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The stereo correspondence problem exists because false matches between the images from multiple sensors camouflage the true (veridical) matches. True matches are correspondences between image points that have the same generative source; false matches are correspondences between similar image points that have different sources. This problem of selecting true matches among false ones must be overcome by both biological and artificial stereo systems in order for them to be useful depth sensors. The proposed re-examination of this fundamental issue shows that false matches form a symmetrical pattern in the array of all possible matches, with true matches forming the axis of symmetry. The patterning of false matches can therefore be used to locate true matches and derive the depth profile of the surface that gave rise to them. This reverses the traditional strategy, which treats false matches as noise. The new approach is particularly well-suited to extract the 3-D locations and shapes of camouflaged surfaces and to work in scenes characterized by high degrees of clutter. We demonstrate that the symmetry of false-match signals can be exploited to identify surfaces in random-dot stereograms. This strategy permits novel depth computations for target detection, localization, and identification by machine-vision systems, accounts for physiological and psychophysical findings that are otherwise puzzling and makes possible new ways for combining stereo and motion signals.

Solving the stereo correspondence problem with false matches