  We present a novel framework to learn to convert the perpixel photometric
information at each view into spatially distinctive and view-invariant
low-level features, which can be plugged into existing multi-view stereo
pipeline for enhanced 3D reconstruction. Both the illumination conditions
during acquisition and the subsequent per-pixel feature transform can be
jointly optimized in a differentiable fashion. Our framework automatically
adapts to and makes efficient use of the geometric information available in
different forms of input data. High-quality 3D reconstructions of a variety of
challenging objects are demonstrated on the data captured with an illumination
multiplexing device, as well as a point light. Our results compare favorably
with state-of-the-art techniques.
