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Carnegie Mellon Robotics Institute

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Abstract

We describe a factorization-based method to recover 3D models 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. We present the results of applying this method to building modeling, terrain recovery and multi-camera calibration.

Keywords

structure from motion, camera self-calibration, 3D modeling

Notes

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

Associated Lab(s) / Group(s): Virtualized Reality^TM

Associated Project(s): Modeling by Videotape

Text Reference

Mei Han and Takeo Kanade, "Creating 3D Models with Uncalibrated Cameras," proceeding of IEEE Computer Society Workshop on the Application of Computer Vision (WACV2000), December, 2000.

BibTeX Reference

@inproceedings{Han_2000_3391,
   author = "Mei Han and Takeo Kanade",
   title = "Creating 3D Models with Uncalibrated Cameras",
   booktitle = "proceeding of IEEE Computer Society Workshop on the Application of Computer Vision (WACV2000)",
   month = "December",
   year = "2000",
}

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