Camera Calibration MCQ
Pinhole intrinsics, lens distortion, planar targets, and why accurate K matters for 3D and AR.
Intrinsics
K matrix
Extrinsics
R, t
Distortion
Radial / tangential
Targets
Checkerboard
Why calibrate cameras?
Calibration estimates intrinsics (focal length, principal point, skew) and often radial/tangential distortion so that projected rays match real lenses. Planar checkerboard targets (Zhang's method) are standard: each view gives homography constraints that solve for K and distortion, then extrinsics per pose.
Reprojection error
After calibration, compare detected image points with projections of 3D model points; RMS reprojection error should be small (sub-pixel for good setups).
Key ideas
Intrinsic K
Maps normalized camera coordinates to pixel coordinates; includes fx, fy, cx, cy.
Distortion
Brown–Conrady model: k1, k2 radial; p1, p2 tangential before projection.
Zhang's method
Multiple views of a planar pattern; closed-form init then non-linear refinement.
Extrinsics
Per-image R, t from world (target) frame to camera frame.
Calibration pipeline
Capture images → detect corners → estimate K, distortion, poses → optimize jointly → validate error