Deep Learning-Enhanced Feature Fusion for Multichannel Autostereoscopic 3D Measurement of Micro-Structured Surfaces

Accurate 3D topography measurement of micro-structured surfaces remains challenging due to the limitations of conventional autostereoscopic systems, particularly the intrinsic constraints of light-field imaging and dependence on single-source data. Building on a multichannel autostereoscopic measurement system that simultaneously captures a high-resolution (HR) 2D center view containing rich textural and edge information and a light-field image providing dense multiview geometric cues, a deep learning-enhanced feature fusion network is introduced. This model is a hybrid deep learning architecture featuring a convolutional local feature extractor for the HR image, a Transformer-based global feature extractor for angular relations in the light field, and a cross-channel attention fusion module for effective feature integration. The end-to-end trainable network is optimized using a composite loss function. Experiments on synthetic and real micro-structured surfaces demonstrate stable performance of the proposed approach, achieving improved accuracy and stability over current depth-estimation methods in challenging micro-scale scenarios.