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.

通过使用残差多尺度注意力特征融合技术处理对象的复杂区域，从而提取准确的物体法线贴图，该方法在光度立体方法中克服了对象几何和表面材料变异引起的物体表面反射性质的影响。

RMAFF-PSN：一种残差多尺度注意特征融合光度立体网络

RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric  Stereo Network

Recovering the geometry of a human head from a single image, while
factorizing the materials and illumination is a severely ill-posed problem that
requires prior information to be solved. Methods based on 3D Morphable Models
(3DMM), and their combination with differentiable renderers, have shown
promising results. However, the expressiveness of 3DMMs is limited, and they
typically yield over-smoothed and identity-agnostic 3D shapes limited to the
face region. Highly accurate full head reconstructions have recently been
obtained with neural fields that parameterize the geometry using multilayer
perceptrons. The versatility of these representations has also proved effective
for disentangling geometry, materials and lighting. However, these methods
require several tens of input images. In this paper, we introduce SIRA, a
method which, from a single image, reconstructs human head avatars with high
fidelity geometry and factorized lights and surface materials. Our key
ingredients are two data-driven statistical models based on neural fields that
resolve the ambiguities of single-view 3D surface reconstruction and appearance
factorization. Experiments show that SIRA obtains state of the art results in
3D head reconstruction while at the same time it successfully disentangles the
global illumination, and the diffuse and specular albedos. Furthermore, our
reconstructions are amenable to physically-based appearance editing and head
model relighting.

使用基于神经场的两个数据驱动统计模型，从单张图像中重建具有高保真度几何结构和因式化光线和表面材质的人头像，成功分离了全局照明和漫反射和镜面反射反射率，并使头部模型的外观编辑和照明调整成为可能。