In order to obtain optimal 3D structure and viewing parameter estimates, bundle adjustment is often used as the last step of feature-based structure and motion estimation algorithms. Bundle adjustment involves the formulation of a large scale, yet sparse minimization problem, which is traditionally solved using a sparse variant of the Levenberg-Marquardt optimization algorithm that avoids storing and operating on zero entries. This paper argues that considerable computational benefits can be gained by substituting the sparse Levenberg-Marquardt algorithm in the implementation of bundle adjustment with a sparse variant of Powell's dog leg non-linear least squares technique. Detailed comparative experimental results provide strong evidence supporting this claim.


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    Titel :

    Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?


    Beteiligte:
    Lourakis, M.L.A. (Autor:in) / Argyros, A.A. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    179615 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?

    Lourakis, M. / Argyros, A. / IEEE | British Library Conference Proceedings | 2005


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