A Phase Fusion and Restoration Method Based on Global Neural Calibration Model

Fringe projector profilometry provides incomplete and noisy measurement data for machined metal surfaces that are extensively utilized in various industrial applications. Previous methods have attempted to address this issue through high dynamic range imaging or by introducing reflectance information of extra facilities with multiple lights. However, these methods decrease measurement efficiency and complicate the measurement facility. Therefore, this paper combines these two strategies by utilizing the reflectance information from nonfringe images to guide the fusion and restoration of incomplete phase information at different exposure times, where an advanced anisotropic reflectance model is employed to obtain the reflectance information. A global neural calibration model based on a neural radiance field is proposed to bridge the phase and reflectance information. This model comprises global neural phase-to-coordinate and phase-to-light models. The global neural phase-to-coordinate model uses the neural radiance field to establish a relationship from the pixel position and the relative phase to the relative point coordinate. Besides, the global neural phase-to-light model employs the neural radiance field to describe the light distribution of the projector. The global neural calibration model transforms and propagates the reflectance information to optimize the phase information, thereby enhancing the performance of fringe projection profilometry for machined metal surfaces. Real experiments on standard ball bars demonstrate that the global neural calibration model achieves calibration accuracy of 31.89 μm. In addition, experiments on six machined metal workpieces demonstrate that the proposed method achieves measurement accuracy of 58.5 μm compared with coordinate measuring machine results.