stereo guest lecture by li zhang
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Stereo
Guest Lecture by Li Zhanghttp://www.cs.washington.edu/homes/lizhang/
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Last lecture: new images from images
Stitching:
Compositing:
+ + · · · +
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This lecture: 3D structures from images
How might we do this automatically?• What cues in the image provide 3D information?
Readings• Trucco & Verri, Chapter 7
– Read through 7.1, 7.2.1, 7.2.2, 7.3.1, 7.3.2, 7.3.7 and 7.4, 7.4.1. The rest is optional.
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Shading
Visual cues
Merle Norman Cosmetics, Los Angeles
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Visual cuesShading
Texture
The Visual Cliff, by William Vandivert, 1960
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Visual cues
From The Art of Photography, Canon
Shading
Texture
Focus
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Visual cuesShading
Texture
Focus
Motion
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Visual cues
Others:• Highlights• Shadows• Silhouettes• Inter-reflections• Symmetry• Light Polarization• ...
Shading
Texture
Focus
Motion
Shape From X• X = shading, texture, focus, motion, ...• In this class we’ll focus on stereo: motion between two images
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Public Library, Stereoscopic Looking Room, Chicago, by Phillips, 1923
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Teesta suspension bridge-Darjeeling, India
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Woman getting eye exam during immigration procedure at Ellis
Island, c. 1905 - 1920 , UCR Museum of Phography
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Mark Twain at Pool Table", no date, UCR Museum of Photography
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Stereograms onlineUCR stereographs
• http://www.cmp.ucr.edu/site/exhibitions/stereo/The Art of Stereo Photography
• http://www.photostuff.co.uk/stereo.htmHistory of Stereo Photography
• http://www.rpi.edu/~ruiz/stereo_history/text/historystereog.htmlDouble Exposure
• http://home.centurytel.net/s3dcor/index.htmlStereo Photography
• http://www.shortcourses.com/book01/chapter09.htm3D Photography links
• http://www.studyweb.com/links/5243.htmlNational Stereoscopic Association
• http://204.248.144.203/3dLibrary/welcome.htmlBooks on Stereo Photography
• http://userwww.sfsu.edu/~hl/3d.biblio.html
A free pair of red-blue stereo glasses can be ordered from Rainbow Symphony Inc• http://www.rainbowsymphony.com/freestuff.html
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Stereo
scene pointscene point
optical centeroptical center
image planeimage plane
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Stereo
Basic Principle: Triangulation• Gives reconstruction as intersection of two rays
• Requires – calibration
– point correspondence
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Stereo correspondenceDetermine Pixel Correspondence
• Pairs of points that correspond to same scene point
Epipolar Constraint• Reduces correspondence problem to 1D search along conjugate
epipolar lines• Java demo: http://www.ai.sri.com/~luong/research/Meta3DViewer/EpipolarGeo.html
epipolar planeepipolar lineepipolar lineepipolar lineepipolar line
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Stereo image rectification
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Stereo image rectification
• reproject image planes onto a commonplane 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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Stereo matching algorithms
Match Pixels in Conjugate Epipolar Lines• Assume brightness constancy• This is a tough problem• Numerous approaches
– A good survey and evaluation: http://www.middlebury.edu/stereo/
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Your basic stereo algorithm
For each epipolar line
For each pixel in the left image• compare with every pixel on same epipolar line in right image
• pick pixel with minimum match cost
Improvement: match windows• This should look familar...
• Can use Lukas-Kanade or discrete search (latter more common)
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Window size
• Smaller window+ –
• Larger window+ –
W = 3 W = 20
Effect of window size
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Stereo results
Ground truthScene
• Data from University of Tsukuba• Similar results on other images without ground truth
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Results with window search
Window-based matching(best window size)
Ground truth
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Better methods exist...
State of the art methodBoykov et al., Fast Approximate Energy Minimization via Graph Cuts,
International Conference on Computer Vision, September 1999.
Ground truth
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Depth from disparity
f
x x’
baseline
z
C C’
X
f
input image (1 of 2) [Szeliski & Kang ‘95]
depth map 3D rendering
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Real-time stereo
Used for robot navigation (and other tasks)• Several software-based real-time stereo techniques have
been developed (most based on simple discrete search)
Nomad robot searches for meteorites in Antarticahttp://www.frc.ri.cmu.edu/projects/meteorobot/index.html
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• Camera calibration errors• Poor image resolution• Occlusions• Violations of brightness constancy (specular reflections)• Large motions• Low-contrast image regions
Stereo reconstruction pipelineSteps
• Calibrate cameras• Rectify images• Compute disparity• Estimate depth
What will cause errors?
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Active stereo with structured light
Project “structured” light patterns onto the object• simplifies the correspondence problem
camera 2
camera 1
projector
camera 1
projector
Li Zhang’s one-shot stereo
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Active stereo with structured light
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Laser scanning
Optical triangulation• Project a single stripe of laser light• Scan it across the surface of the object• This is a very precise version of structured light scanning
Digital Michelangelo Projecthttp://graphics.stanford.edu/projects/mich/
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Laser scanned models
The Digital Michelangelo Project, Levoy et al.
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Laser scanned models
The Digital Michelangelo Project, Levoy et al.
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Laser scanned models
The Digital Michelangelo Project, Levoy et al.
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Laser scanned models
The Digital Michelangelo Project, Levoy et al.
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Laser scanned models
The Digital Michelangelo Project, Levoy et al.
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Moving sceneswith Noah Snavely, Brian Curless, Steve Seitz
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video projectors
color cameras
black & white cameras
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time
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time
Face surface
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time
Face surface
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stereo
time
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stereo active stereo
time
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spacetime stereostereo active stereo
time
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time
time=1
Spacetime Stereo
Face surface
surface motion
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time
time=2
Spacetime Stereo
Face surface
surface motion
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time
time=3
Spacetime Stereo
Face surface
surface motion
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time
time=4
Spacetime Stereo
Face surface
surface motion
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time
time=5
Spacetime Stereo
Face surface
surface motion
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time
time
Spacetime Stereo
surface motion
Better • spatial resolution• temporal stableness
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Spacetime stereo matching
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Non-linear least square
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Spacetime stereo matching
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Demos
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