TY - GEN
T1 - Distributed message passing for large scale graphical models
AU - Schwing, Alexander
AU - Hazan, Tamir
AU - Pollefeys, Marc
AU - Urtasun, Raquel
PY - 2011
Y1 - 2011
N2 - 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 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 MPixels.
AB - 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 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 MPixels.
UR - http://www.scopus.com/inward/record.url?scp=80052908754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052908754&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2011.5995642
DO - 10.1109/CVPR.2011.5995642
M3 - Conference contribution
AN - SCOPUS:80052908754
SN - 9781457703942
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1833
EP - 1840
BT - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PB - IEEE Computer Society
ER -