Article

3-D shape perception.

Purdue University, West Lafayette, IN 47907-1364, USA.
Perception & Psychophysics (Impact Factor: 2.22). 08/1995; 57(5):692-714.
Source: PubMed

ABSTRACT In this paper, we analyze and test three theories of 3-D shape perception: (1) Helmholtizian theory, which assumes that perception of the shape of an object involves reconstructing Euclidean structure of the object (up to size scaling) from the object's retinal image after taking into account the object's orientation relative to the observer, (2) Gibsonian theory, which assumes that shape perception involves invariants (projective or affine) computed directly from the object's retinal image, and (3) perspective invariants theory, which assumes that shape perception involves a new kind of invariants of perspective transformation. Predictions of these three theories were tested in four experiments. In the first experiment, we showed that reliable discrimination between a perspective and nonperspective image of a random polygon is possible even when information only about the contour of the image is present. In the second experiment, we showed that discrimination performance did not benefit from the presence of a textured surface, providing information about the 3-D orientation of the polygon, and that the subjects could not reliably discriminate between the 3-D orientation of textured surface and that of a shape. In the third experiment, we compared discrimination for solid shapes that either had flat contours (cuboids) or did not have visible flat contours (cylinders). The discrimination was very reliable in the case of cuboids but not in the case of cylinders. In the fourth experiment, we tested the effectiveness of planar motion in perception of distances and showed that the discrimination threshold was large and similar to thresholds when other cues to 3-D orientation were used. All these results support perspective invariants as a model of 3-D shape perception.

Download full-text

Full-text

Available from: Zygmunt Pizlo, Oct 13, 2014
0 Followers
 · 
59 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The process of surface perception is complex and based on several influencing factors, e.g., shading, silhouettes, occluding contours, and top down cognition. The accuracy of surface perception can be measured and the influencing factors can be modified in order to decrease the error in perception. This paper presents a novel concept of how a perceptual evaluation of a visualization technique can contribute to its redesign with the aim of improving the match between the distal and the proximal stimulus. During analysis of data from previous perceptual studies, we observed that the slant of 3D surfaces visualized on 2D screens is systematically underestimated. The visible trends in the error allowed us to create a statistical model of the perceived surface slant. Based on this statistical model we obtained from user experiments, we derived a new shading model that uses adjusted surface normals and aims to reduce the error in slant perception. The result is a shape-enhancement of visualization which is driven by an experimentally-founded statistical model. To assess the efficiency of the statistical shading model, we repeated the evaluation experiment and confirmed that the error in perception was decreased. Results of both user experiments are publicly-available datasets.
    IEEE Transactions on Visualization and Computer Graphics 12/2012; 18(12):2265-2274. DOI:10.1109/TVCG.2012.188 · 1.92 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A group model of mental transformations based on the geometric model of P. B. Yale (1968, Geometry and symmetry, Holden-Day, San Francisco) was constructed for form recognition. The model consisted of nine characteristic subgroups of the similarity group in Euclidean space. With these subgroups, six series were formed, representing six visual paths for form recognition. Each series involved five characteristic subgroups. Six subframes were associated with nine characteristic subgroups in the model. These subframes were shape (angle measure), the sense, size (volume), verticality, uprightness, and position. The model was validated by an experiment, using reaction time as the behavior index. Since shape is the common invariant property of all subgroups of the similarity group, angle measure was not included in ordering of subframes. The findings show that the preservation of uprightness of a form provides the best condition for form recognition, followed by the preservation of sense and verticality of a form. While the effect of position is not strong, size has the weakest influence on space form recognition. Copyright 1999 Academic Press.
    Journal of Mathematical Psychology 10/1999; 43(3):410-432. DOI:10.1006/jmps.1998.1230 · 1.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Human beings perceive 3D shapes veridically, but the underlying mechanisms remain unknown. The problem of producing veridical shape percepts is computationally difficult because the 3D shapes have to be recovered from 2D retinal images. This paper describes a new model, based on a regularization approach, that does this very well. It uses a new simplicity principle composed of four shape constraints: viz., symmetry, planarity, maximum compactness and minimum surface. Maximum compactness and minimum surface have never been used before. The model was tested with random symmetrical polyhedra. It recovered their 3D shapes from a single randomly-chosen 2D image. Neither learning, nor depth perception, was required. The effectiveness of the maximum compactness and the minimum surface constraints were measured by how well the aspect ratio of the 3D shapes was recovered. These constraints were effective; they recovered the aspect ratio of the 3D shapes very well. Aspect ratios recovered by the model were compared to aspect ratios adjusted by four human observers. They also adjusted aspect ratios very well. In those rare cases, in which the human observers showed large errors in adjusted aspect ratios, their errors were very similar to the errors made by the model.
    Vision research 08/2008; 49(9):979-91. DOI:10.1016/j.visres.2008.05.013 · 2.38 Impact Factor