NEURAL MODELS OF STEREOSCOPIC VISION

Human stereopsis remains an enigma: how does the brain match features between the left and right eye images and compute disparity between these matched features? Developments in computational neuroscience and machine vision have led to several models of human stereopsis that provide insight into possible mechanisms underlying this phenomenon. These models, reviewed in this paper, adopt one of three general strategies. One class of models employs cooperative interactions, whereby a unique solution to the matching problem emerges from excitatory and inhibitory interactions among binocular neural elements. A second class of models implements matching and disparity computation serially over multiple spatial scales. A third class relies on local, non-interacting computations performed in parallel to overcome speed limitations inherent in the other models. Considered together, these theoretical developments offer fresh insights concerning the actual neural concomitants of binocular stereopsis.