  Recovering the 3D geometry of a purely texture-less object with generally
unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging
task in multi-view reconstruction. The major obstacle revolves around
establishing cross-view correspondences where photometric constancy is
violated. This paper proposes a simple and practical solution to overcome this
challenge based on a co-located camera-light scanner device. Unlike existing
solutions, we do not explicitly solve for correspondence. Instead, we argue the
problem is generally well-posed by multi-view geometrical and photometric
constraints, and can be solved from a small number of input views. We formulate
the reconstruction task as a joint energy minimization over the surface
geometry and reflectance. Despite this energy is highly non-convex, we develop
an optimization algorithm that robustly recovers globally optimal shape and
reflectance even from a random initialization. Extensive experiments on both
simulated and real data have validated our method, and possible future
extensions are discussed.
