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Midterms graded (handed back at end of lecture) |
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Handout (Chap 7, Trucco & Verri) |
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Questions on project? |
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http://www.dartfish.com/technologies/technologies_stromotion.html |
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So far, we’ve relied on a human to provide depth
cues |
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parallel lines, reference points, etc. |
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How might we do this automatically? |
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What cues in the image provide 3D information? |
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Shading |
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Texture |
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Focus |
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Motion |
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Shading |
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Texture |
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Focus |
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Motion |
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Readings |
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Trucco & Verri, Chapter 7 (handout) |
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Read through 7.3.2, also 7.3.7 and 7.4,
7.4.1. The rest is optional. |
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UCR stereographs |
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http://www.cmp.ucr.edu/site/exhibitions/stereo/ |
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The Art of Stereo Photography |
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http://www.photostuff.co.uk/stereo.htm |
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History of Stereo Photography |
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http://www.rpi.edu/~ruiz/stereo_history/text/historystereog.html |
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Double Exposure |
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http://home.centurytel.net/s3dcor/index.html |
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Stereo Photography |
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http://www.shortcourses.com/book01/chapter09.htm |
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3D Photography links |
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http://www.studyweb.com/links/5243.html |
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National Stereoscopic Association |
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http://204.248.144.203/3dLibrary/welcome.html |
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Books on Stereo Photography |
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http://userwww.sfsu.edu/~hl/3d.biblio.html |
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Determine Pixel Correspondence |
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Pairs of points that correspond to same scene
point |
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Features vs. Pixels? |
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Do we extract features prior to matching? |
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Image Reprojection |
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reproject image planes onto common
plane parallel to line between optical centers |
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a homography (3x3 transform)
applied to both input images |
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pixel motion is horizontal after this
transformation |
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C. Loop and Z. Zhang. Computing Rectifying
Homographies for Stereo Vision. IEEE Conf. Computer Vision and Pattern
Recognition, 1999. |
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Match Pixels in Conjugate Epipolar Lines |
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Assume brightness constancy |
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This is a tough problem |
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Numerous approaches |
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dynamic programming [Baker 81,Ohta 85] |
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smoothness functionals |
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more images (trinocular, N-ocular) [Okutomi 93] |
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graph cuts [Boykov 00] |
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Smaller window |
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more details |
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more noise |
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Larger window |
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less noise |
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less detail |
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Data from University of Tsukuba |
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Similar results on other images without ground
truth |
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Render new views from raw disparity |
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S. M. Seitz and C. R. Dyer, View Morphing, Proc.
SIGGRAPH 96, 1996, pp. 21-30. |
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L. McMillan and G. Bishop. Plenoptic Modeling:
An Image-Based Rendering System, Proc. of SIGGRAPH 95, 1995, pp. 39-46. |
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Steps |
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Calibrate cameras |
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Rectify images |
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Compute disparity |
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Estimate depth |
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Project “structured” light patterns onto the
object |
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simplifies the correspondence problem |
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Optical triangulation |
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Project a single stripe of laser light |
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Scan it across the surface of the object |
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This is a very precise version of structured
light scanning |
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Used for robot navigation (and other tasks) |
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Several software-based real-time stereo
techniques have been developed (most based on simple discrete search) |
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Things to take away from this lecture |
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Cues for 3D inference, shape from X |
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Epipolar geometry |
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Stereo image rectification |
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Stereo matching |
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window-based epipolar search |
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effect of window size |
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sources of error |
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Active stereo |
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structured light |
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laser scanning |
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