  This paper solves the Sparse Photometric stereo through Lighting
Interpolation and Normal Estimation using a generative Network (SPLINE-Net).
SPLINE-Net contains a lighting interpolation network to generate dense lighting
observations given a sparse set of lights as inputs followed by a normal
estimation network to estimate surface normals. Both networks are jointly
constrained by the proposed symmetric and asymmetric loss functions to enforce
isotropic constrain and perform outlier rejection of global illumination
effects. SPLINE-Net is verified to outperform existing methods for photometric
stereo of general BRDFs by using only ten images of different lights instead of
using nearly one hundred images.
