Shading- and geometry-aware lighting calibration network for uncalibrated photometric stereo

Abstract

Three-dimensional measurement provides essential geometric information for fault diagnosis and product optimization in intelligent manufacturing applications. Photometric stereo is a non-destructive 3D measurement technique that estimates the surface normals of objects using shading cues from images under different lighting conditions. However, the generalized bas-relief (GBR) ambiguity caused by unknown or varying lighting will significantly decrease measurement accuracy. To address this issue, we propose a shading- and geometry-aware lighting calibration network (SGLC-Net) to mitigate the inherent ambiguity and enhance surface normal estimation in uncalibrated photometric stereo by generating accurate lighting information. The proposed method iteratively optimizes lighting direction and intensity by leveraging self-generated shading and normal prior features. To further improve the accuracy of the lighting estimation, we introduce collocated light into SGLC-Net to implicitly extract shading features of images to generate accurate rough lighting. Accurate rough lighting can generate accurate shading and normal prior features, which can be used to optimize rough lighting to generate fine lighting. Experimental results indicate that the proposed method significantly outperforms most uncalibrated photometric stereo methods in lighting estimation on multiple real-world datasets. Furthermore, our method can seamlessly integrate with most uncalibrated photometric stereo methods to effectively enhance the accuracy of the surface normal estimation under unknown illumination.