Generalized subgraph preconditioners for large-scale bundle adjustment

Authors:
Yong-Dian Jian;Doru C. Balcan;Frank Dellaert
Affiliations:
College of Computing, Georgia Institute of Technology, USA;College of Computing, Georgia Institute of Technology, USA;College of Computing, Georgia Institute of Technology, USA
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

Citing 0
Cited 3

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
Dense 3d reconstruction from wide baseline image sets

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.