Title:
Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment
Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment
| dc.contributor.author | Jian, Yong-Dian | |
| dc.contributor.author | Balcan, Doru C. | |
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.date.accessioned | 2012-09-12T22:03:27Z | |
| dc.date.available | 2012-09-12T22:03:27Z | |
| dc.date.issued | 2011-11 | |
| dc.description | ©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | Presented at the 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona, Spain. | |
| dc.description | DOI: 10.1109/ICCV.2011.6126255 | |
| dc.description.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. | en_US |
| dc.identifier.citation | Jian, Y-D; Balcan, D.C.; & Dellaert, F. (2011). "Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment". Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, pp.295-302. | en_US |
| dc.identifier.doi | 10.1109/ICCV.2011.6126255 | |
| dc.identifier.issn | 1550-5499 | |
| dc.identifier.uri | http://hdl.handle.net/1853/44646 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Bundle adjustment | en_US |
| dc.subject | Datasets | en_US |
| dc.subject | Generalized subgraph preconditioning | en_US |
| dc.title | Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |