Sparse Non-linear Least Squares Optimization for Geometric Vision

  • Manolis I. A. Lourakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


Several estimation problems in vision involve the minimization of cumulative geometric error using non-linear least-squares fitting. Typically, this error is characterized by the lack of interdependence among certain subgroups of the parameters to be estimated, which leads to minimization problems possessing a sparse structure. Taking advantage of this sparseness during minimization is known to achieve enormous computational savings. Nevertheless, since the underlying sparsity pattern is problem-dependent, its exploitation for a particular estimation problem requires non-trivial implementation effort, which often discourages its pursuance in practice. Based on recent developments in sparse linear solvers, this paper provides an overview of sparseLM, a general-purpose software package for sparse non-linear least squares that can exhibit arbitrary sparseness and presents results from its application to important sparse estimation problems in geometric vision.


Bundle Adjustment Sparsity Pattern Trifocal Tensor Multiple View Geometry Camera Matrice 
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 2010

Authors and Affiliations

  • Manolis I. A. Lourakis
    • 1
  1. 1.Institute of Computer Science, Foundation for Research and Technology - HellasHeraklion, CreteGreece

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