  The photometric stereo (PS) problem consists in reconstructing the 3D-surface
of an object, thanks to a set of photographs taken under different lighting
directions. In this paper, we propose a multi-scale architecture for PS which,
combined with a new dataset, yields state-of-the-art results. Our proposed
architecture is flexible: it permits to consider a variable number of images as
well as variable image size without loss of performance. In addition, we define
a set of constraints to allow the generation of a relevant synthetic dataset to
train convolutional neural networks for the PS problem. Our proposed dataset is
much larger than pre-existing ones, and contains many objects with challenging
materials having anisotropic reflectance (e.g. metals, glass). We show on
publicly available benchmarks that the combination of both these contributions
drastically improves the accuracy of the estimated normal field, in comparison
with previous state-of-the-art methods.
