  Retrieving accurate 3D reconstructions of objects from the way they reflect
light is a very challenging task in computer vision. Despite more than four
decades since the definition of the Photometric Stereo problem, most of the
literature has had limited success when global illumination effects such as
cast shadows, self-reflections and ambient light come into play, especially for
specular surfaces.
  Recent approaches have leveraged the power of deep learning in conjunction
with computer graphics in order to cope with the need of a vast number of
training data in order to invert the image irradiance equation and retrieve the
geometry of the object. However, rendering global illumination effects is a
slow process which can limit the amount of training data that can be generated.
  In this work we propose a novel pixel-wise training procedure for normal
prediction by replacing the training data (observation maps) of globally
rendered images with independent per-pixel generated data. We show that global
physical effects can be approximated on the observation map domain and this
simplifies and speeds up the data creation procedure.
  Our network, PX-NET, achieves the state-of-the-art performance compared to
other pixelwise methods on synthetic datasets, as well as the Diligent real
dataset on both dense and sparse light settings.
