Camera Calibration and 3D Reconstruction Using RBF Network in Stereovision System

  • Hai-feng Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


In this paper, RBF network (RBFN) is used to provide effective methodologies for solving difficult computational problems in camera calibration and 3D reconstruction process. RBFN works in three aspects: Firstly, a RBFN is adopted to learn and memorize the nonlinear relationship in stereovision system. Secondly, another RBFN is trained to search the correspondent lines in two images such that stereo matching is performed in one dimension. Finally, the trained network in the first stage is used to reconstruct the object’s 3D figuration and surface. The technique avoids the complicated and large calculation in conventional methods. Experiments have been performed on common stereo pairs and the results are accurate and convincing.


Radial Basis Function Network Camera Calibration Stereo Image Stereo Match Stereo Pair 
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

  • Hai-feng Hu
    • 1
  1. 1.Department of Electronics and Communication EngineeringSun Yat-sen UniversityGuangzhouP.R. China

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