Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A signal-processing framework for inverse rendering
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Image-based reconstruction of spatial appearance and geometric detail
ACM Transactions on Graphics (TOG)
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
Reflectance Sharing: Predicting Appearance from a Sparse Set of Images of a Known Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixture of Spherical Distributions for Single-View Relighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Entropy Minimization for Shadow Removal
International Journal of Computer Vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Single image multimaterial estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
What an image reveals about material reflectance
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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.