Paper Info
Title
Bundle adjustment in the large
Abstract
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15552-9_3
ECCV (2)
Keywords
Field
DocType
large-scale bundle adjustment problem,bundle adjustment problem,large-scale bundle adjustment,different bundle adjustment algorithm,art performance,computationally efficient preconditioners,truncated newton method,newton step,newton type algorithm,detailed performance data,bundle adjustment,conjugate gradient,structure from motion,schur complement
Conjugate gradient method,Structure from motion,Mathematical optimization,Bundle adjustment,Computer science,Algorithm,Newton's method in optimization,Artificial intelligence,Factorization,Machine learning,Schur complement,Levenberg–Marquardt algorithm
Conference
Volume
ISSN
ISBN
6312
0302-9743
3-642-15551-0
Citations 
PageRank 
References 
106
4.09
14
Authors
4
Search Limit
100106
Name
Order
Citations
PageRank
Sameer Agarwal110328478.10
Noah Snavely24262197.04
Steven M. Seitz38729495.13
Richard Szeliski4213002104.74