RMAFF-PSN：一种残差多尺度注意力特征融合光度立体网络
RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Network
AI总结：
针对复杂结构与材质变化区域法线图预测难的问题，提出残差多尺度注意力特征融合光度立体网络RMAFF-PSN，构建新真实数据集，在同等输入下优于多数现有标定方法，稀疏光照下也表现良好。
摘要：
使用光度立体方法从二维图像中预测结构复杂、空间材质变化区域的物体精确法线图极具挑战性，原因在于物体几何形状与表面材质的变化会带来表面反射特性的影响。为解决这一问题，我们提出了一种名为RMAFF-PSN的光度立体网络，该网络采用残差多尺度注意力特征融合来处理物体的“困难”区域。与以往仅使用堆叠卷积层从输入图像中提取深度特征的方法不同，我们的方法整合了图像不同分辨率阶段与不同尺度的特征信息。该方法通过深浅阶段特征提取、双分支增强以及注意力优化，能够保留复杂区域中物体纹理、几何形状等更多物理信息。为在真实场景下测试该网络结构，我们提出了一个名为Simple PS data的新型真实数据集，其中包含多个结构与材质各异的物体。在公开基准数据集上的实验结果表明，在输入图像数量相同的情况下，我们的方法优于大多数现有的标定光度立体方法，尤其在高度非凸物体结构的场景下表现更突出。我们的方法在稀疏光照条件下也能取得良好效果。
英文摘要：
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.
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