Close-Range Photometric Stereo with Point Light Sources

Shape recovery based on shading variations of a lighted object was recently revisited with improvements that allow for the photometric stereo approach to serve as a competitive alternative for other shape reconstruction methods. However, most efforts of using photometric stereo tend to ignore some factors that are relevant in practical applications. The approach we consider tackles the photometric stereo reconstruction in the case of near-field imaging which means that both camera and light sources are close to the imaged object. The known challenges that characterize the problem involve perspective viewing geometry, attenuation of light and possibly missing regions. Here, we pay special attention to the question of how to faithfully model these aspects and by the same token design an efficient and robust numerical solver. We present a well-posed mathematical representation that integrates the above assumptions into a single coherent model. The surface reconstruction in our near-field scenario can then be executed efficiently in linear time. The merging strategy of the irradiance equations provided for each light source allows us to consider a characteristic expansion model which enables the direct computation of the surface. We evaluate several types of light attenuation models with nonuniform albedo and noise on synthetic data using four virtual sources. We also demonstrate the proposed method on surface reconstruction of real data using three images, each one taken with a different light source.