Optimal multiple fringe pattern composition by weighted complex amplitude fusion in fringe projection profilometry

This paper presents an optimal multiple fringe pattern composition method for 3D shape measurement of high-dynamic-range (HDR) objects using fringe projection profilometry (FPP). With the inverse variance weighting theory, we take the square of the modulation intensities of the fringe pattern images with different intensity levels as the weights to obtain the composited phase of fringe patterns by weighted complex amplitude fusion, which improves the measurement precision of HDR objects. Additionally, we integrate HDR 3D shape measurement and temporal noise reduction into a unified framework by utilizing weighted complex amplitude fusion to completely measure translucent objects with specular reflections. Simulations and experiments demonstrate that our method can achieve higher measurement precision and is resistant to the time-varying ambient light.