Lecture 16: Stereo
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Transcript of Lecture 16: Stereo
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Lecture 16: Stereo
CS4670 / 5670: Computer VisionNoah Snavely
Single image stereogram, by Niklas Een
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Readings• Szeliski, Chapter 10 (through 10.5)
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Public Library, Stereoscopic Looking Room, Chicago, by Phillips, 1923
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Mark Twain at Pool Table", no date, UCR Museum of Photography
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Stereo
• Given two images from different viewpoints– How can we compute the depth of each point in the image?– Based on how much each pixel moves between the two images
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epipolar lines
Epipolar geometry
(x1, y1)(x1, y1) (x2, y1)(x2, y1)
x2 -x1 = the disparity of pixel (x1, y1)
Two images captured by a purely horizontal translating camera(rectified stereo pair)
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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
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Window size
• Smaller window+ –
• Larger window+ –
W = 3 W = 20
Better results with adaptive window• T. Kanade and M. Okutomi, A Stereo Matching
Algorithm with an Adaptive Window: Theory and Experiment,, Proc. International Conference on Robotics and Automation, 1991.
• D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155-174, July 1998
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
For the latest and greatest: http://www.middlebury.edu/stereo/
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Stereo as energy minimization
What defines a good stereo correspondence?1. Match quality
– Want each pixel to find a good match in the other image
2. Smoothness– If two pixels are adjacent, they should (usually) move about
the same amount
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Stereo as energy minimization
• Find disparity map d that minimizes an energy function
• Simple pixel / window matching
SSD distance between windows I(x, y) and J(x + d(x,y), y)
=
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Stereo as energy minimization
I(x, y) J(x, y)
y = 141
C(x, y, d); the disparity space image (DSI)x
d
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Stereo as energy minimization
y = 141
x
d
Simple pixel / window matching: choose the minimum of each column in the DSI independently:
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Stereo as energy minimizationBetter objective function
{ {
match cost smoothness cost
Want each pixel to find a good match in the other image
Adjacent pixels should (usually) move about the
same amount
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Stereo as energy minimization
match cost:
smoothness cost:
4-connected neighborhood
8-connected neighborhood
: set of neighboring pixels
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Smoothness cost
“Potts model”
L1 distance
How do we choose V?
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Dynamic programming
Can minimize this independently per scanline using dynamic programming (DP)
: minimum cost of solution such that d(x,y) = d
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Dynamic programming
Finds “smooth” path through DPI from left to right
y = 141
x
d
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Dynamic Programming
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Dynamic programming
Can we apply this trick in 2D as well?
No: dx,y-1 and dx-1,y may depend on different values of dx-1,y-1
Slide credit: D. Huttenlocher
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Stereo as a minimization problem
The 2D problem has many local minima• Gradient descent doesn’t work well
And a large search space• n x m image w/ k disparities has knm possible solutions• Finding the global minimum is NP-hard in general
Good approximations exist… we’ll see this soon
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Questions?
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Depth from disparity
f
x x’
baseline
z
C C’
X
f
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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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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.