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 appreciatingthe significant role that veridicality and complexity play in the natural visual environment. Theability to see objects as they really are ''out there" is derived from the complexity inherent in the 3Dobject'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 3Dshape of an object from only one of its 2D images. This model uses a simplicity principle consisting ofonly 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 humanbeing. In the rare recoveries in which errors are observed, the errors made by the model and human subjectsare very similar. The model makes no use of depth, surfaces or learning. Recent elaborations of thismodel include: (i) the recovery of the shapes of natural objects, including human and animal bodies withlimbs in varying positions (ii) providing the model with two input images that allowed it to achieve virtuallyperfect shape constancy from almost all viewing directions. The review concludes with a comparisonof some of the highlights of our novel, successful approach to the recovery of 3D shape from a 2Dimage with prior, less successful approaches.