Learning Deep Feature Correlation for Microscopic Structured Light Imaging

Abstract

Structured light imaging is a typical technique for industrial 3-D microscopic measurement. Extensive research on structured light codecs has been conducted to accurately correlate camera and projector pixels. However, these methods suffer significant degradation when measuring low-reflectivity and complex surfaces. This article introduces a deep correlation-based cascade structured light network (CasSLNet) that utilizes deep phase and column features to calculate correspondences at the subpixel scale. To mitigate the huge computational cost of full correlation, a coarse-to-fine approach is proposed. Specifically, multiscale features from the camera observation sequence and the 1-D encoding pattern are extracted through a pseudosiamese network, and cascade cost volumes are constructed. An initial column map is then regressed from the low-resolution column cost volume. Based on this, an iterative update operator is introduced to refine initial estimates, resulting in a full-resolution column map. Furthermore, a structured light dataset has been collected and experiments have been conducted on a typical structured light imaging platform. Experimental results demonstrate that CasSLNet outperforms both traditional and state-of-the-art deep learning-based methods.