Circle pattern or chessboard for calibration?

edit

Hello guys!

I've been reading a paper, Which Pattern? Biasing Aspects of Planar Calibration Patterns and Detection Methods, it says "two popular checkerboard and circular dot patterns are each examined with two detection strategies for invariance to the potential bias from projective transformations and nonlinear distortions. It is theoretically and experimentally shown that circular patterns can potentially be affected by both biasing sources. Guidelines are given to control such bias. In contrast, appropriate chessboard detection is shown to be bias free." which indicates that from this aspect chessboard is better than circular dot while both are used to calibrate the camera.

But, in another book, Learning OpenCV 3 By Gary Bradski, Adrian Kaehler, it is explicitly mentioned that circular dots are preferred these days.

Thus, these two contradictory statements make me kind of confused. Known that the paper mentioned above has been published a decade ago, if its conclusion has been widely accepted, did OpenCV function "findChessboardCorners()" and "findCirclesGrid()" solved the bias problems it proposed? Or it is just because I misunderstood one, or potentially the two statements?

Thanks for answering my question!