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.
Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 ; 2 ; 1526-1531 Vol. 2
2005-01-01
179615 byte
Conference paper
Electronic Resource
English
Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?
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