Transparency and Occlusion

One of the great computational challenges in recovering scene structure from images arises from the fact that some surfaces in a scene are partially obscured by nearer surfaces or media. Both occluding and transparent surfaces interrupt the projection of more distant surfaces, and may be considered two ends of a continuum. Occluding surfaces completely obscure the surfaces that they occlude, whereas transparent surfaces only partially obscure the surfaces they overlay. The degree to which a transparent surface obscures an underlying surface depends on its transmittance (i.e., the proportion of light that it lets through of the underlying layer). Thus, when the transmittance is zero, the near surface is an opaque occluder; when it is greater than zero, some light of the underlying layer is transmitted. In order for the visual system to recover scene structure in contexts in which the transmittance of a near layer falls between zero and one, it must decompose the image into a “layered” representation that specifies the presence of multiple surfaces (or a surface and intervening media) along the same line of sight. This form of decomposition has been termed scission. In addition to the relationship between transparency and occlusion, the physical transformations induced by transparent surfaces are intimately related to the physical transformations that are caused by changes in illumination. This suggests that possibility that the decomposition that underlies the perception of transparency may also underlie the separation of illumination from surface reflectance, and hence, the computation of surface lightness and/or color. Thus, the topic of transparency is intimately related to two apparently distinct domains: the computation of occlusion relationships; and the computation of surface lightness. In this paper, I will describe some recent evidence that reveals the intimate relationship between scission and the perception of surface opacity, lightness, and depth, and the impact this research has on theoretical frameworks in vision more broadly.