  Predicting accurate normal maps of objects from two-dimensional images in
regions of complex structure and spatial material variations is challenging
using photometric stereo methods due to the influence of surface reflection
properties caused by variations in object geometry and surface materials. To
address this issue, we propose a photometric stereo network called a RMAFF-PSN
that uses residual multiscale attentional feature fusion to handle the
``difficult'' regions of the object. Unlike previous approaches that only use
stacked convolutional layers to extract deep features from the input image, our
method integrates feature information from different resolution stages and
scales of the image. This approach preserves more physical information, such as
texture and geometry of the object in complex regions, through shallow-deep
stage feature extraction, double branching enhancement, and attention
optimization. To test the network structure under real-world conditions, we
propose a new real dataset called Simple PS data, which contains multiple
objects with varying structures and materials. Experimental results on a
publicly available benchmark dataset demonstrate that our method outperforms
most existing calibrated photometric stereo methods for the same number of
input images, especially in the case of highly non-convex object structures.
Our method also obtains good results under sparse lighting conditions.
