The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

This paper presents a structure-from-motion system which delivers dense structure information from a sequence of dense optical flows. Most traditional feature-based approaches cannot be extended to compute dense structure due to impractical computational complexity. We demonstrate that by decomposing uncertainty information into independent and correlated parts we can decrease these complexities from O (N2) to O(N), where is the number of pixels in the images. We also show that this dense structure-from-motion system requires only local optical flows, i.e. image matchings between two adjacent frames, instead of the tracking of features over a long sequence of frames.

Notes

Grant ID: DACA76-89-C-0014, DAAE07-90-C-R059

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Calibrated Imaging Lab

Number of pages: 33

Text Reference

Yalin Xiong and Steven Shafer, "Dense Structure from a Dense Optical Flow Sequence," tech. report CMU-RI-TR-95-10, Robotics Institute, Carnegie Mellon University, April, 1995

BibTeX Reference

@techreport{Xiong_1995_369,
   author = "Yalin Xiong and Steven Shafer",
   title = "Dense Structure from a Dense Optical Flow Sequence",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "1995",
   number= "CMU-RI-TR-95-10",
   address= "Pittsburgh, PA",
}

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