  Reconstructing the 3D shape of an object using several images under different
light sources is a very challenging task, especially when realistic assumptions
such as light propagation and attenuation, perspective viewing geometry and
specular light reflection are considered. Many of works tackling Photometric
Stereo (PS) problems often relax most of the aforementioned assumptions.
Especially they ignore specular reflection and global illumination effects. In
this work, we propose the first CNN based approach capable of handling these
realistic assumptions in Photometric Stereo. We leverage recent improvements of
deep neural networks for far-field Photometric Stereo and adapt them to near
field setup. We achieve this by employing an iterative procedure for shape
estimation which has two main steps. Firstly we train a per-pixel CNN to
predict surface normals from reflectance samples. Secondly, we compute the
depth by integrating the normal field in order to iteratively estimate light
directions and attenuation which is used to compensate the input images to
compute reflectance samples for the next iteration. To the best of our
knowledge this is the first near-field framework which is able to accurately
predict 3D shape from highly specular objects. Our method outperforms competing
state-of-the-art near-field Photometric Stereo approaches on both synthetic and
real experiments.
