Structured Light Image Planar-Topography Feature Decomposition for Generalizable 3D Shape Measurement

The application of structured light (SL) techniques has achieved remarkable success in three-dimensional (3D) measurements. Traditional methods generally calculate SL information pixel by pixel to obtain the measurement results. Recently, the rise of deep learning (DL) has led to significant developments in this task. However, existing DL-based methods generally learn all features within the image in an end-to-end manner, ignoring the distinction between SL and non-SL information. Therefore, these methods may encounter difficulties in focusing on subtle variations in SL patterns across different scenes, thereby degrading measurement precision. To overcome this challenge, we propose a novel SL Image Planar-Topography Feature Decomposition Network (SIDNet). To fully utilize the information from different SL modality images (fringe and speckle), we decompose different modalities into topography features (modality-specific) and planar features (modality-shared). A physics-driven decomposition loss is proposed to make the topography/planar features dissimilar/similar, which guides the network to distinguish between SL and non-SL information. Moreover, to obtain modality-fused features with global overview and local detail information, we propose a wrapped phase-driven feature fusion module. Specifically, a novel Tri-modality Mamba block is designed to integrate different sources with the guidance of the wrapped phase features. Extensive experiments demonstrate the superiority of our SIDNet in multiple simulated 3D measurement scenes. Moreover, our method shows better generalization ability than other DL models and can be directly applicable to unseen real-world scenes.