Calibration Best Practices

Accurate calibration is of key importance for performance in most machine and computer vision tasks. The following lists our best practices which we have found through extensive experimentation and theoretical considerations. 

  1. Choose the right size calibration target. Large enough to properly constrain parameters. Ideally it should cover at least half of the total area when seen fronto-parallel in the camera images. 
  2. Perform calibration at the approximate working distance (WD) of your final application. The camera should be focused at this distance and lens focus and aperture must not be changed during or after calibration.
  3. The target should have a high feature count. Using fine patterns is preferable. However, at some point detection robustness suffers. Our recommendation is to use fine pattern counts for cameras above 3MPx and if the lighting is controlled and good.
  4. Collect images from different areas and tilts. Move the target to fully cover the image area and aim for even coverage. Lens distortion can be properly determined from fronto-parallel images, but focal length and principle point  estimation is dependent on observing foreshortening. Include both frontoparallel images, and images taken with the board tilted up to +/- 45 degrees in both horizontal an vertical directions. Tilting more is usually not a good idea as feature localization accuracy suffers and can become biased.
  5. Use good lighting. This is often overlooked, but hugely important. The calibration target should preferably be diffusely lit by means of controlled photography lighting. Strong point sources give rise to uneven illumination, possibly making detection fail, and not utilizing the camera's dynamic range very well. Shadows can do the same.
  6. Have enough observations. Usually, calibration should be performed on at least 6 observations (images) of a calibration target. If a higher order camera or distortion model is used, more observations are beneficial.
  7. Consider using uniquely coded targets such as CharuCo boards. These allow you to gather observations from the very edges of the camera sensor and lens, and hence constrain the distortion parameters very well. Also, they allow you to collect data even when some of the feature points do not fulfil the other requirements.
  8. Calibration is only as accurate as the calibration target used. Use laser or inkjet printed targets only to validate and test.
  9. Proper mounting of calibration target and camera. In order to minimize distortion and bow in larger targets, mount them either vertically, or laying flat on a rigid support. Consider moving the camera instead of the target in these cases instead. Use a quality tripod, and avoid touching the camera during acquisitions. 
  10. Remove bad observations. Carefully inspect reprojection errors. Both per-view and per-feature. If any of these appear as outliers, exclude them and recalibrate. 
  11. Obtaining a low reproduction error does not equal a good camera calibration, but merely indicates that the provided data/evidence can be described with the used model. This could be due to overfitting. Parameter uncertainties are indications of how well the chosen camera model was constrained.
  12. Analyse the individual reprojection errors. Their direction and magnitude should not correlate with position, i.e. they should point chaotically in all directions.'s Camera Calibrator software provides powerfull visualizations to investigate the reprojected errors.

Following these practices should ensure the most accurate and precise calibration possible. 


Have any questions, comments or additional insights? Post them below.


  • @Ernest: This depends on the detection and sub pixel algorithms you are using and also on the contrast and image noise present. It usually needs testing. We would generally not recommend having squares smaller than 20×20px in the image. If detections look ok, bad numbers you are seeing are probably caused by other issues/bugs.
  • Hi, Your site is all very helpful. I am using your square based calibration target. Is there a minimum recommended resolution for each square in the image? For example, at my working distance, each square in the target is about 20×20 pixels on the ‘full frontal’ 640×512 image, but of course as I tilt the board for other views the squares appear smaller. OpenCV routine is returning bad numbers for focal length causing ‘undistorted()’ image to appear as if they were taken by a fisheye lens when in fact I am using 12mm lens with low distortion. Should I be using a target with larger (but fewer) squares? Thanks!

    Ernest Merrill
  • @Toby: the offset could very well be due to diffraction in the glass. With the glass exactly perpendicular to the optical axis, you would (ideally) expect no shift of the principal point but a slight change of focal length.
  • Thank you for the excelent recommendations for calibration.

    I have calibrated a camera with and without a cover glass and found the center offset to be 129 pixels with the cover glass! (3096×2080 camera) could this indicate an incorrect camera calibration or could this be due to the cover glass?

    Toby Hijzen
  • @Eugene, the short answer would be no, you do not need to cover different distances. In fact, it is usually advisable to have the calibration target at the exact focus distance (albeit tilted to observe foreshortening), or at least at the hyperfocal distance. Moving further away would constraint a smaller image area which would not help.
    Non-central general camera models would be the exception, but these are usually not called for. If you suspect the camera to exhibit distance-dependent projection, moving to different distance could help average the parameters in a standard model such that they work well under all conditions which your application calls for.

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