  The problem of estimating a surface shape from its observed reflectance
properties still remains a challenging task in computer vision. The presence of
global illumination effects such as inter-reflections or cast shadows makes the
task particularly difficult for non-convex real-world surfaces.
State-of-the-art methods for calibrated photometric stereo address these issues
using convolutional neural networks (CNNs) that primarily aim to capture either
the spatial context among adjacent pixels or the photometric one formed by
illuminating a sample from adjacent directions.
  In this paper, we bridge these two objectives and introduce an efficient
fully-convolutional architecture that can leverage both spatial and photometric
context simultaneously. In contrast to existing approaches that rely on
standard 2D CNNs and regress directly to surface normals, we argue that using
separable 4D convolutions and regressing to 2D Gaussian heat-maps severely
reduces the size of the network and makes inference more efficient. Our
experimental results on a real-world photometric stereo benchmark show that the
proposed approach outperforms the existing methods both in efficiency and
accuracy.
