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

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 representingsurface shape. We characterized the surface features that affected performance in the classification task.