The Computation of Stereo Disparity for Transparent and for Opaque Surfaces

The classical computational model for stereo vision incorporates a uniqueness inhibition constraint to enforce a one-to-one feature match, thereby sacrificing the ability to handle transparency. Critics of the model disregard the uniqueness constraint and argue that the smoothness constraint can provide the excitation support required for transparency computation. However, this modification fails in neighborhoods with sparse features. We propose a Bayesian approach to stereo vision with priors favoring cohesive over transparent surfaces. The disparity and its segmentation into a multi-layer "depth planes" representation are simultaneously computed. The smoothness constraint propagates support within each layer, providing mutual excitation for non-neighboring transparent or partially occluded regions. Test results for various random-dot and other stereograms are presented.