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Abstract

When trying to recover 3D structure from a set of images, the most difficult problem is establishing the correspondence between the measurements. Most existing approaches assume that features can be tracked across frames, whereas methods that exploit rigidity constraints to facilitate matching do so only under restricted camera motion. In this paper we propose a Bayesian approach that avoids the brittleness associated with singling out one "best" correspondence, and instead consider the distribution over all possible correspondences. We treat both a fully Bayesian approach that yields a posterior distribution, and a MAP approach that makes use of EM to maximize this posterior. We show how Markov chain Monte Carlo methods can be used to implement these techniques in practice, and present experimental results on real data.

Notes

Text Reference

Frank Dellaert, Steven Seitz, Chuck Thorpe, and Sebastian Thrun, "Feature Correspondence: A Markov Chain Monte Carlo Approach," Advances in Neural Information Processing Systems 13 (NIPS 2000), 2001.

BibTeX Reference

@inproceedings{Dellaert_2001_3429,
   author = "Frank Dellaert, Steven Seitz, Chuck Thorpe, and Sebastian Thrun",
   title = "Feature Correspondence: A Markov Chain Monte Carlo Approach",
   booktitle = "Advances in Neural Information Processing Systems 13 (NIPS 2000)",
   year = "2001",
}

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