Abstract. This paper addresses the problem of photometric stereo for
non-Lambertian surfaces. Existing approaches often adopt simplified re-
flectance models to make the problem more tractable, but this greatly
hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an
arbitrary number of images of a static object captured under different
light directions with a fixed camera as input, and predicts a normal map
of the object in a fast feed-forward pass. Unlike the recently proposed
learning based method, PS-FCN does not require a pre-defined set of
light directions during training and testing, and can handle multiple
images and light directions in an order-agnostic manner. Although we
train PS-FCN on synthetic data, it can generalize well on real datasets.
We further show that PS-FCN can be easily extended to handle the
problem of uncalibrated photometric stereo. Extensive experiments on
public real datasets show that PS-FCN outperforms existing approaches
in calibrated photometric stereo, and promising results are achieved in
uncalibrated scenario, clearly demonstrating its effectiveness.