lec22_compphoto

# lec22_compphoto - CS6670: Computer Vision Noah Snavely...

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Lecture 22: Computational photography CS6670: Computer Vision Noah Snavely photomatix.com

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Announcements Final project midterm reports due on Tuesday to CMS by 11:59pm
BRDF’s can be incredibly complicated…

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Shape from shading Suppose You can directly measure angle between normal and light source Not quite enough information to compute surface shape But can be if you add some additional info, for example – assume a few of the normals are known (e.g., along silhouette) – constraints on neighboring normals—―integrability‖ – smoothness Hard to get it to work well in practice – plus, how many real objects have constant albedo?
Photometric stereo N L 1 L 2 V L 3 Can write this as a matrix equation:

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Solving the equations
More than three lights Get better results by using more lights What’s the size of L T L ? Least squares solution: Solve for N, k d as before

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Example Recovered albedo Recovered normal field Forsyth & Ponce, Sec. 5.4
Computing light source directions Trick: place a chrome sphere in the scene the location of the highlight tells you where the light source is

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Depth from normals What we have What we want
Depth from normals Get a similar equation for V 2 Each normal gives us two linear constraints on z compute z values by solving a matrix equation V 1 V 2 N

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Example
Limitations Big problems doesn’t work for shiny things, semi-translucent things shadows, inter-reflections Smaller problems camera and lights have to be distant calibration requirements – measure light source directions, intensities – camera response function

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## This note was uploaded on 09/27/2010 for the course CS 667 at Cornell University (Engineering School).

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lec22_compphoto - CS6670: Computer Vision Noah Snavely...

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