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Stephen Grossberg, Yongqiang Cao; A Laminar Cortical Model of Stereopsis and 3D Surface Perception: Closure and da Vinci Stereopsis. Journal of Vision 2004;4(8):599. https://doi.org/10.1167/4.8.599.
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© ARVO (1962-2015); The Authors (2016-present)
A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model further develops the 3D LAMINART model of Grossberg and Howe (Vision Research, 2003). It describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the lateral geniculate nucleus (LGN) and cortical areas V1, V2, and V4. In particular, it details how interactions between layers 4, 3B, and 2/3A in V1 and V2 contribute to stereopsis, and proposes how binocular and monocular information combine to form 3D boundary and surface representations. The current model includes two main new developments: (1) It clarifies that surface-to-boundary feedback is needed in stereopsis, and plays an indispensable role in some cases. Previous modeling has suggested that the complementary properties of boundary and surface computations can be rendered consistent by this feedback pathway, which is proposed to operate between V2 pale stripes and thin stripes, and that it also helps to explain data about 3D figure-ground separation. (2) It proposes that the disparity filter, which helps to solve the Correspondence Problem, can be realized as part of the inhibitory interactions that control perceptual grouping by horizontal connections in V2 layer 2/3. The model hereby combines suppression of false matches with long-range Gestalt grouping processes. The enhanced model explains all psychophysical data previously simulated by Grossberg and Howe (2003), such as contrast variations of dichoptic masking and the Correspondence Problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, and da Vinci stereopsis. In addition, it can explain psychophysical data such as the role of perceptual closure and variations of da Vinci stereopsis, which previous models cannot yet explain.
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