We present a new algorithm for reconstructing 3D shapes. The algorithm takes one 2D image of a 3D shape and
reconstructs the 3D shape by applying a priori constraints: symmetry, planarity and compactness. The shape is
reconstructed without using information about the surfaces, such as shading, texture, binocular disparity or motion.
Performance of the algorithm is illustrated on symmetric polyhedra, but the algorithm can be applied to a very wide
range of shapes. Psychophysical plausibility of the algorithm is discussed.
Binocular reconstruction of a 3D shape is an ill-conditioned inverse problem: in the presence of visual and oculomotor noise the reconstructions based solely on visual data are very unstable. A question, therefore, arises about the nature of a priori constraints that would lead to accurate and stable solutions. Our previous work showed that planarity of contours, symmetry of an object and minimum variance of angles are useful priors in binocular reconstruction of polyhedra. Specifically, our algorithm begins with producing a 3D reconstruction from one retinal image by applying priors. The second image (binocular disparity) is then used to correct the monocular reconstruction. In our current study, we performed psychophysical experiments to test the importance of these priors. The subjects were asked to recognize shapes of 3D polyhedra from unfamiliar views. Hidden edges of the polyhedra were removed. The recognition performance, measured by detectability measure d¢, was high when shapes satisfied regularity constraints, and was low otherwise. Furthermore, the binocular recognition performance was highly correlated with the monocular one. The main aspects of our model will be illustrated by a demo, in which binocular disparity and monocular priors are put in conflict.