lec13_mvs - CS6670: Computer Vision Noah Snavely Lecture...

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Lecture 12: Structure from motion CS6670: Computer Vision Noah Snavely
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Lecture 13: Multi-view stereo CS6670: Computer Vision Noah Snavely
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Announcements Project 2 voting open later today Final project page will be released after class Project 3 out soon Quiz 2 on Thursday, beginning of class
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Readings Szeliski, Chapter 11.6
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Fundamental matrix calibrated case 0 { the Essential matrix
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Fundamental matrix uncalibrated case 0 the Fundamental matrix
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Properties of the Fundamental Matrix is the epipolar line associated with is the epipolar line associated with and is rank 2 How many parameters does F have? 7 T
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How many parameters? Matrix has 9 entries -1 due to scale invariance -1 due to rank 2 constraint 7 parameters in total 8
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Rectified case
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Rectified case if
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Stereo image rectification reproject image planes onto a common plane parallel to the line between optical centers pixel motion is horizontal after this transformation two homographies (3x3 transform), one for each input image reprojection C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision . IEEE Conf. Computer Vision and Pattern Recognition, 1999 .
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Rectifying homographies Idea: compute two homographies and such that
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Rectifying homographies
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Estimating F If we don’t know K 1 , K 2 , R , or t , can we estimate F ? Yes, given enough correspondences
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