A systematic approach to testing and predicting light-material interactions

Fan Zhang, Huib de Ridder, Pascal Barla, Sylvia C. Pont

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
142 Downloads (Pure)

Abstract

Photographers and lighting designers set up lighting environments that best depict objects and human figures to convey key aspects of the visual appearance of various materials, following rules drawn from experience. Understanding which lighting environment is best adapted to convey which key aspects of materials is an important question in the field of human vision. The endless range of natural materials and lighting environments poses a major problem in this respect. Here we present a systematic approach to make this problem tractable for lighting–material interactions, using optics-based models composed of canonical lighting and material modes. In two psychophysical experiments, different groups of inexperienced observers judged the material qualities of the objects depicted in the stimulus images. In the first experiment, we took photographs of real objects as stimuli under canonical lightings. In a second experiment, we selected three generic natural lighting environments on the basis of their predicted lighting effects and made computer renderings of the objects. The selected natural lighting environments have characteristics similar to the canonical lightings, as computed using a spherical harmonic analysis. Results from the two experiments correlate strongly, showing (a) how canonical material and lighting modes associate with perceived material qualities; and (b) which lighting is best adapted to evoke perceived material qualities, such as softness, smoothness, and glossiness. Our results demonstrate that a system of canonical modes spanning the natural range of lighting and materials provides a good basis to study lighting–material interactions in their full natural ecology.

Original languageEnglish
Article number11
Pages (from-to)1-22
Number of pages22
JournalJournal of vision
Volume19
Issue number4
DOIs
Publication statusPublished - 2019

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