New approach to the perception of 3D shape based on veridicality, complexity, symmetry and volume

This paper reviews recent progress towards understanding 3D shape perception made possible by appreciating the significant role that veridicality and complexity play in the natural visual environment. The ability to see objects as they really are “out there” is derived from the complexity inherent in the 3D object’s shape. The importance of both veridicality and complexity was ignored in most prior research. Appreciating their importance made it possible to devise a computational model that recovers the 3D shape of an object from only one of its 2D images. This model uses a simplicity principle consisting of only four a priori constraints representing properties of 3D shapes, primarily their symmetry and volume. The model recovers 3D shapes from a single 2D image as well, and sometimes even better, than a human being. In the rare recoveries in which errors are observed, the errors made by the model and human subjects are very similar. The model makes no use of depth, surfaces or learning. Recent elaborations of this model include: (i) the recovery of the shapes of natural objects, including human and animal bodies with limbs in varying positions (ii) providing the model with two input images that allowed it to achieve virtually perfect shape constancy from almost all viewing directions. The review concludes with a comparison of some of the highlights of our novel, successful approach to the recovery of 3D shape from a 2D image with prior, less successful approaches.