Photometric stereo (PS) endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often overlook the complexity of object surfaces. These neural network models, which exclusively rely on photometric images for training, often produce blurred results in high-frequency regions characterized by local discontinuities, such as wrinkles and edges with significant gradient changes. To address this, we propose the Image Gradient-Aided Photometric Stereo Network (IGA-PSN), a dual-branch framework extracting features from both photometric images and their gradients. Furthermore, we incorporate an hourglass regression network along with supervision to regularize normal regression. Experiments on DiLiGenT benchmarks show that IGA-PSN outperforms previous methods in surface normal estimation, achieving a mean angular error of 6.46 while preserving textures and geometric shapes in complex regions.

翻译：光度立体（PS）旨在利用不同光照条件下光度图像的明暗线索来确定表面法线。当前基于深度学习的PS方法常忽略物体表面的复杂性。这些仅依赖光度图像进行训练的神经网络模型，在具有局部不连续性的高频区域（如褶皱和梯度变化显著的边缘）往往产生模糊结果。为解决此问题，我们提出图像梯度辅助光度立体网络（IGA-PSN），这是一种从光度图像及其梯度中提取特征的双分支框架。此外，我们引入沙漏回归网络及监督机制以规范化法线回归过程。在DiLiGenT基准测试上的实验表明，IGA-PSN在表面法线估计方面优于现有方法，在保持复杂区域纹理与几何形状的同时，将平均角度误差降至6.46。