  We present a novel framework to automatically learn to transform the
differential cues from a stack of images densely captured with a rotational
motion into spatially discriminative and view-invariant per-pixel features at
each view. These low-level features can be directly fed to any existing
multi-view stereo technique for enhanced 3D reconstruction. The lighting
condition during acquisition can also be jointly optimized in a differentiable
fashion. We sample from a dozen of pre-scanned objects with a wide variety of
geometry and reflectance to synthesize a large amount of high-quality training
data. The effectiveness of our features is demonstrated on a number of
challenging objects acquired with a lightstage, comparing favorably with
state-of-the-art techniques. Finally, we explore additional applications of
geometric detail visualization and computational stylization of complex
appearance.
