points, even if you use RANSAC or LMEDS.

// opencv java example:
Mat H = Calib3d.findHomography( src_points, dst_points, Calib3d.RANSAC, 10 );

How can you tell if the resulting 3x3 homography matrix is acceptable or not?

I have looked for an answer to this here in Stackoverflow and in Google and was unable to find it.

I found this article, but it is a bit cryptic to me:

"The geometric error for homographies"

解决方案

The best way to tell if the homography is acceptable is.

1- Take the points of one image and reproject them using the computed homography.

//for one 3D point, this would be the projection
px' = H * px;
py' = H * py;
pz' = H * pz;

2- Calculate the euclidean distance between the reprojected points and the real points in the image.

Reprojection error for one point. p is the projected point and q is the real point.

3- Establish a treshold that decides if the reprojection error is acceptable.

For example, an error greater than one pixel wouldn't be acceptable for many tracking applications.

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