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ACM Transactions on Mathematical Software (TOMS)
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ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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ACM Transactions on Mathematical Software (TOMS)
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ACM Transactions on Mathematical Software (TOMS)
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
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Image and Vision Computing
Communications of the ACM
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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
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EuroMed'12 Proceedings of the 4th international conference on Progress in Cultural Heritage Preservation
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ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Large-Scale bundle adjustment by parameter vector partition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Learning feature subspaces for appearance-based bundle adjustment
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Incremental 3D reconstruction using Bayesian learning
Applied Intelligence
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We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.