The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We describe a factorization-based method to reconstruct Euclidean shape and motion from multiple perspective views with uncalibrated cameras. The method first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The first two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear, dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. Experimental results show that out method is efficient and reliable.

Keywords

structure from motion, self-calibration, Euclidean reconstruction

Notes

Text Reference

Mei Han and Takeo Kanade, "Scene Reconstruction from Multiple Uncalibrated Views," tech. report CMU-RI-TR-00-09, Robotics Institute, Carnegie Mellon University, January, 2000

BibTeX Reference

@techreport{Han_2000_3303,
   author = "Mei Han and Takeo Kanade",
   title = "Scene Reconstruction from Multiple Uncalibrated Views",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "2000",
   number= "CMU-RI-TR-00-09",
   address= "Pittsburgh, PA",
}

The Robotics Institute

Menu