Fringe Pattern

Fringe pattern analysis is crucial for three-dimensional shape measurement, particularly in techniques like fringe projection profilometry. Current research focuses on developing deep learning models, including convolutional neural networks (CNNs) and transformer architectures, to improve the accuracy and robustness of 3D reconstruction from fringe images, often aiming to reduce the number of required patterns (even down to a single image). These advancements leverage techniques like multi-branch networks and novel loss functions to address challenges such as fringe order ambiguity and improve accuracy, especially near object boundaries. This leads to more efficient and potentially more accessible 3D imaging systems with applications in various fields.