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ICCV
2011
IEEE

Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment

7 years 4 months ago
Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment
We present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either direct or iterative methods as the linear solver, GSP combines their advantages and is significantly faster on large datasets. Similar to [11], the main idea is to identify a sub-problem (subgraph) that can be solved efficiently by sparse factorization methods and use it to build a preconditioner for the conjugate gradient method. The difference is that GSP is more general and leads to much more effective preconditioners. We design a greedy algorithm to build subgraphs which have bounded maximum clique size in the factorization phase, and also result in smaller condition numbers than standard preconditioning techniques. When applying the proposed method to the “bal” datasets [1], GSP displays promising performance.
Yong-Dian Jian, Doru C. Balcan, Frank Dellaert
Added 11 Dec 2011
Updated 11 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Yong-Dian Jian, Doru C. Balcan, Frank Dellaert
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