Underwater structured-light 3D imaging method based on FP-DiffNet

This work focuses on underwater structured-light 3d imaging method based on fp-diffnet, contributing to Wei Pan’s research thread in 3D vision, optical metrology, geometry processing, and industrial inspection.

In the broader publication record, this work sits in Applied Optics, 65(10):3138-3148 and connects to practical problems in 3D sensing, computational geometry, and industrial machine vision.

Algorithm principle

The core pipeline follows the structured-light measurement loop: project coded fringe patterns, recover phase information from captured images, unwrap or refine the phase, and convert it into depth. The algorithmic contribution is the correction layer around this loop, such as motion compensation, learned phase restoration, diffusion-based reconstruction, calibration, or model predictive control, so that the 3D result remains stable when the scene, lighting, or imaging geometry is difficult.

Visual material

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Results and impact

The reported evaluation in Applied Optics, 65(10):3138-3148 positions the method as a practical contribution to the surrounding 3D vision and industrial inspection workflow. The experiments are used to show whether the proposed representation or pipeline improves robustness, accuracy, speed, or deployability compared with the relevant baseline methods.