SIAM Journal on Scientific and Statistical Computing
Matrix computations (3rd ed.)
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
3D Urban Scene Modeling Integrating Recognition and Reconstruction
International Journal of Computer Vision
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Image and Vision Computing
Generalized subgraph preconditioners for large-scale bundle adjustment
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
How to localize humanoids with a single camera?
Autonomous Robots
Large-Scale bundle adjustment by parameter vector partition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Learning feature subspaces for appearance-based bundle adjustment
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Bundle adjustment for multi-view reconstruction is traditionally done using the Levenberg-Marquardt algorithm with a direct linear solver, which is computationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop. This is appealing since the main computational step of the CG algorithm involves only a simple matrix-vector multiplication with the Jacobian. In this work we improve on the latest published approaches to bundle adjustment with conjugate gradients by making full use of the least squares nature of the problem. We employ an easy-to-compute QR factorization based block preconditioner and show how a certain property of the preconditioned system allows us to reduce the work per iteration to roughly half of the standard CG algorithm.