Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.
CITATION STYLE
Drory, A., Haubold, C., Avidan, S., & Hamprecht, F. A. (2014). Semi-global matching: A principled derivation in terms of message passing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8753, pp. 43–53). Springer Verlag. https://doi.org/10.1007/978-3-319-11752-2_4
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