Computational Approaches to Shape Constancy

The appearance of a three-dimensional object (that is, the pattern formed by its projection onto the retina of an eye or onto the imaging plane of a camera) depends on the point of view of the observer. The collective human awareness of this dependence is attested to by the widespread use of expressions that involve the metaphor of point of view, in languages as different as English, Russian, and Hebrew. Nevertheless, as far as recognition is concerned, the matters of viewpoint seem to be of secondary importance: the human visual system exhibits an impressive ability to recognize a familiar object viewed from an unfamiliar perspective. This phenomenon has been termed shape constancy, by analogy with other perceptual constancies. Computational understanding of shape constancy can be gained both by attempting to build artificial vision systems for object recognition, and by modeling human performance in this task. Maintaining a constant interpretation of the three-dimensional world in the face of changing viewing conditions has long been a major goal of computer vision. The first part of this chapter classifies and reviews several approaches to 3D object recognition developed within this field. In the second part of the chapter, we list the central characteristics of shape constancy in human vision, and compare the virtues and the shortcomings of the computational approaches, this time considered as models of human performance. We conclude with a general discussion of the phenomenon of shape constancy within the framework of the computational study of perception