|
The corner detection is bad. Not sure why. I also think its gotten worse since 3.5.
I know the corner detection refinement is worse than the raw detections, so turn that shit off. Yeah thats a common problem if the calibration failed. It could also be that you are not cropping to what is in front of the camera, but if its really weird, its most likely the former. So the default, and probably most of the camera models in opencv requires a monotonic change lenses and bijective imaging. The former is common unless the lense has defects on the surface, and the latter is practically a physical constraint. The problem is that these constraints are difficult to add to the estimator, so they didnt. Meaning it will find a solution where they are not satisfied. If say the bijectiveness is not satisfied a bit outside the image, but stil valid accounting for infront and float accuracy, then that would absolutely account for the problem you describe. Is pretty obvious if you consider the function what the problem is, just hard to add the constraint in opencvs estimator. The solution is 1, verify the result is satisfied after estimation, 2 make sure you make the parameters are as observable as possible during calibration. This means spread out in the image, evenly distributed, and all the way to the edges. Also make verify it has not rotated one or more of the detected chessboards upside down, or 90 deg sideways. Finally, because it becomes harder and harder to avoid this problem with more parameters, always start with 1, then try 2, then the two variations of 3, and so on. More parameters always fit better, so use an appropriate test. |