PPT-A Non-Local Cost Aggregation Method for Stereo Matching

Yang QingXiong 杨庆雄 City University of Hong Kong 3 6 9 2 5 8 1 4 7 A 2D image 3x3 gt a planar graph 3 6 9 2 5 8 1 4 7 Computing minimum spanning tree MST. 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. Chapter 11 Stereo Correspondence. Presented by: . 蘇唯誠. 0921679513. r02922114@ntu.edu.tw. 指導教授. : . 傅楸善 博士. Introduction. Stereo matching is the process of taking two or more images and estimating a 3D model of the scene by finding matching pixels in the images and converting their 2D positions into 3D depths.. 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. Thesis by . Asya. . Leikin. Under the supervision of Prof. Leonid P. . Yaroslavsky. . Stereopsis. - the process that allows our visual system to translate the captured 2D images (for each eye) into 3D perception [1]. 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. Kumar Pandey. 1. Stéphane. Weiss. 1. Roman Vitenberg. 1. Kaiwen. Zhang. 2. Hans-Arno Jacobsen. 2. 2. University of Toronto. 1. University of Oslo. Minimizing the communication cost of aggregation in Publish/Subscribe systems. 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 . Xi Mo. 4/3/2017. x. y. z. x. y. xl. xr. Disparity=xl-. xr. Disparity space. Traditional way of stereo matching. Benchmark of. Middlebury. 3DMST(Rank 1). Error . Map. Disparity map of left view. Left view. Noah . Snavely. , . Zhengqi. Li. Single image stereogram, by . Niklas. . Een. Mark Twain at Pool Table", no date, UCR Museum of Photography. Stereo. Given two images from different viewpoints. How can we compute the depth of each point in the image?. Network to Compare Image Patches. Jure . Zbontar. , Yann . LeCun. Background. Motivation. Problem Formulation. Methodology. Training Data. Suggested Net Architectures. Sequential Steps. Results. Conclusion. 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). .