DocumentCode
2917272
Title
Multicore bundle adjustment
Author
Wu, Changchang ; Agarwal, Sameer ; Curless, Brian ; Seitz, Steven M.
Author_Institution
Univ. of Washington, Seattle, WA, USA
fYear
2011
fDate
20-25 June 2011
Firstpage
3057
Lastpage
3064
Abstract
We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overcoming the severe memory and bandwidth limitations of current generation GPUs not only leads to more space efficient algorithms, but also to surprising savings in runtime. Our CPU based system is up to ten times and our GPU based system is up to thirty times faster than the current state of the art methods, while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs.washington.edu/projects/mcba.
Keywords
computer graphic equipment; computer vision; coprocessors; image reconstruction; multiprocessing systems; natural scenes; 3D scene reconstruction; GPU; bundle adjustment algorithm; inexact Newton algorithm; multicore CPU; space efficient algorithms; Cameras; Graphics processing unit; Instruction sets; Jacobian matrices; Multicore processing; Random access memory; Sparse matrices;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4577-0394-2
Type
conf
DOI
10.1109/CVPR.2011.5995552
Filename
5995552
Link To Document