A Domain Reduction Algorithm for Incremental Projective Reconstruction

  • Rafael Lemuz-López
  • Miguel Arias-Estrada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


In this paper we address the problem of recovering the three-dimensional shape of an object and the motion of the camera based on multiple feature correspondences from an image sequence. We present a new incremental projective factorization algorithm using a perspective camera model. The original projective factorization method produces robust results. However, the method can not be applied to real-time applications since it is based on a batch processing pipeline and the size of the data matrix grows with each additional frame. The proposed algorithm obtains an estimate of shape and motion for each additional frame adding a dimension reduction step. A subset of frames is selected analyzing the contribution of frames to the reconstruction quality. The main advantage of the novel algorithm is the reduction of the computational cost while keeping the robustness of the original method. Experiments with synthetic and real images illustrate the accuracy and performance of the new algorithm.


Singular Value Decomposition Factorization Method Measurement Matrix Bundle Adjustment Epipolar Geometry 
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

  • Rafael Lemuz-López
    • 1
  • Miguel Arias-Estrada
    • 1
  1. 1.Instituto Nacional de Astrofísica Óptica y ElectrónicaTonantzintlaMéxico

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