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Global Stereo Reconstruction under Second Order Smoothness Priors
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BibTeX
@MISC{Woodford_globalstereo,
author = {O. J. Woodford and et al.},
title = {Global Stereo Reconstruction under Second Order Smoothness Priors },
year = {}
}
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Abstract
Second-order priors on the smoothness of 3D surfaces are a better model of typical scenes than first-order priors. However, stereo reconstruction using global inference algorithms, such as graph-cuts, has not been able to incorporate second-order priors because the triple cliques needed to express them yield intractable (non-submodular) optimization problems. This paper shows that inference with triple cliques can be effectively optimized. Our optimization strategy is a development of recent extensions to α-expansion, based on the “QPBO ” algorithm [5, 14, 26]. The strategy is to repeatedly merge proposal depth maps using a novel extension of QPBO. Proposal depth maps can come from any source, for example fronto-parallel planes as in α-expansion, or indeed any existing stereo algorithm, with arbitrary parameter settings. Experimental results demonstrate the usefulness of the second-order prior and the efficacy of our optimization framework. An implementation of our stereo framework is available online [34].
Keyphrases
second order smoothness prior global stereo reconstruction second-order prior triple clique first-order prior stereo reconstruction stereo framework optimization framework qpbo algorithm recent extension global inference algorithm optimization strategy optimization problem typical scene arbitrary parameter setting experimental result example fronto-parallel plane available online stereo algorithm novel extension proposal depth map
