Generalized subgraph preconditioners for large-scale bundle adjustment

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. © 2012 Springer-Verlag.