  Recovering the shape and reflectance of non-Lambertian surfaces remains a
challenging problem in computer vision since the view-dependent appearance
invalidates traditional photo-consistency constraint. In this paper, we
introduce a novel concentric multi-spectral light field (CMSLF) design that is
able to recover the shape and reflectance of surfaces with arbitrary material
in one shot. Our CMSLF system consists of an array of cameras arranged on
concentric circles where each ring captures a specific spectrum. Coupled with a
multi-spectral ring light, we are able to sample viewpoint and lighting
variations in a single shot via spectral multiplexing. We further show that
such concentric camera/light setting results in a unique pattern of specular
changes across views that enables robust depth estimation. We formulate a
physical-based reflectance model on CMSLF to estimate depth and multi-spectral
reflectance map without imposing any surface prior. Extensive synthetic and
real experiments show that our method outperforms state-of-the-art light
field-based techniques, especially in non-Lambertian scenes.
