One of the key issues in photometric stereo is the extension of its application in real world objects which shows non-Lambertian reflectance. This letter proposes a multi-scale weighted convolutional fusion network with deep learning architecture to realize high-precision perception of non-Lambertian surfaces under arbitrary illumination conditions. A multi-scale convolutional fusion module is designed to strengthen the photometric physics and the utilization of the neighborhood features at the same time so as to overcome shadows and distinguish multiple materials. In order to further deal with the problem of arbitrary illumination conditions, a multi-resolution polar coordinate division method is proposed to integrate the input image information and fully utilizes the multi-scale convolution. Both syntheses and real-world experiments verifies the performance of the proposed method in recovery accuracy and computational efficiency.

中文翻译：

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非朗伯光度立体的多尺度卷积融合网络

光度立体的关键问题之一是其在显示非朗伯反射率的现实世界对象中的应用扩展。这封信提出了一种具有深度学习架构的多尺度加权卷积融合网络，以实现任意光照条件下对非朗伯表面的高精度感知。多尺度卷积融合模块旨在同时加强光度物理和邻域特征的利用，以克服阴影并区分多种材料。为了进一步处理任意光照条件的问题，提出了一种多分辨率极坐标划分方法来整合输入图像信息，充分利用多尺度卷积。