  Reflectance Transformation Imaging (RTI) is a popular technique that allows
the recovery of per-pixel reflectance information by capturing an object under
different light conditions. This can be later used to reveal surface details
and interactively relight the subject. Such process, however, typically
requires dedicated hardware setups to recover the light direction from multiple
locations, making the process tedious when performed outside the lab.
  We propose a novel RTI method that can be carried out by recording videos
with two ordinary smartphones. The flash led-light of one device is used to
illuminate the subject while the other captures the reflectance. Since the led
is mounted close to the camera lenses, we can infer the light direction for
thousands of images by freely moving the illuminating device while observing a
fiducial marker surrounding the subject. To deal with such amount of data, we
propose a neural relighting model that reconstructs object appearance for
arbitrary light directions from extremely compact reflectance distribution data
compressed via Principal Components Analysis (PCA). Experiments shows that the
proposed technique can be easily performed on the field with a resulting RTI
model that can outperform state-of-the-art approaches involving dedicated
hardware setups.
