Camera Parameters and Calibration
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1 Camera Parameters and Calibration
2 Camera parameters U V W Ê Ë ˆ = Transformation representing intrinsic parameters Ê Ë ˆ Ê Ë ˆ Transformation representing extrinsic parameters Ê Ë ˆ X Y Z T Ê Ë ˆ From last time.
3 Homogeneous Coordinates (Again)
4 Extrinsic Parameters: Characterizing Camera position Chasles's theorem: Any motion of a solid body can be composed of a translation and a rotation.
5 3D Rotation Matrices
6 3D Rotation Matrices cont d Euler s Theorem: An arbitrary rotation can be described by 3 rotation parameters For example: R = More Generally: Most General:
7 Rodrigue s Formula Take any rotation axis a and angle q: What is the matrix? with R = e Aq
8 Rotations can be represented as points in space (with care) Turn vector length into angle, direction into axis: Useful for generating random rotations, understanding angular errors, characterizing angular position, etc. Problem: not unique Not commutative
9 Other Properties of rotations NOT Commutative R*R2 R2*R
10 Rotations To form a rotation matrix, you can plug in the columns of new coordinate points For Example: The unit x-vector goes to x : È x'= Î x x 2 x 3 È x y z È = x 2 y 2 z 2 Î x 3 y 3 z 3 Î
11 Other Properties of Rotations Inverse: R - = R T rows, columns are othonormal r T i r j = if i j, else r T i r i = Determinant: det( R ) = The effect of a coordinate rotation on a function: x = R x F( x ) = F( R - x )
12 Extrinsic Parameters p = R p + t R = rotation matrix t = translation vector In Homogeneous coordinates, p = R p + t => È x' È r r 2 r 3 t x È x y' r = 2 r 22 r 23 ty y z' r 3 r 32 r 33 t z z Î Î Î
13 Intrinsic Parameters u * Su v * Sv È Î = Su Sv È Î u v È Î Differential Scaling u + u v + v È Î = u v È Î u v È Î Camera Origin Offset
14 Î È - Î È 2 2 Î È Rotation 9 o Scaling *2 Translation Î È y x p = House Points 2D Example
15 The Whole (Linear) Transformation U V W È Î = - f s u u - f s v v È Î È Î r r 2 r 3 t x r 2 r 22 r 23 t y r 3 r 32 r 33 t z È Î x y z T È Î U V W Ê Ë ˆ = Transformation representing intrinsic parameters Ê Ë ˆ Ê Ë ˆ Transformation representing extrinsic parameters Ê Ë ˆ X Y Z T Ê Ë ˆ u =U/W v =U/W Final image coordinates
16 Non-linear distortions (not handled by our treatment)
17 Camera Calibration You need to know something beyond what you get out of the camera Known World points (object) Traditional camera calibration Linear and non-linear parameter estimation Known camera motion Camera attached to robot Execute several different motions Multiple Cameras
18
19 Classical Calibration
20 Camera Calibration Take a known set of points. Typically 3 orthogonal planes. Treat a point in the object as the World origin Points x, x2, x3, Project to y,y2,y3
21 Calibration Patterns
22 Classical Calibration Put the set of points known object points x i into a matrix X = [ x L x ] n And projected points u i into a matrix U = [ u L u ] n Solve for the transformation matrix: Odd derivation of the Least Squares solution: U = P X UX t = P(XX t ) Note this is only for instructional purposes. It does not work as a real procedure. UX t (XX t ) - = P(XX t )(XX t ) - UX t (XX t ) - = P Next extract extrinsic and intrinsic parameters from P
23 Real Calibration Real calibration procedures look quite different Weight points by correspondence quality Nonlinear optimization Linearizations Non-linear distortions Etc.
24 Camera Motion
25 Calibration Example (Zhang, Microsoft) 8 Point matches manually picked Motion algorithm used to calibrate camera
26 Applications Image based rendering: Light field -- Hanrahan (Stanford)
27 Virtualized Reality
28 Projector-based VR UNC Chapel Hill
29 Shader Lamps
30 Shape recovery without calibration Fitzgibbons, Zisserman Fully automatic procedure: Converting the images to 3D models is a black-box filter: Video in, VRML out. * We don't require that the motion be regular: the angle between views can vary, and it doesn't have to be known. Recovery of the angle is automatic, and accuracy is about 4 millidegrees standard deviation. In golfing terms, that's an even chance of a hole in one. * We don't use any calibration targets: features on the objects themselves are used to determine where the camera is, relative to the turntable. Aside from being easier, this means that there is no problem with the setup changing between calibration and acquisition, and that anyone can use the software without special equipment. For example, this dinosaur sequence was supplied to us by the University of Hannover without any other information. (Actually, we do have the ground-truth angles so that we can make the accuracy claims above, but of course these are not used in the reconstruction).
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