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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.

OpenCV is a software library for computer vision and image processing. Its camera-geometry tools connect observed pixel coordinates to models of imaging, including camera calibration, correction of lens distortion, stereo rectification and three-dimensional reconstruction.^[ref]

A checkerboard calibration target supplies known geometry that can be observed from several camera positions. Fitting the model compares detected image points with projected target points. The resulting reprojection error measures agreement in the image, rather than independently confirming every physical dimension of the reconstructed scene.

A simple perspective projection illustrates the distinction: in $u=fX/Z$, replacing $X$ and $Z$ by $kX$ and $kZ$ for $k>0$ leaves $u$ unchanged. This scale ambiguity of perspective projection means that image agreement alone cannot verify a chosen unit of length. A camera baseline or a target of known size supplies additional metric information. Using the library well therefore includes checking geometric assumptions and units, alongside the numerical fit.