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3D reconstruction of cameras and structure x i = PX i x’ i = P’X i
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Outline of Reconstruction method 1. Compute the fundamental matrix from point correspondences 2. Compute the camera matrices from the fundamental matrix 3. For each point correspondence x i x’ i, compute the point in space that projects to these 2 image points
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Computation of the fundamental matrix x’ i F x i = 0 With the x’ I and x i known, this equation is linear in the unknown entries of the matrix F. Thus 8 pairs of corresponding points is sufficient to solve for the entries of F up to scale. Usually, more than 8 point correspondences are used in a least square solution.
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Computation of the camera matrices
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Triangulation
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Reconstruction ambiguity (a)
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Reconstruction ambiguity (b)
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Fig 9.2 Reconstruction ambiguity
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Ambiguity for calibrated camera
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Projective ambiguity
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Projective reconstruction theorem
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Relationship between projective and Euclidean reconstructions
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Projective reconstruction
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Projective Reconstruction 2 views of a house Fig. 9.3 a
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Two views of a 3D projective reconstruction ( camera calibration matrices and scene geometry are not required) Fig 9.3b
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Stratified reconstruction
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The step to affine reconstruction
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The essence of affine reconstruction is to locate the plane at infinity
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Translation motion, Scene constraints
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Parallel lines, distance ratios on a line
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Projective reconstruction can be upgraded to affine using parallel scene lines
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Affine reconstruction
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Affine reconstruction 2
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Affine reconstruction 3
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The infinite homography
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Result 9.3
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One of the cameras is affine
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The step to metric reconstruction
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Proof
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Proof 2
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Constraints
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Constraints 2
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Constraints from the same cameras in all images
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Direct metric reconstruction uisng
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Metric Reconstruction Fig. 9.5
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Metric Reconstruction Texture mapped piecewise planar model
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Metric Reconstruction 2
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Direct Reconstruction Fig 9.6
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Direct Reconstruction
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Direct reconstruction Fig. 9.6
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Direct reconstruction 2
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Direct reconstruction 3
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Table 9.1
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