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2011
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Distributed Message Passing for Large Scale Graphical Models

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Distributed Message Passing for Large Scale Graphical Models
In this paper we propose a distributed message-passing algorithm for inference in large scale graphical models. Our method can handle large problems efficiently by distributing and parallelizing the computation and the memory requirements. The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages, sent between the distributed computers. We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery, and show that inference is possible with more than 200 labels in images larger than 10 MPixel.
Alexander Schwing, Hazan Tamir, Marc Pollefeys, Ra
Added 24 Feb 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Alexander Schwing, Hazan Tamir, Marc Pollefeys, Raquel Urtasun
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