# Revision history [back]

### What is a good result from stereo_calib?

I downloaded and built OpenCV 4.1.1 (using install-opencv.sh) then tried using stereo_calib on my camera, but the results are consistently poor; I get an RMS error of 0.6. The blog post Building OpenCV Stereo Vision - Calibration says:

When calibration finished, it outputs and RMS error. I got an RMS of 0.3 which is pretty good for VGA cameras, but it could be better. If RMS is anything above 0.5, I'd advise that you repeat the process.

In order to demonstrate a good example I used the sample data provided in samples/data - stereo_calib.xml and the images it lists. The result from stereo_calib is:

..........................13 pairs have been successfully detected.
Running stereo calibration ...
done with RMS error=0.635856
average epipolar err = 0.443478


This seems to be about the same as I get from my data. Is this a reasonable result, even though the RMS error is above 0.5? What about the average epipolar err?

I used the results to run stereo_match on the first example image:

stereo_match -i=intrinsics.yml -e=extrinsics.yml -o=disparity.png -p=point_cloud.txt --max-disparity=16 --blocksize=15 left01.jpg right01.jpg


Viewing point_cloud.txt in CloudCompare does seem to show a flat object (the chessboard), albeit poorly. Is this working correctly? Or is it a bad example?