Shape from shaded random surfaces

Abstract

The perception of surface relief from random shading patterns is measured by having observers adjust three-dimensional local probes, the projections of which are superimposed on the image. Three observers perform four settings of 91 probes on each of 14 images. These images are generated by calculating the Lambertian reflectance of a random superposition of elliptical Gaussian hills and valleys illuminated by a single distant light source as well as by ambient light. Neither the surface reflectance equation nor the light source direction is conveyed to our observers in any way. Mathematically, this "pure" shape-from-shading problem has highly non-unique solutions. Perception of a well-defined, stable shape therefore implies that the ambiguity is resolved, i.e. a gauge is fixed. We analyse the surface ambiguity or gauge freedom which is left unconstrained by pure shading information and we investigate possible ways of restricting it. Statistical analysis of the curl component of the field of probe settings reveals that the settings are significantly consistent with an underlying perceived surface. In spite of the large theoretical ambiguity in the stimuli, the settings are reproducible and show considerable inter-observer agreement. Even the correlation of the settings with the real surfaces is surprisingly large. If the settings are compared to the real surface normals, one finds a series of biases, the strongest of which is that the global surface slant is systematically underestimated, even in those cases where ending occluding contours or high-contrast luminance ridges, indicative of "almost" contours, are present in the image. Another bias then is that the corresponding rims on the surface are seen as roughly parallel to the picture plane.

Frequency-based heuristics for material perception

“…In images of materials, the 3-D structure of a surface is mainly conveyed by shape from shading. Many algorithms for the recovery of 3-D shape from shading cues have been proposed in the computational literature (Breton & Zucker, 1996;Horn, 1970: Ikeuchi & Horn, 1981Pentland, 1982), and a few studies have investigated human performance in recovery of object shape (De Haan, Erens, & Noest, 1995;Erens, Kappers, & Koenderink, 1993), but none have studied Giesel, M., & Zaidi, Q. (2013).…”

A Perceptual-Statistics Shading Model

“…There is an evidence that the pictorial relief, i.e., the imaginary relief extracted from a 2D projection of a 3D scene, such as a rendering or a photograph, is systematically distorted relative to the actual structure of the observed scene [7,34]. The variations among observers' judgments were revealed to be complex and thus could not be accounted for by a simple depth scaling transformation.…”

“…Cole et al showed that certain shape cues can be extracted solely from important lines, even though shape cues from shaded images are more accurate [6]. However, shading alone cannot yield the depth structure of a scene correctly [7]. The depth cues from shading are poor when compared to the retinal disparity (stereopsis) and kinetic cues [14].…”

“…This allows for the possibility to loop between the rendering stage, evaluation and improvement. In this paper, we present how this concept can be used to improve the perception of surface slant in visualizations viewed on monoscopic screens, which is systematically underestimated [7,34]. [6].…”

Visual shape perception in the case of transparent objects

“…(c) Relative proportion of the accuracy variance in the total variance for each stimulus condition. Noest, 1995;Koenderink & van Doorn, 1992;Todd, Oomes, Koenderink, & Kappers, 2004). To test for this effect and further potential differences between the stimulus conditions, we reparametrized the normals indicated by the gauge figure in terms of spherical slant and tilt coordinates (see Figure 14a).…”