A perspective factorization method for Euclidean reconstruction with uncalibrated cameras

Abstract

AbstractStructure from motion (SFM), which is recovering camera motion and scene structure from image sequences, has various applications, such as scene modelling, robot navigation, object recognition and virtual reality. Most of previous research on SFM requires the use of intrinsically calibrated cameras. In this paper we describe a factorization‐based method to recover Euclidean structure 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. The process of updating a projective solution to a full metric one is referred as normalization in most factorization‐based SFM methods. We present three normalization algorithms which enforce Euclidean constraints on camera calibration parameters to recover the scene structure and the camera calibration simultaneously, assuming zero skew cameras. 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. The results of experiments are presented. Copyright © 2002 John Wiley & Sons, Ltd.