Multicore bundle adjustment

Changchang Wu, Sameer Agarwal, Brian Curless, Steven M. Seitz
University of Washington
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011


   title={Multicore Bundle Adjustment},

   author={Wu, C. and Agarwal, S. and Curless, B. and Seitz, S.M.},

   booktitle={Multicore bundle adjustment},



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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 [1], while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs. washington.edu/projects/mcba.
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