Multicore Bundle Adjustment
Venue
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2011), pp. 3057-3064
Publication Year
2011
Authors
Changchang Wu, Sameer Agarwal, Brian Curless, Steven Seitz
BibTeX
Abstract
The emergence of multi-core computers represents a fundamental shift, with major
implications for the design of computer vision algorithms. Most computers sold
today have a multicore CPU with 2-16 cores and a GPU with anywhere from 4 to 128
cores. Exploiting this hardware parallelism will be key to the success and
scalability of computer vision algorithms in the future. In this project, we
consider 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.
