A laminar cortical model of three-dimensional surface perception and figure-ground separation: Stereogram depth, lightness, and amodal completion
In viewing a 3D scene, object features are seen on 3D surfaces infused with lightness and color at correct depths. By only focusing on how left and right features are correctly matched, most 3D vision models have not explained how this happens. A 3D LAMINART model (Grossberg and Howe, 2003; Cao and Grossberg, 2005) proposed that laminar cortical mechanisms interact to create 3D surface percepts using interactions between boundary and surface representations. Previous work using this model explained perception of relatively simple objects, like bars and blocks, in relatively simple spatial configurations that did not contain any mutual occlusions. This thesis extends the 3D LAMINART model to predict how textured images with multiple potential false binocular matches, e.g. dense stereograms, generate correct 3D surface representations of figures and their backgrounds. The model also clarifies how sparse stereograms can induce the formation of continuous surfaces at correct depths across contrast-free regions. Furthermore, when textured stereograms define emergent occluding and occluded surfaces, the model shows how these surfaces are correctly separated in depth and the partially occluded textured surfaces can be amodally completed behind the occluding textured surface. Thus, the model provides a unified explanation of stereopsis, 3D figure-ground separation, and completion of partially occluded object surfaces. The model clarifies how interactions between layers 4, 3B, and 2/3A in V1 and V2 contribute to stereopsis, and proposes how a disparity filter and 3D perceptual grouping laws in V2 interact with 3D surface filling-in operations in V1, V2, and V4 to produce appropriate figure-ground perception. These interactions help to convert the complementary rules for boundary and surface formation (Grossberg, 1994) into a consistent, unitary visual percept.