Abstract
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.
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Lourakis, M.I.A. (2010). Sparse Non-linear Least Squares Optimization for Geometric Vision. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15552-9_4
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