Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features

  • Jun Liu
  • Roger Hubbold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


The goal of our research is to robustly reconstruct general 3D scenes from 2D images, with application to automatic model generation in computer graphics and virtual reality. In this paper we aim at producing relatively dense and well-distributed 3D points which can subsequently be used to reconstruct the scene structure. We present novel camera calibration and scene reconstruction using scale-invariant feature points. A generic high-dimensional vector matching scheme is proposed to enhance the efficiency and reduce the computational cost while finding feature correspondences. A framework for structure and motion is also presented that better exploits the advantages of scale-invariant features. In this approach we solve the “phantom points” problem and this greatly reduces the possibility of error propagation. The whole process requires no information other than the input images. The results illustrate that our system is capable of producing accurate scene structure and realistic 3D models within a few minutes.


Augmented Reality Camera Calibration Scale Invariant Feature Transformation Structure From Motion Descriptor Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Liu
    • 1
  • Roger Hubbold
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUnited Kingdom

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