Adaptive high dynamic range 3D shape measurement based on time-domain superposition

Abstract

Fringe projection profilometry (FPP) has found extensive application in the field of 3D measurement, but achieving high-dynamic-range (HDR) measurement remains a challenge. Despite many progresses to address it, an evaluation model to determine the optimal parameters and quantitatively predict measurement accuracy is still absent. Time-domain superposition (TDS) is a new HDR measurement method that can avoid overexposure by splitting a longer exposure into several shorter ones and then blending the fringe patterns together. In this paper, we present an adaptive HDR method (A-HDR) using TDS. For a specific measurement scene, the optimal measurement parameters can be determined by using the proposed adaptive parameter selection method. Once the system parameters are known, the proposed method can quantitatively predict the final phase accuracy and provide guidance for the selection of optimal measurement parameters, thus avoiding redundancies of projected patterns and deficiencies in measurement accuracy. Experiments demonstrate the consistency between the actual measurement accuracy and the theoretical prediction accuracy, and the effectiveness of the proposed method in selecting optimal parameters for 3D shape measurement in HDR scenes has been proved. The presented method paves an effective way to quantitatively determine the optimal system parameters and predict measurement accuracy for HDR measurement.