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Dense depth maps from correspondences derived from perceived motion

[+] Author Affiliations
Richard Kirby, Ross Whitaker

University of Utah, School of Computing, Salt Lake City, Utah, United States

J. Electron. Imaging. 26(1), 013026 (Feb 25, 2017). doi:10.1117/1.JEI.26.1.013026
History: Received December 7, 2016; Accepted February 9, 2017
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Abstract.  Many computer vision applications require finding corresponding points between images and using the corresponding points to estimate disparity. Today’s correspondence finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3-D computer vision applications, however, do not produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. We present an image correspondence finding technique that aligns pairs of image sequences using optical flow fields. The optical flow fields provide information about the structure and motion of the scene, which are not available in still images but can be used in image alignment. We apply the technique to a dual focal length stereo camera rig consisting of a visible light—infrared camera pair and to a coaxial camera rig. We test our method on real image sequences and compare our results with the state-of-the-art multimodal and structure from motion (SfM) algorithms. Our method produces more accurate depth and scene velocity reconstruction estimates than the state-of-the-art multimodal and SfM algorithms.

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Citation

Richard Kirby and Ross Whitaker
"Dense depth maps from correspondences derived from perceived motion", J. Electron. Imaging. 26(1), 013026 (Feb 25, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013026


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Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling. IEEE Trans Pattern Anal Mach Intell 2014;36(11):2144-58.
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