Although a lot of research has been performed in the field of reconstructing 3D shape from the shading in an image, only a small portion of this work has examined the association of local shading patterns over image patches with the underlying 3D geometry. Such approaches are a promising way to tackle the ambiguities inherent in the shape-from-shading (SfS) problem, but issues such as their sensitivity to non-lambertian reflectance or photometric calibration have reduced their real-world applicability. In this paper we show how the information in local shading patterns can be utilized in a practical approach applicable to real-world images, obtaining results that improve the state of the art in the SfS problem. Our approach is based on learning a set of geometric primitives, and the distribution of local shading patterns that each such primitive may produce under different reflectance parameters. The resulting dictionary of primitives is used to produce a set of hypotheses about 3D shape; these hypotheses are combined in a Markov Random Field (MRF) model to determine the final 3D shape.
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