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A checkerboard calibration target is a planar pattern of alternating dark and light squares with known geometry, used to estimate camera calibration parameters. Image measurements of internal corner intersections are paired with known target coordinates. Several tilted views constrain the image projection and distortion model.^[ref]

For a board with nine squares across and six down, the internal-corner grid has eight columns and five rows. If square spacing is 20 millimetres, one can assign corner coordinates $(20i,20j,0)$ millimetres for $i=0,\dots ,7$ and $j=0,\dots ,4$. Supplying only unit grid spacing instead yields translation estimates in square-size units. A useful experiment varies the target's orientation and image position, then examines reprojection error. Repeated identical views provide less geometric information; a warped board or inaccurate corner measurements can also bias the fitted camera model.

A reprojection error compares an observed image point with the position predicted by projecting a reconstructed scene point through a camera model. If $q_i$ is an observed position and ${\pi }_i(P)$ its prediction, the residual is $e_i=q_i-{\pi }_i(P)$. One possible aggregate is the per-point root mean square Euclidean norm:Coordinates in pixels give an error in pixels. Least squares instead minimizes the associated sum of squares; normalization conventions should be stated when reporting a summary.^[ref]

For an observation $(100,50)$ and prediction $(103,54)$, the Euclidean norm of the residual is 5 pixels. Camera calibration can adjust model parameters to reduce many such residuals, while stereoscopic triangulation can adjust a reconstructed point with the camera parameters fixed. Low reprojection error tests agreement with observations under that model; incorrect stereo correspondence or an incorrect camera baseline can still produce wrong scene geometry.