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Shape and Illumination from Shading using the Generic Viewpoint Assumption
Daniel Zoran · Dilip Krishnan · José Bento · Bill Freeman

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

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.

Author Information

Daniel Zoran (Google DeepMind)
Dilip Krishnan (Massachusetts Institute of Technology)
José Bento (Boston College)
Bill Freeman (MIT/Google)

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