Abstract not found

The accuracy of depth judgments that are based on binocular disparity or structure from motion (motion parallax and object rotation) was studied in 3 experiments. In Experiment 1, depth judgments were recorded for computer simulations of cones specified by binocular disparity, motion parallax, or stereokinesis. In Experiment 2, judgments were recorded for real cones in a structured environment, with depth information from binocular disparity, motion parallax, or object rotation about the y-axis. In both of these experiments, judgments from binocular disparity information were quite accurate, but judgments on the basis of geometrically equivalent or more robust motion information reflected poor recovery of quantitative depth information. A 3rd experiment demonstrated stereoscopic depth constancy for distances of 1 to 3 m using real objects in a well-illuminated, structured viewing environment in which monocular depth cues (e.g., shading) were minimized. It has been pointed out that the geometric information supporting the perception of depth from binocular disparity is actually less determinate than that supporting the recovery of structure from object rotation or motion parallax

We are surrounded by surfaces that we perceive by visual means. Understanding the basic principles behind this perceptual process is a central theme in visual psychology, psychophysics and computational vision. In many of the computational models employed in the past, it has been assumed that a metric representation of physical space can be derived by visual means. Psychophysical experiments, as well as computational considerations, can convince us that the perception of space and shape has a much more complicated nature, and that only a distorted version of actual, physical space can be computed. This paper develops a computational geometric model that explains why such distortion might take place. The basic idea is that, both in stereo and motion, we perceive the world from multiple views. Given the rigid transformation between the views and the properties of the image correspondence, the depth of the scene can be obtained. Even a slight error in the rigid transformation parameters c...

A stereo vision system requires accurate knowledge of the internal parameters of its two cameras and of the relative orientation between them in order to faithfully reconstruct a scene from corresponding image points. When incorrect values for the parameters of the stereo system are used, the resulting reconstruction is distorted in a systematic way. The transformation between the true scene structure and its distorted reconstruction, which we call the shape distortion transformation, is a quadratic Cremona transformation --- a rational transformation that is one-to-one almost everywhere, but that does not, in general, preserve collinearity. Our distortion framework describes and enables the analysis of situations where insufficient information is available to compute even a weak calibration of a stereo rig. It also makes possible a thorough error analysis of systematic errors in computing structure from motion. The global, qualitative, behavior of the shape distortion is studied by ...

Most people would agree that the shape of an object is one of its most perceptually important attributes, and some researchers have argued that it is the primary attribute by which observers are able to recognize objects �e.g., Biederman, 1987). Given the ubiquity of this common intuition, it is somewhat puzzling to note that the concept of ``shape' ' has no formal mathematical definition that can adequately characterize its intended meaning when used colloquially. For example, almost everyone would concur that a big sphere and a small sphere both have the same shape, yet by most of the standard measures used in geometry they are quite different. The abstract nature of the concept of shape is perhaps best revealed by the perceptual classification of biological forms �e.g., see Thompson, 1942). Consider, for example, the ability of normal individuals to identify their friends and loved ones under a variety of different viewing conditions. We are able to identify people from different vantage points, and with different facial expressions, hairstyles, make-up, or clothing accessories, such as hats or jewellery. We

How the eye measures reality and virtual reality We, as a species, seem to have been fascinated with pictures throughout our history. The paintings at Niaux, Altamira, and Lascaux (Clottes, 1995; Ruspoli, 1986), for example, are known to be about 14,000 years old, but with the recently discovered paintings in the Grotte Chauvet, the origin of representational art appears to have been pushed back even further (Chauvet, Brunel Deschamps, & Hillaire, 1995; Clottes, 1996), to 20,000 years ago if not longer. 1 Thus, these paintings date from about the time at which homo sapiens sapiens first appeared in Europe (Nougier, 1969). We should remember these paintings in the context of virtual reality; our fascination with pictures is by no means recent. My intent is threefold: first, to discuss our perception of the cluttered layout, or space, that we normally find around us; second, to discuss the development of representational art up to our current appreciation of it; and third, to apply this knowledge to virtual reality systems. The first discussion focuses on the use of multiple sources of information specifying ordinal depth relations, within the theoretical framework that I have called directed perception

Abstract not found

We examined the influence of context on exocentric pointing. In a virtual three-dimensional set-up, we asked our subjects to aim a pointer toward a target in two conditions: The target and the pointer were visible alone, or they were visible with planes through each of them. The planes consisted of a regular grid of horizontal and vertical lines. The presence of the planes had a significant influence on the indicated direction. These changes in indicated direction depended systematically on the orientation of the planes relative to the subject and on the angle between the planes. When the orientation of the (perpendicular) planes varied from asymmetrical to symmetrical to the frontoparallel plane, the indicated direction varied over a range of 15º—from a slightly larger slant to a smaller slant—as compared with the condition without the contextual planes. When the dihedral angle between the two planes varied from 90º to 40º, the indicated direction varied over a range of less than 5º: A smaller angle led to a slightly larger slant. The standard deviations in the indicated directions (about 3º) did not change systematically. The additional structure provided by the planes did not lead to more consistent pointing. The systematic changes in the indicated direction contradict all theories that assume that the perceived distance between any two given points is independent of whatever else is present in the visual field—that is, they contradict all theories of visual space that assume that its geometry is independent

The three-dimensional space around us is conveniently and reasonably Euclidean. Can we assume our perception of this space, and of objects in it, would follow suit? This assumption is quite natural, but empirical results suggest that it is also quite wrong, except in narrow circumstances. Perceptual space grades from being nearly Euclidean within a meter of our eyes to being affine and foreshortened at increasing distance, although considerable variation occurs across task, environments, and individuals. Compression with distance is steeper than one modeled by an exponent. Since pictures are most typically composed with distant content, similar perceived distortions should and do occur in pictorial, particularly photographic, space. One can think of perceived space—even at is articulated, near-Euclidean best—as built up incrementally from constraints of ordinality. These constraints, when sufficiently rich, converge on a near-Euclidean framework. How do we perceive the space in pictures? In answering this question theorists typically consider standard photographs and other representational images, such as architectural drawings, engravings, and paintings in linear perspective. Adding motion augments this