PPT-Encoding Stereo Images Christopher Li,

Idoia Ochoa and Nima Soltani Outline System overview Detailed encoder description Demonstration Results Extensions Conclusions System Overview Encoder R L DWT Quant Arith. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Stereo and Multiple . View Geometry. Presented by. Radhakrishna Dasari . Contents. Stereo Practical Demo. Camera Intrinsic and Extrinsic parameters. Essential and Fundamental Matrix. Multiple View . Geometry. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. http://en.wikipedia.org/wiki/File:Jodhpur_tilt_shift.jpg. In close-up photo, the depth of field is limited.. Miniature faking. Miniature faking. http://en.wikipedia.org/wiki/File:Oregon_State_Beavers_Tilt-Shift_Miniature_Greg_Keene.jpg. A. dvances . in . Geometric . C. omputer . V. ision . & . Recognition. Jan-Michael Frahm. Spring 2014. Introductions. 2. Grade Requirements. Presentation of . 2 . papers in class . 30 min talk, . Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Many slides adapted from S. Seitz. Multi-view . stereo. Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape. Reconstruction (side). If necessary, rectify the two stereo images to transform . epipolar. lines into . scanlines. For each pixel x in the first image. Find corresponding . epipolar. . scanline. in the right image. Examine all pixels on the . Stereo and Multiple . View Geometry. Presented by. Radhakrishna Dasari . Contents. Stereo Practical Demo. Camera Intrinsic and Extrinsic parameters. Essential and Fundamental Matrix. Multiple View . Geometry. Many slides drawn from Lana . Lazebnik. , UIUC. Basic stereo matching algorithm. For each pixel in the first image. Find corresponding . epipolar. line in the right image. Examine all pixels on the . Shading. [Figure from Prados & Faugeras 2006]. Focus/defocus. [figs from H. Jin and P. Favaro, 2002]. Images from same point of view, different camera parameters. 3d shape / depth estimates. Texture. Several slides from Larry . Zitnick. and Steve Seitz. Why do we perceive depth?. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). . 1. 2. Why do we perceive depth?. 3. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). . Several slides from Larry . Zitnick. and Steve Seitz. Why do we perceive depth?. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). .