Levin Kuhlmann, Stephen Grossberg, Ennio Mingolla; 3D surface representations derived from texture gradients: Filtering, grouping and filling-in. Journal of Vision 2006;6(6):255. doi: 10.1167/6.6.255.
A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are grouped into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive feedback loop that coherently groups texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a 3D surface percept. The model quantitatively simulates a large set of psychophysical data pertaining to perception of prolate ellipsoids (Todd and Akerstrom. 1987, J. Exp. Psych., 13, 242). In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. To demonstrate the more general capabilities of the model, simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are presented.