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Eventually, the solution was analogous to the one provided in #67855 and it boiled down to removing some of the calibration images to complete the calibration procedure. Apologies for asking a duplicate question!

I had 25 calibration images and it took me some trial and error to single out the culprits: apparently 4 out of 25 images were bad stereo pairs. Visually, they appeared equivalently good to the others that were accepted. So, even though the practical question is answered, I still don't understand why those 4 stereo images were bad and the other 21 were good.

Hoping that any of the fisheye module developer might read this, I'd like to observe that it would be very helpful if:

1. cv::fisheye::stereoCalibrate() offered some clues as to which or how many stereo pairs are found to be "bad", to streamline the identification and removal of bad calibration images; and/or
2. its documentation clarified the criterion used to check goodness of a stereo pair (which unfortunately I couldn't understand from looking at the code), so users might possibly capture better calibration images in the first place.