Shadows in Three-Source Photometric Stereo

Abstract

Shadows are one of the most significant difficulties of the photometric stereo method. When four or more images are available, local surface orientation is overdetermined and the shadowed pixels can be discarded. In this paper we look at the challenging case when only three images under three different illuminations are available. In this case, when one of the three pixel intensity constraints is missing due to shadow, a 1 dof ambiguity per pixel arises. We show that using integrability one can resolve this ambiguity and use the remaining two constraints to reconstruct the geometry in the shadow regions. As the problem becomes ill-posed in the presence of noise, we describe a regularization scheme that improves the numerical performance of the algorithm while preserving data. We propose a simple MRF optimization scheme to identify and segment shadow regions in the image. Finally the paper describes how this theory applies in the framework of color photometric stereo where one is restricted to only three images. Experiments on synthetic and real image sequences are presented.

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