Cortical dynamics of three-dimensional surface perception: Binocular and half-occluded scenic images

Previous models of stereopsis have concentrated on the task of binocularly matching left and right eye primitives uniquely. A disparity smoothness constraint is often invoked to limit the number of possible matches. These approaches neglect the fact that surface discontinuities are both abundant in natural everyday scenes, and provide a useful cue for scene segmentation. da Vinci stereopsis refers to the more general problem of dealing with surface discontinuities and their associated unmatched monocular regions within binocular scenes. This study develops a mathematical realization of a neural network theory of biological vision, called FACADE theory, that shows how early cortical stereopsis processes are related to later cortical processes of three-dimensional surface representation. The mathematical model demonstrates through computer simulation how the visual cortex may generate three-dimensional boundary segmentations and use them to control filling-in of three-dimensional surface properties in response to visual scenes. Model mechanisms correctly match disparate binocular regions while filling-in monocular regions with the correct depth within a binocularly viewed scene. This achievement required the introduction of a new multiscale binocular filter for stereo matching which clarifies how cortical complex cells match image contours of like contrast polarity, while pooling signals from opposite contrast polarities. The filter also suggests how false binocular matches and unmatched monocular cites are automatically handled across multiple spatial scales. Pooling of signals from even-and odd-symmetric simple cells at complex cells helps to eliminate spurious activity peaks in matchable signals. Later stages of cortical processing by the blob and interblob streams, including refined models of cooperative boundary grouping and reciprocal stream interactions between boundary and surface representations, are modeled to provide a complete simulation of the da Vinci stereopsis percept. (C) 1997 Elsevier Science Ltd. All rights reserved.