Reflectance and Natural Illumination from a Single Image
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Estimating reflectance and natural illumination from a single image of an object of known shape is a challenging task due to the ambiguities between reflectance and illumination. Although there is an inherent limitation in what can be recovered as the reflectance band-limits the illumination, explicitly estimating both is desirable for many computer vision applications. Achieving this estimation requires that we derive and impose strong constraints on both variables. We introduce a probabilistic formulation that seamlessly incorporates such constraints as priors to arrive at the maximum a posteriori estimates of reflectance and natural illumination. We begin by showing that reflectance modulates the natural illumination in a way that increases its entropy. Based on this observation, we impose a prior on the illumination that favors lower entropy while conforming to natural image statistics. We also impose a prior on the reflectance based on the directional statistics BRDF model that constrains the estimate to lie within the bounds and variability of real-world materials. Experimental results on a number of synthetic and real images show that the method is able to achieve accurate joint estimation for different combinations of materials and lighting.
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About this Chapter
- Reflectance and Natural Illumination from a Single Image
- Book Title
- Computer Vision – ECCV 2012
- Book Subtitle
- 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI
- pp 582-595
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 16. Microsoft Research Ltd.
- 17. Dept. of Computer Science, University of North Carolina
- 18. California Institute of Technology
- 19. Institute of Industrial Science, The University of Tokyo
- 20. INRIA
- Author Affiliations
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