If you got extrinsic parameters then you got everything. That means that you can have Homography from the extrinsics (also called CameraPose). Pose is a 3×4 matrix, homography is a 3×3 matrix, H defined as H = K*[r1, r2, t], //eqn 8.1, Hartley and Zisserman with K being the camera intrinsic matrix, r1 and r2 … Read more
I would take random samples of the 30 input points and compute the homography in each case along with the errors under the estimated homographies, a RANSAC scheme, and verify consensus between error levels and homography parameters, this can be just a verification of the global optimisation process. I know that might seem unnecessary, but … Read more
In short: yes. In order to make a mathematical model that can describe a camera with rectangular pixels, you have to introduce two separate focal lengths. I’ll quote from the often recommended “Learning OpenCV” (p. 373) which covers that section pretty well and which I recommend getting if you would like more background on this: … Read more
Hmm, are you looking for “handsome” or “accurate”? Camera calibration is one of the very few subjects in computer vision where accuracy can be directly quantified in physical terms, and verified by a physical experiment. And the usual lesson is that (a) your numbers are just as good as the effort (and money) you put … Read more
Given your configuration, errors of 20-40mm at the edges are average. It looks like you’ve done everything well. Without modifying camera/system configuration, doing better will be hard. You can try to redo camera calibration and hope for better results, but this will not improve them alot (and you may eventually get worse results, so don’t … Read more