Single-shot 3D measurement of highly reflective objects with deep learning

Three-dimensional (3D) measurement methods based on fringe projection profilometry (FPP) have been widely applied in industrial manufacturing. Most FPP methods adopt phase-shifting techniques and require multiple fringe images, thus having limited application in dynamic scenes. Moreover, industrial parts often have highly reflective areas leading to overexposure. In this work, a single-shot high dynamic range 3D measurement method combining FPP with deep learning is proposed. The proposed deep learning model includes two convolutional neural networks: exposure selection network (ExSNet) and fringe analysis network (FrANet). The ExSNet utilizes self-attention mechanism for enhancement of highly reflective areas leading to overexposure problem to achieve high dynamic range in single-shot 3D measurement. The FrANet consists of three modules to predict wrapped phase maps and absolute phase maps. A training strategy directly opting for best measurement accuracy is proposed. Experiments on a FPP system showed that the proposed method predicted accurate optimal exposure time under single-shot condition. A pair of moving standard spheres with overexposure was measured for quantitative evaluation. The proposed method reconstructed standard spheres over a large range of exposure level, where prediction errors for diameter were 73 mu m (left) and 64 mu m (right) and prediction error for center distance was 49 mu m. Ablation study and comparison with other high dynamic range methods were also conducted.