Shape and Illumination from Shading Using the Generic Viewpoint Assumption
Author(s)Zoran, Daniel; Krishnan, Dilip; Bento, Jose; Freeman, William T.
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The Generic Viewpoint Assumption (GVA) states that the position of the viewer or the light in a scene is not special. Thus, any estimated parameters from an observation should be stable under small perturbations such as object, viewpoint or light positions. The GVA has been analyzed and quantified in previous works, but has not been put to practical use in actual vision tasks. In this paper, we show how to utilize the GVA to estimate shape and illumination from a single shading image, without the use of other priors. We propose a novel linearized Spherical Harmonics (SH) shading model which enables us to obtain a computationally efficient form of the GVA term. Together with a data term, we build a model whose unknowns are shape and SH illumination. The model parameters are estimated using the Alternating Direction Method of Multipliers embedded in a multi-scale estimation framework. In this prior-free framework, we obtain competitive shape and illumination estimation results under a variety of models and lighting conditions, requiring fewer assumptions than competing methods.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Advances in Neural Information Processing Systems (NIPS)
Neural Information Processing Systems
Zoran, Daniel, Dilip Krishnan, Jose Bento, and Bill Freeman. "Shape and Illumination from Shading Using the Generic Viewpoint Assumption." Advances in Neural Information Processing Systems (NIPS 2014).
Final published version