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

Abstract

We studied whether human observers can estimate the illumination direction from 3D textures of random Brownian surfaces, containing undulations over a range of scales. The locally Lambertian surfaces were illuminated with a collimated beam from random directions. The surfaces had a uniform albedo and thus texture appeared only through shading and shadowing. The data confirm earlier results with Gaussian surfaces, containing undulations of a single scale. Observers were able to accurately estimate the source azimuth. If shading dominated the images, the observers committed 180° errors. If cast shadows were present, they resolved this convex-concave-ambiguity almost completely. Thus, observers relied on second-order statistics in the shading regime and used an unidentified first-order cue in the shadow regime. The source elevations could also be estimated, which can be explained by the observers’ exploitation of the statistical homogeneity of the stimulus set. The fraction of the surface that is in shadow and the median intensity are likely cues for these elevation estimates.

Illumination discrimination in the absence of a fixed surface-reflectance layout

“…Illumination perception has been studied both directly, using tasks in which observers make explicit judgments about the illumination, and indirectly, in paradigms where inferences about the perceptual representation are made on the basis of measurements of other stimulus attributes. Studies involving direct judgments have probed the ability to perceive spatial characteristics of the illumination, including direction (Pont, van Doorn, & Koenderink, 2017 ; Xia, Pont, & Heynderickx, 2016 ), diffuseness (Morgenstern, Geisler, & Murray, 2014 ), and perceived distribution of illumination in space (“the visual light field”, Kartashova, Sekulovski, de Ridder, te Pas, & Pont, 2016 ; Koenderink, Pont, van Doorn, Kappers, & Todd, 2007 ; Xia, Pont, & Heynderickx, 2014 ; see also Schirillo, 2013 ). In other studies using direct assessments, observers were asked to make explicit asymmetric matches of illumination levels or directions (Khang, Koenderink, & Kappers, 2006 ; Rutherford & Brainard, 2002 ; see also Logvinenko & Menshikova, 1994 ).…”

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

“…On the assumption of diffuse lighting, surface peaks and troughs align with light and dark image regions, leading to the perception that “dark is deep” ( Cooper & Norcia, 2014 ; Hibbard et al, 2023 ; Langer & Bülthoff, 2000 ; Langer & Zucker, 1994 ; Potetz & Lee, 2003 ; Schofield, Rock, & Georgeson, 2011 ; Sun & Schofield, 2012 ). On the assumption of punctate lighting, a single-point light source means luminance peaks are perceived as surfaces facing that light source, such as a hedge with a highlight on the side facing the sun ( Adams, Graf, & Ernst, 2004 ; Berbaum, Bever, & Chung, 1983 ; Brewster, 1826 ; Koenderink, van Doorn, Kappers, te Pas, & Pont, 2003 ; Mamassian & Goutcher, 2001 ; Pont, van Doorn, & Koenderink, 2017 ; Ramachandran, 1988 ; Rittenhouse, 1786 ; Schofield, Rock, & Georgeson, 2011 ; Stone, Kerrigan, & Porrill, 2009 ; Sun & Perona, 1998 ; Sun & Schofield, 2012 ). These assumptions invoke subtly different relationships between luminance and shape.…”