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Reflectance and Illumination Recovery in the Wild
Journal article   Open access   Peer reviewed

Reflectance and Illumination Recovery in the Wild

Stephen Lombardi and Ko Nishino
IEEE transactions on pattern analysis and machine intelligence, v 38(1), pp 129-141
01 Jan 2016
DOI: https://doi.org/10.1109/TPAMI.2015.2430318
PMID: 26656582
url
https://doi.org/10.1109/tpami.2015.2430318View
Accepted (AM)Open Access (Publisher-Specific),  Open

Abstract

Analytical models Brain modeling Computational modeling DSBRDF Estimation Image color analysis Joints Lighting natural illumination estimation real-world reflectance Reflectance estimation
The appearance of an object in an image encodes invaluable information about that object and the surrounding scene. Inferring object reflectance and scene illumination from an image would help us decode this information: reflectance can reveal important properties about the materials composing an object; the illumination can tell us, for instance, whether the scene is indoors or outdoors. Recovering reflectance and illumination from a single image in the real world, however, is a difficult task. Real scenes illuminate objects from every visible direction and real objects vary greatly in reflectance behavior. In addition, the image formation process introduces ambiguities, like color constancy, that make reversing the process ill-posed. To address this problem, we propose a Bayesian framework for joint reflectance and illumination inference in the real world. We develop a reflectance model and priors that precisely capture the space of real-world object reflectance and a flexible illumination model that can represent real-world illumination with priors that combat the deleterious effects of image formation. We analyze the performance of our approach on a set of synthetic data and demonstrate results on real-world scenes. These contributions enable reliable reflectance and illumination inference in the real world.

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86 citations in Web of Science
98 citations in Scopus

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Web of Science research areas
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
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