Graphics enhanced version of this site

Addressing the Correspondence Problem: A Markov Chain Monte Carlo Approach
F. Dellaert
tech. report CMU-RI-TR-00-11, Robotics Institute, Carnegie Mellon University, January, 2000.

Jump to: Download | Abstract | Text Reference | BibTeX Reference

Adobe portable document format (pdf) [141 KB]
Compressed postscript (ps.gz) [104 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Many vision and AI techniques assume that some form of the infamous correspondence problem has been solved. Typically, a best mapping between sets of features is found either as a pre-processing step or as a side-effect of applying the technique. In this paper we argue that it is incorrect to insist on a single 'best' mapping between features in order to estimate a property that depends on this correspondence. Instead, one should take into account the posterior distribution over all possible mappings, given the measured feature data. The estimate thus obtained can differ substantially from the one where a best mapping is first singled out. The main contribution in this paper is to show how Markov Chain Monte Carlo methods can be used to efficiently sample the correct distribution over correspondences, and how this sample can subsequently be used to estimate a property of interest. We will show examples and results for several applications, including pose estimation and structure from motion. The method we propose can be used in any application where the correspondence problem is a central component.

F. Dellaert, Addressing the Correspondence Problem: A Markov Chain Monte Carlo Approach, tech. report CMU-RI-TR-00-11, Robotics Institute, Carnegie Mellon University, January, 2000.

@techreport{Dellaert_2000_3307,
   author = "Frank Dellaert",
   title = "Addressing the Correspondence Problem: A Markov Chain Monte Carlo Approach",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "January",
   year = "2000",
   number = "CMU-RI-TR-00-11",
   address = "Pittsburgh, PA"
}