  Uncalibrated photometric stereo is proposed to estimate the detailed surface
normal from images under varying and unknown lightings. Recently, deep learning
brings powerful data priors to this underdetermined problem. This paper
presents a new method for deep uncalibrated photometric stereo, which
efficiently utilizes the inter-image representation to guide the normal
estimation. Previous methods use optimization-based neural inverse rendering or
a single size-independent pooling layer to deal with multiple inputs, which are
inefficient for utilizing information among input images. Given multi-images
under different lighting, we consider the intra-image and inter-image
variations highly correlated. Motivated by the correlated variations, we
designed an inter-intra image feature fusion module to introduce the
inter-image representation into the per-image feature extraction. The extra
representation is used to guide the per-image feature extraction and eliminate
the ambiguity in normal estimation. We demonstrate the effect of our design on
a wide range of samples, especially on dark materials. Our method produces
significantly better results than the state-of-the-art methods on both
synthetic and real data.
