Shape and Pose Recovery of Solar-illuminated Surfaces from Compressive Spectral-polarimetric Image Data

We present simulation-based studies of the use of compressively sensed spectral-polarimetric spatial image data from a solar-illuminated reflecting surface to recover its material signature, three-dimensional (3D) shape, pose, and degree of surface roughness. The spatial variations of the polarimetric BRDF around glint points contain unique information about the shape and roughness of the reflecting surface that is revealed most dramatically in polarization-difference maps from which the spatially generalized diffuse-scattering contibutions to brightness are largely absent. Here we employ a specific compressed-sensing protocol, the so-called Coded-Aperture Snapshot Spectral Polarimetric Imager advanced recently by Tsai and Brady, to simulate noisy measurements from which these surface attributes are recovered in a sequential manner. Even in the presence of additive sensor noise, the recovery of these surface attributes for roughened, highly reflecting surfaces seems to be quite robust.