  We propose a novel framework to automatically learn to aggregate and
transform photometric measurements from multiple unstructured views into
spatially distinctive and view-invariant low-level features, which are fed to a
multi-view stereo method to enhance 3D reconstruction. The illumination
conditions during acquisition and the feature transform are jointly trained on
a large amount of synthetic data. We further build a system to reconstruct the
geometry and anisotropic reflectance of a variety of challenging objects from
hand-held scans. The effectiveness of the system is demonstrated with a
lightweight prototype, consisting of a camera and an array of LEDs, as well as
an off-the-shelf tablet. Our results are validated against reconstructions from
a professional 3D scanner and photographs, and compare favorably with
state-of-the-art techniques.
