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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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

Lei Lu, Yuheng Wang, Zhilong Su

|April 24, 2026

Summary

This summary is machine-generated.

This study introduces FP-DiffNet, a novel deep learning framework that restores degraded fringe patterns for accurate underwater 3D imaging. It significantly improves 3D reconstruction in turbid waters without needing water optical parameter models.

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Area of Science:

Optics and Photonics
Computer Vision
Robotics

Background:

Underwater 3D imaging is crucial for scientific and engineering tasks.
Degradation of fringe patterns due to light attenuation, scattering, and distortion limits accuracy.
Existing methods struggle with accuracy in turbid or optically complex underwater environments.

Purpose of the Study:

To develop a robust method for restoring degraded fringe patterns in underwater structured-light 3D imaging.
To enhance the accuracy of phase reconstruction and 3D object reconstruction in challenging underwater conditions.
To create a practical and adaptable solution for underwater 3D imaging without prior water optical property knowledge.

Main Methods:

Analysis of structured light propagation and its impact on fringe image quality in water.
Development of FP-DiffNet, a diffusion-model-based neural framework for fringe restoration.
Training a U-Net model with physics-guided constraints and adaptive noise annealing for iterative denoising.

Main Results:

FP-DiffNet effectively restores degraded fringe patterns, separating scattering-induced noise while preserving fringe structures.
Achieved high performance in extremely turbid water: PSNR of 42.42 dB and wrapped-phase MAE of 0.0354 rad.
Enabled sub-millimeter absolute measurement and 3D reconstruction with RMSE below 0.1 mm in turbid water.

Conclusions:

The proposed FP-DiffNet framework offers a significant advancement in underwater structured-light 3D imaging.
It provides robust and accurate 3D reconstruction capabilities in challenging, turbid underwater environments.
The method's independence from water optical parameter modeling makes it highly practical for real-world applications.