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Uncalibrated Stereo Correspondence
by Singular Value Decomposition

Maurizio Pilu          Adele Lorusso
Digital Media Department
Hewlett-Packard Research Labs
Filton Road, Stoke Gifford
Bristol BS12 6QZ (UK)
email: mp,lorusso@hplb.hpl.hp.com

Abstract:

This paper presents a new simple method for achieving feature correspondence across a pair of images which requires no calibration information and draws from the method proposed by Scott and Longuet Higgins [ 8 ]. Despite the well-known combinatorial complexity of the problem, this work shows that an acceptably good solution can be obtained directly by singular value decomposition of an appropriate image-based correspondence strength matrix. The paper includes several experiments and discusses the method and draws qualitative comparisons with the relaxation method in [ 14 ]. Given its tremendous performance/complexity figure, the method is particularly suitable for research purposes where an off-the-shelf but reliable feature correspondence method in needed. For this reason, a succinct MATLAB implementation of the method is given in the paper and a ``C'' version has been made available.

• Introduction
• The correspondence problem
• The Scott and Longuet-Higgins algorithm
• Stereo matching by SVD
• Some experimental results
• Comparison with a recent relaxation method
• Discussion
• References
• About this document ...