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Presentation transcript:

제 5 장 스테레오

Stereo Many slides adapted from Steve Seitz

Simplest Case: Parallel images Image planes of cameras are parallel to each other and to the baseline Camera centers are at same height Focal lengths are the same

Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image image 1 image 2 Dense depth map

Basic stereo matching algorithm For each pixel in the first image Find corresponding epipolar line in the right image Examine all pixels on the epipolar line and pick the best match Triangulate the matches to get depth information Simplest case: epipolar lines are scanlines When does this happen?

Correspondence search Left Right scanline Matching cost disparity Slide a window along the right scanline and compare contents of that window with the reference window in the left image Matching cost: SSD or normalized correlation

Active stereo with structured light Project “structured” light patterns onto the object Simplifies the correspondence problem Allows us to use only one camera camera projector L. Zhang, B. Curless, and S. M. Seitz. Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming. 3DPVT 2002

Active stereo with structured light L. Zhang, B. Curless, and S. M. Seitz. Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming. 3DPVT 2002

Active stereo with structured light http://en.wikipedia.org/wiki/Structured-light_3D_scanner

Kinect: Structured infrared light http://bbzippo.wordpress.com/2010/11/28/kinect-in-infrared/

Digital Michelangelo Project Laser scanning Digital Michelangelo Project Levoy et al. http://graphics.stanford.edu/projects/mich/ 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 Source: S. Seitz

The Digital Michelangelo Project, Levoy et al. Laser scanned models The Digital Michelangelo Project, Levoy et al. Source: S. Seitz

Multi-view stereo Many slides adapted from S. Seitz

Multiple-baseline stereo results M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo System,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4):353-363 (1993).

3D Display

Two Types of 3D Display

Stereoscopic

Stereoscopic

Stereoscopic

Stereoscopic

Autostereoscopic

Autostereoscopic

Autostereoscopic

Autostereoscopic

Holographic

Camera model

Camera model

Intermediate view reconstruction