A Graph-Theoretic Game and its Application to the $k$-Server Problem
SIAM Journal on Computing
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Support Theory for Preconditioning
SIAM Journal on Matrix Analysis and Applications
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate
ACM Transactions on Mathematical Software (TOMS)
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)
Bundle adjustment in the large
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Conjugate gradient bundle adjustment
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Algorithm 915, SuiteSparseQR: Multifrontal multithreaded rank-revealing sparse QR factorization
ACM Transactions on Mathematical Software (TOMS)
Generalized subgraph preconditioners for large-scale bundle adjustment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose the Generalized Subgraph Preconditioners (GSP) to solve large-scale bundle adjustment problems efficiently. In contrast with previous work using either direct or iterative methods alone, GSP combines their advantages and is significantly faster on large datasets. Similar to [12], the main idea is to identify a sub-problem (subgraph) that can be solved efficiently by direct methods and use its solution to build a preconditioner for the conjugate gradient method. The difference is that GSP is more general and leads to more effective preconditioners. When applied to the "bal" datasets [2], our method shows promising results.