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Abstract

Many materials including water, plastic and metal have specular surface characteristics. Specular reflections have commonly been considered a nuisance for the recovery of object shape. However, the way that reflections are distorted across the surface depends crucially on 3D curvature, suggesting that they could in fact be a useful source of information. Indeed, observers can have a vivid impression of 3D shape when an object is perfectly mirrored (i.e. the image contains nothing but specular reflections). This leads to the question what are the underlying mechanisms of our visual system to extract this 3D shape information from a perfectly mirrored object. In this paper we propose a biologically motivated recurrent model for the extraction of visual features relevant for the perception of 3D shape information from images of mirrored objects. We analyze qualitatively and quantitatively the results of computational model simulations and show that bidirectional recurrent information processing leads to better r
esults then pure feedforward processing. Furthermore we utilize the model output to create a rough non-photorealistic sketch representation of a mirrored object, which emphasizes image features that are mandatory for 3D shape perception (e.g. occluding contour, regions of high curvature). Moreover, this sketch illustrates that the model generates a representation of object features independent of the surrounding scene reflected in the mirrored object.