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Camera Model & Camera Calibration
Slides are from Marc
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Pinhole camera model
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Pinhole camera model
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Principal point offset
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Principal point offset
calibration matrix
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Camera rotation and translation
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CCD camera
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Finite projective camera
11 dof (5+3+3) non-singular decompose P in K,R,C? {finite cameras}={P4x3 | det M≠0} If rank P=3, but rank M<3, then cam at infinity
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=( )-1= -1 -1 Camera matrix decomposition Finding the camera center
(use SVD to find null-space) Finding the camera orientation and internal parameters (use RQ decomposition ~QR) (if only QR, invert) Q R =( )-1=
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Cameras at infinity Camera center at infinity
Affine and non-affine cameras Definition: affine camera has P3T=(0,0,0,1)
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Affine cameras
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Camera calibration
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Resectioning
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Basic equations
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Basic equations n 6 points minimal solution
P has 11 dof, 2 independent eq./points 5½ correspondences needed (say 6) Over-determined solution n 6 points minimize subject to constraint
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Degenerate configurations
More complicate than 2D case Camera and points on a twisted cubic Points lie on plane or single line passing through projection center
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Data normalization Less obvious (i) Simple, as before
(ii) Anisotropic scaling
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Geometric error
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Gold Standard algorithm
Objective Given n≥6 2D to 2D point correspondences {Xi↔xi’}, determine the Maximum Likelyhood Estimation of P Algorithm Linear solution: Normalization: DLT: Minimization of geometric error: using the linear estimate as a starting point minimize the geometric error: Denormalization: ~
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Calibration example Canny edge detection
Straight line fitting to the detected edges Intersecting the lines to obtain the images corners typically precision <1/10 (HZ rule of thumb: 5n constraints for n unknowns
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Errors in the world Errors in the image and in the world
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Radial distortion short and long focal length
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Correction of distortion
Choice of the distortion function and center Computing the parameters of the distortion function Minimize with additional unknowns Straighten lines …
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