Visible surfaces of three-dimensional objects are reconstructed from two-dimensional retinal images in the early stages of human visual processing. In the computational model of surface reconstruction based on the standard regularization theory, an energy function is minimized. Two types of model have been proposed, called "membrane" and "thin-plate" after their function formulas, in which the first or the second derivative of depth information is used. In this study, the threshold of surface reconstruction from binocular disparity was investigated using a sparse random dot stereogram, and the predictive accuracy of these models was evaluated. It was found that the thin-plate model reconstructed surfaces more accurately than the membrane model and showed good agreement with experimental results. The likelihood that these models imitate human processing of visual information is discussed in terms of the size of receptive fields in the visual pathways of the human cortex.

No abstract

Binocular disparity and motion parallax are particularly important for our 3D surface perception, and the properties of perceived surfaces are similar when these cues are presented separately. Moreover, when we perceive surfaces on random dot patterns, we can perceive smooth surfaces even though the dot density may be low. That is, we reconstruct a surface by interpolating areas between dots. In this research, we used random dot patterns in which we can perceive 3D surfaces by disparity or parallax. We made a region without dots (a gap) on the surface, and investigated the surface properties perceived. The experimental results were then compared with output of a computational model based on the standard regularization theory. We conclude as follows; (1) The ability for surface reconstruction due to binocular disparity is more effective than that by parallax. (2) The experimental data corresponded well to a model which uses the second order differential of depth data. This indicates the usage of the curvature of the disparity or parallax in the surface perception. (3) The gap width difference between disparity and parallax can be explained by considering the magnitude of the receptive fields in the neural pathways where disparity and parallax are processed.

No abstract

:In the present study, the subjective evaluation experiment for abstract 3D shapes was conducted as the first step of construction of a system that allows non-design-professional users to design 3D shapes via subjective evaluation words intuitively. In particular, the structure of the subjective evaluation for 3D shapes and the reliability of the evaluation by non-design-professionals were examined. The results showed three-factor solution in which "Uniformity," "Activity" and "Potency" factors were important evaluative standards in the subjective evaluation for 3D shapes. These factors are similar to Osgood's three factors, which were frequently reported factors in previous semantic differential studies. Furthermore, the subjective evaluations by non-designprofessionals were generally consistent, which means that, with respect to the subjective evaluation, non-design-professionals without high level of design skills can evaluate 3D shapes in a reliable way.