The role of natural lighting diffuseness in human visual perception

Yaniv Morgenstern, Wilson S. Geisler, Richard F. Murray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The pattern of the light that falls on the retina is a conflation of real-world sources such as illumination and reflectance. Human observers often contend with the inherent ambiguity of the underlying sources by making assumptions about what real-world sources are most likely. Here we examine whether the visual system's assumptions about illumination match the statistical regularities of the real world. We used a custom-built multidirectional photometer to capture lighting relevant to the shading of Lambertian surfaces in hundreds of real-world scenes. We quantify the diffuseness of these lighting measurements, and compare them to previous biases in human visual perception. We find that (1) natural lighting diffuseness falls over the same range as previous psychophysical estimates of the visual system's assumptions on diffuseness, and (2) natural lighting almost always provides lighting direction cues that are strong enough to override the human visual system's well known assumption that light tends to come from above. A consequence of these findings is that what seem to be errors in visual perception are often actually byproducts of the visual system knowing about and using reliable properties of real-world lighting when contending with ambiguous retinal images.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XX
EditorsBernice E. Rogowitz, Thrasyvoulos N. Pappas, Huib de Ridder
ISBN (Electronic)9781628414844
StatePublished - 2015
EventHuman Vision and Electronic Imaging XX - San Francisco, United States
Duration: Feb 9 2015Feb 12 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherHuman Vision and Electronic Imaging XX
Country/TerritoryUnited States
CitySan Francisco


  • Bayesian modeling
  • Illumination statistics
  • Inverse optics
  • Lightness perception


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