SHAPE FROM POLARIZATION FOR FEATURELESS AND SPECULAR OBJECTS

Abstract

Objects with textureless and specular surfaces are commonplace in manmade environments. Their reconstruction challenges optical 3-D metrology methods typically yielding incomplete and noisy results. Existing solutions, including structured light, shape from shading, and learning-based specular removal methods, place a high demand on projection patterns, the number of light sources and cameras, or training data. While requiring elaborate setups, these solutions are not general enough, and their success in handling these surfaces is only partial. To address this reconstruction challenge, we study in this paper the application of shape from polarization for the 3-D reconstruction of textureless objects. Using a single view and a known light source, polarization-based constraints can be expressed as a set of linear equations for the unknown depth. We then estimate depth directly as an optimization problem constrained by the linear polarization equations. Results demonstrate complete and accurate 3-D reconstructions of typical glossy featureless objects, suggesting that shape from polarization is a valuable strategy for generating dense 3-D surface models.