问题描述
当使用 OpenCV 的 findHomography 函数来估计来自不同图像的两组点之间的单应性时，由于输入点内的异常值，有时您会得到一个糟糕的单应性，即使您使用 RANSAC 或LMEDS.
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 );
如何判断生成的 3x3 单应矩阵是否可接受?
How can you tell if the resulting 3x3 homography matrix is acceptable or not?
我已在 Stackoverflow 和 Google 中寻找此问题的答案，但无法找到.
I have looked for an answer to this here in Stackoverflow and in Google and was unable to find it.
我找到了这篇文章，但对我来说有点神秘:
I found this article, but it is a bit cryptic to me:
推荐答案
判断单应性是否可接受的最好方法是.
The best way to tell if the homography is acceptable is.
1- 获取一张图像的点并使用计算的单应性重新投影它们.
//for one 3D point, this would be the projection
px' = H * px;
py' = H * py;
pz' = H * pz;
2- 计算重投影点与实际点之间的欧几里得距离图片.
2- Calculate the euclidean distance between the reprojected points and the real points in the image.
重投影错误一点.p是投影点，q是实点.
Reprojection error for one point. p is the projected point and q is the real point.
3- 建立一个阈值来决定重投影误差是否可以接受.
例如，对于许多跟踪应用程序来说，大于一像素的误差是不可接受的.
For example, an error greater than one pixel wouldn't be acceptable for many tracking applications.
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