1	Announcements Midterm went out on Tuesday (due next Tuesday) Project 3 out today
2	Recovering 3D from images So far, we’ve relied on a human to provide depth cues parallel lines, reference points, etc. How might we do this automatically? What cues in the image provide 3D information?
3	Visual cues Shading
4	Visual cues Shading Texture
5	Visual cues Shading Texture Focus
6	Visual cues Shading Texture Focus Motion
7	Visual cues Shading Texture Focus Motion
8	Stereo Readings Trucco & Verri, Chapter 7 Read through 7.3.2, also 7.3.7 and 7.4, 7.4.1. The rest is optional.
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14	Stereograms online UCR stereographs http://www.cmp.ucr.edu/site/exhibitions/stereo/ The Art of Stereo Photography http://www.photostuff.co.uk/stereo.htm History of Stereo Photography http://www.rpi.edu/~ruiz/stereo_history/text/historystereog.html Double Exposure http://home.centurytel.net/s3dcor/index.html Stereo Photography http://www.shortcourses.com/book01/chapter09.htm 3D Photography links http://www.studyweb.com/links/5243.html National Stereoscopic Association http://204.248.144.203/3dLibrary/welcome.html Books on Stereo Photography http://userwww.sfsu.edu/~hl/3d.biblio.html
15	Stereo
16	Stereo
17	Stereo correspondence Determine Pixel Correspondence Pairs of points that correspond to same scene point
18	Stereo image rectification
19	Stereo image rectification Image Reprojection reproject image planes onto common plane parallel to line between optical centers a homography (3x3 transform) applied to both input images pixel motion is horizontal after this transformation C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
20	Stereo matching algorithms Match Pixels in Conjugate Epipolar Lines Assume brightness constancy This is a tough problem Numerous approaches dynamic programming [Baker 81,Ohta 85] smoothness functionals more images (trinocular, N-ocular) [Okutomi 93] graph cuts [Boykov 00] A good survey and evaluation: http://www.middlebury.edu/stereo/
21	Your basic stereo algorithm
22	Window size Smaller window Larger window
23	Stereo results Data from University of Tsukuba Similar results on other images without ground truth
24	Results with window correlation
25	Results with better method
26	Depth from disparity
27	Image-based rendering Render new views from raw disparity S. M. Seitz and C. R. Dyer, View Morphing, Proc. SIGGRAPH 96, 1996, pp. 21-30. L. McMillan and G. Bishop. Plenoptic Modeling: An Image-Based Rendering System, Proc. of SIGGRAPH 95, 1995, pp. 39-46.
28	Stereo reconstruction pipeline Steps Calibrate cameras Rectify images Compute disparity Estimate depth
29	Stereo matching Features vs. Pixels? Do we extract features prior to matching?
30	Active stereo with structured light Project “structured” light patterns onto the object simplifies the correspondence problem
31	Active stereo with structured light
32	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
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34	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)
35	Summary Things to take away from this lecture Cues for 3D inference, shape from X Epipolar geometry Stereo image rectification Stereo matching window-based epipolar search effect of window size sources of error Active stereo structured light laser scanning