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Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment

  • Yong-Dian Jian
  • Doru C. Balcan
  • Frank Dellaert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)

Abstract

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.

Keywords

Conjugate Gradient Method Unary Factor Original Graph Factor Graph Bundle Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yong-Dian Jian
    • 1
  • Doru C. Balcan
    • 1
  • Frank Dellaert
    • 1
  1. 1.College of ComputingGeorgia Institute of TechnologyUSA

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