The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

In this paper we describe a factorization-based method for Euclidean recon- struction with a perspective camera model. It iteratively recovers shape and motion by weak perspective factorization method and converges to a per- spective model. We discuss the approach of solving the reversal shape am- biguity and analyze its convergence. We also present a factorization-based method to recover Euclidean shape and camera focal lengths from multiple semi-calibrated perspective views. The focal lengths are the only unknown intrinsic camera parameters and they are not necessarily constant among dif- ferent views. The method rst performs projective reconstruction by using iterative factorization, then converts the projective solution to the Euclidean one and generates the focal lengths by using normalization constraints. This method introduces a new way of camera self-calibration. Experiments of shape reconstruction and camera calibration are presented. We design a cri- terion called back projection compactness to quantify the calibration results. It measures the radius of the minimum sphere through which all back projec- tion rays from the image positions of the same object point pass. We discuss the validity of this criterion and use it to compare the calibration results with other methods.

Keywords

structure from motion, calibration, computer vision

Notes

Text Reference

Mei Han and Takeo Kanade, "Perspective Factorization Methods for Euclidean Reconstruction," tech. report CMU-RI-TR-99-22, Robotics Institute, Carnegie Mellon University, August, 1999

BibTeX Reference

@techreport{Han_1999_3225,
   author = "Mei Han and Takeo Kanade",
   title = "Perspective Factorization Methods for Euclidean Reconstruction",
   booktitle = "",
   institution = "Robotics Institute",
   month = "August",
   year = "1999",
   number= "CMU-RI-TR-99-22",
   address= "Pittsburgh, PA",
}

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