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Hello,

I am just trying out opencv, specifically cameraCalibrate, and I am attempting to get a baseline for my expectations when using this to calibrate real cameras. S I have generated images using 3d software, and rendered posed checkerboards such as the one below. I have the the checkerboard at the origin, with the centre matching that of OpenCV (This seems to be 1 row in from the top and left that OpenCV considers the checkerboard centre to be). Everything is scaled correctly in terms of the object points I am supply to cameraCalibrate.

Now I know the cameras has no distortion, so I am using the following flags:

fl = cv2.CALIB_FIX_K1 | cv2.CALIB_FIX_K2 | cv2.CALIB_FIX_K3 | cv2.CALIB_FIX_K4 | cv2.CALIB_FIX_K5 | cv2.CALIB_ZERO_TANGENT_DIST | cv2.CALIB_FIX_PRINCIPAL_POINT

I am fixing the principle point to be at image.w/2 image.h/2.

I know the virtual camera in my software is translated to 10.000 -4.000 20.000. My software has y up, z towards the camera so these translate to 10.000, 4.000, -20.000 in terms of the opencv coordinates with cameraCalibrate.

I have played alot and found that large resolutions (upwards of 2,500 pixels wide) are giving a much better result for tvecs. I am calcuating the translation of my camera from tvecs with the following

mat =  cv2.Rodrigues(rvecs[0])[0]
mat = mat.T
camTranslation = np.dot(-mat, tvecs[0])

which is giving this result,

Translation 9.53675668, 3.86981952 , -19.19919903

this is close, but still relatively far off the 10.000, 4.000, -20.000 I was expecting.

cameraCalibrate returns a reprojection error of 1.265... on a 4096 pixel wide image. (I assume the reproj error is in terms of pixels, so this doesn't seem to bad)

I am basically wondering why am I not getting something far closer to that actual translation of this setup. I was expecting to see perhaps 0.1 off, ideally less, but this is considerably more for an image that is of far higher quality than something I will have in practice with a real camera.

Side note:

I noticed an the opencv googled image of a checkerboard that I am using (I think its an offical one) has imperfect corners on the checkerboard.

is this by design ?

I'd be very interested to hear any advice on the points above from someone who has experience with cameraCalibration, and some guidance as to what I should realistically expect in terms of getting accurate extrinsic matricies of the camera. Also any advice on things to try to improve results.

I assume with many cameras a bundle adjustment step would improve on this? but should I be able to achieve a more accurate extrinsic matrix from this stage?

Thanks in advance!

Hello,

I am just trying out opencv, specifically cameraCalibrate, calibrateCamera, and I am attempting to get a baseline for my expectations when using this to calibrate real cameras. S I have generated images using 3d software, and rendered posed checkerboards such as the one below. I have the the checkerboard at the origin, with the centre matching that of OpenCV (This seems to be 1 row in from the top and left that OpenCV considers the checkerboard centre to be). Everything is scaled correctly in terms of the object points I am supply to cameraCalibrate.calibrateCamera.

Now I know the cameras has no distortion, so I am using the following flags:

fl = cv2.CALIB_FIX_K1 | cv2.CALIB_FIX_K2 | cv2.CALIB_FIX_K3 | cv2.CALIB_FIX_K4 | cv2.CALIB_FIX_K5 | cv2.CALIB_ZERO_TANGENT_DIST | cv2.CALIB_FIX_PRINCIPAL_POINT

I am fixing the principle point to be at image.w/2 image.h/2.

I know the virtual camera in my software is translated to 10.000 -4.000 20.000. My software has y up, z towards the camera so these translate to 10.000, 4.000, -20.000 in terms of the opencv coordinates with cameraCalibrate.calibrateCamera.

I have played alot and found that large resolutions (upwards of 2,500 pixels wide) are giving a much better result for tvecs. I am calcuating the translation of my camera from tvecs with the following

mat =  cv2.Rodrigues(rvecs[0])[0]
mat = mat.T
camTranslation = np.dot(-mat, tvecs[0])

which is giving this result,

Translation 9.53675668, 3.86981952 , -19.19919903

this is close, but still relatively far off the 10.000, 4.000, -20.000 I was expecting.

cameraCalibrate calibrateCamera returns a reprojection error of 1.265... on a 4096 pixel wide image. (I assume the reproj error is in terms of pixels, so this doesn't seem to bad)

I am basically wondering why am I not getting something far closer to that actual translation of this setup. I was expecting to see perhaps 0.1 off, ideally less, but this is considerably more for an image that is of far higher quality than something I will have in practice with a real camera.

Side note:

I noticed an the opencv googled image of a checkerboard that I am using (I think its an offical one) has imperfect corners on the checkerboard.

is this by design ?

I'd be very interested to hear any advice on the points above from someone who has experience with cameraCalibration, and some guidance as to what I should realistically expect in terms of getting accurate extrinsic matricies of the camera. Also any advice on things to try to improve results.

I assume with many cameras a bundle adjustment step would improve on this? but should I be able to achieve a more accurate extrinsic matrix from this stage?

Thanks in advance!