A Bayesian model of binocular perception of 3D mirror symmetrical polyhedra

Abstract

In our previous studies, we showed that monocular perception of 3D shapes is based on a priori constraints, such as 3D symmetry and 3D compactness. The present study addresses the nature of perceptual mechanisms underlying binocular perception of 3D shapes. First, we demonstrate that binocular performance is systematically better than monocular performance, and it is close to perfect in the case of three out of four subjects. Veridical shape perception cannot be explained by conventional binocular models, in which shape was derived from depth intervals. In our new model, we use ordinal depth of points in a 3D shape provided by stereoacuity and combine it with monocular shape constraints by means of Bayesian inference. The stereoacuity threshold used by the model was estimated for each subject. This model can account for binocular shape performance of all four subjects. It can also explain the fact that when viewing distance increases, the binocular percept gradually reduces to the monocular one, which implies that monocular percept of a 3D shape is a special case of the binocular percept.