Binocular Shape Reconstruction: Psychological Plausibility of the 8-Point Algorithm

Chan, Moses W.; Pizlo, Zygmunt; Chelberg, David

doi:10.1006/cviu.1999.0748

Cited by 11 publications

(7 citation statements)

References 53 publications

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“…What is very difficult, however, is to emulate human performance in the reconstruction of the shapes of 3-D objects. Chan et al (1999) implemented a model of reconstructing 3-D distances and ratios of distances, using binocular disparity. Performance of this model was equally good or better than that of other models that do not use shape constraints.…”

Section: Resultsmentioning

confidence: 99%

“…The left and right images were computed using the same method as that described in Chan, Pizlo, and Chelberg (1999). The simulated viewing distance was 50 cm, and the simulated interocular distance was 6.5 cm.…”

Section: Methodsmentioning

confidence: 99%

“…Because reconstructing a 3-D space directly from binocular disparity is a difficult inverse problem, which is likely to lead to unstable results (see Chan et al, 1999), our model begins 3-D reconstruction by applying constraints to a 2-D image. The fact that monocular reconstruction of 3-D shapes with the use of simplicity constraints may lead to reliable results has been demonstrated by Marill (1991) and by Leclerc and Fischler (1992).…”

Section: Computational Model Of Binocular Shape Reconstructionmentioning

confidence: 99%

“…The means and standard deviations were computed without the outliers. Table 1 shows the means, standard deviations, and the numbers of outliers of r for the shape hypotheses, corrected shapes, and final shapes obtained with our new model (which uses constraints) and for the shapes obtained with the algorithm of Chan et al (1999). Chan et al's algorithm is the best that we identified from those that use visual data only (no constraints on the family of possible solutions).…”

Section: Simulation Experimentsmentioning

confidence: 99%

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Binocular shape constancy from novel views: The role of a priori constraints

Chan

Stevenson

et al. 2006

Perception & Psychophysics

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Shape constancy refers to the fact that the percept of the shape of a given object remains constant despite changes in the shape of the retinal image. The retinal image may change because of changes in the orientation of the object, relative to the observer. Shape is usually defined by angles and by ratios of distances. According to this definition, rigid motions and size scaling do not change shape. In other words, shape is invariant under similarity transformations. A perspective transformation between the 3-D scene and the 2-D retina is quite different from a similarity transformation (Pizlo, Rosenfeld, & Weiss, 1997). First of all, it is a many-to-one mapping. As a result, the information about depth is lost, and the shape on the retina may change substantially when the orientation of the object, relative to the observer, changes. It follows that shape constancy is a difficult computational problem. In the case of familiar objects, shape constancy could, theoretically, be achieved by means of a simple template-matching mechanism, in which the current view is compared with all previously seen views of the object. In order to provide an adequate test between this and other mechanisms, one should use unfamiliar objects and test shape constancy from unfamiliar (novel) views. Rock and DiVita (1987) performed the first systematic study of shape constancy from novel views, using 3-D objects. They used unstructured 3-D wire objects, which were viewed binocularly from a close viewing distance. Rock and DiVita reported a complete failure of shape constancy in the presence of a difference of only 45º between the views of an object. The authors concluded that the shapes of 3-D objects are perceived (and recognized) by memorizing a large number of 2-D images taken from many different viewing directions-the template matching mentioned in the first paragraph. In a subsequent study, Rock, Wheeler, and Tudor (1989) provided additional evidence by showing that observers cannot imagine how a novel wire object looks from a new viewing direction. The results of these two studies strongly suggest that the perceptual representation of objects does not involve 3-D properties. Biederman and Gerhardstein (1993) reported a quite different result. They used line drawings of unfamiliar 3-D geometrical objects built from geons and demonstrated almost perfect shape constancy from novel views (geons are simple 3-D objects, such as a box, cone, cylinder, or pyramid; Biederman, 1987). This result is, actually, hardly surprising because, phenomenologically, it is easy to imagine how novel or familiar objects built from geons (and many other structured objects) look from new viewing directions. In other words, Biederman and Gerhardstein's study provided strong support for the claim We tested shape constancy from novel views in the case of binocular viewing, using a variety of stimuli, including polyhedra, polygonal lines, and points in 3-D. The results of the psychophysical experiments show that constraints such as planarity of surface contour...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Computational Model Of Binocular Shape Reconstructionmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

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Binocular shape constancy from novel views: The role of a priori constraints

Chan

Stevenson

et al. 2006

Perception & Psychophysics

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“…If a single perspective image of a symmetric shape is given, a unique reconstruction is possible, in principle, even without constraints. However, the reconstruction is computationally unstable in the presence of noise, for the same reason that binocular reconstruction is unstable [16] . Therefore, compactness constraint has to be used with perspective images, as well.…”

Section: Summary and Discussionmentioning

confidence: 99%

Reconstruction of shapes of 3D symmetric objects by using planarity and compactness constraints

Pizlo

2007

SPIE Proceedings

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We present a new algorithm for reconstructing 3D shapes. The algorithm takes one 2D image of a 3D shape and reconstructs the 3D shape by applying a priori constraints: symmetry, planarity and compactness. The shape is reconstructed without using information about the surfaces, such as shading, texture, binocular disparity or motion. Performance of the algorithm is illustrated on symmetric polyhedra, but the algorithm can be applied to a very wide range of shapes. Psychophysical plausibility of the algorithm is discussed.

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Fast Distance Computation with a Stereo Head-Eye System

Park¹,

Lee²

2000

Biologically Motivated Computer Vision

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Binocular Shape Reconstruction: Psychological Plausibility of the 8-Point Algorithm

Cited by 11 publications

References 53 publications

Binocular shape constancy from novel views: The role of a priori constraints

Binocular shape constancy from novel views: The role of a priori constraints

Reconstruction of shapes of 3D symmetric objects by using planarity and compactness constraints

Fast Distance Computation with a Stereo Head-Eye System

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