Estimating changes in lighting direction in binocularly viewed three-dimensional scenes

Abstract

We examine human ability to detect changes in scene lighting. Thirteen observers viewed three-dimensional rendered scenes stereoscopically. Each scene consisted of a randomly generated three-dimensional "Gaussian bump" surface rendered under a combination of collimated and diffuse light sources. During each trial, the collimated source underwent a small, quick change of position in one of four directions. The observer's task was to classify the direction of the lighting change. All observers were above chance in performing the task. We developed a model that combined two sources of information, a shape map and a shading map, to predict lighting change direction. We used this model to predict patterns of errors both across observers and across scenes differing in shape. We found that errors in estimating lighting direction were primarily the result of errors in representing surface shape. We characterized the surface features that affected performance in the classification task.

Estimating the Illumination Direction From Three-Dimensional Texture of Brownian Surfaces

“…Since we simultaneously infer higher dimensional material, shape, and illumination properties from two-dimensional images, it is to be expected that such inferences interact. Many studies have shown that it is indeed the case that material, shape, and illumination perception is confounded ( Gerhard & Maloney, 2010 ; Ho et al., 2008 ; Kim et al., 2014 ; Motoyoshi et al., 2007 ; Pont & te Pas, 2006 ; te Pas & Pont, 2005 ; Wijntjes, Doerschner, Kucukoglu, & Pont, 2012 ). Looking at our stimuli in the current study, see, for examples, Figure 2 , we saw that many of our stimuli did not look as being made of matte material.…”

“…Luminance distribution or histogram-based cues are, for instance, the width, average, and skewness of the luminance distribution ( Ho, Landy, & Maloney, 2008 ; Kim & Anderson, 2010 ; Motoyoshi, Nishida, Sharan, & Adelson, 2007 ; Pont & Koenderink, 2005, 2008 ; Wijntjes & Pont, 2010 ). In addition to such relatively easy derivable cues, the spatial properties of 3D textures also provide cues about the illumination, material, and shape ( Chantler, Schmidt, Petrou, & McGunnigle, 2002 ; Gerhard & Maloney, 2010 ; Karlsson, Pont, & Koenderink, 2008 , 2009 ; Kim, Marlow, & Anderson, 2014 ; Knill, 1990 ; Koenderink, 2012 ; Pont & Koenderink, 2008 ; Shepard & Campbell, 1998 ; Varma & Zisserman, 2004 ). The second-order statistics of shaded 3D textures provides us with estimates of the azimuth of the average illumination orientation, that is, the direction modulo 180° ( Koenderink & Pont, 2003 ; Koenderink, van Doorn, Kappers, te Pas, & Pont, 2003 ; Koenderink, van Doorn, & Pont, 2004 , 2007 ).…”

A toolbox for volumetric visualization of light properties

“…47,48 In visual perception studies, results showed that human observers are highly sensitive to the mean illuminance, diffuseness and average direction of light, not only on surfaces but also in empty space. 19,20,[49][50][51][52] Often, authors use different terms to describe those light properties, but it is usually clear that the authors mean the same quality. For example, diffuseness, 20 softness 45 and contrast 47 all seem to describe the same basic property of light, namely the ratio between the directed and ambient light.…”

Enhancement of Glossiness Perception by Retinal-Image Motion: Additional Effect of Head-Yoked Motion Parallax

“…It has been shown that we can distinguish the local reflectance change from a change in the position of the light source [46], [47]. If the effect of stimulus change as shown by Sakano and Ando [38] is the result of motion parallax and enhancement of the perception of the three-dimensional structure of the object, a stimulus change without any motion of the observer, i.e., visually simulating motion parallax in static viewing, would have a weaker effect than in dynamic viewing.…”