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Updated: Jun 18, 2025

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

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

Published on: December 3, 2013

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

Yi Cai¹, Mingyu Guo¹, Congying Wang¹

¹Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This summary is machine-generated.

TTFDNet, a novel transformer network, achieves highly accurate 3D depth estimation from single fringe patterns. This method excels in precision and robustness for applications in manufacturing and computer vision.

Keywords:

deep learning depth estimation fringe projection profilometry transfer learning

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Area of Science:

Computer Vision
Metrology
Machine Learning

Background:

Fringe projection profilometry is crucial for 3D shape measurement.
Accurate depth estimation from single fringe patterns remains challenging.
Existing methods often lack precision or robustness in dynamic scenarios.

Purpose of the Study:

To introduce TTFDNet, a transformer-based network for end-to-end depth estimation.
To improve accuracy and robustness in single-frame fringe pattern analysis.
To enable practical applications in manufacturing and automation.

Main Methods:

Developed TTFDNet incorporating a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module, and a progressive depth extractor (PDE).
Utilized transfer learning with fringe structure consistency evaluation (FSCE) to enhance performance on limited datasets.
Evaluated the network on 208 scenes for depth estimation accuracy.

Main Results:

TTFDNet achieved a mean absolute error (MAE) of 0.00372 mm, significantly outperforming Unet, PDE, and PCTNet.
Demonstrated high precision with deviations of ~90 μm for a ball and ~6 μm for a metal part.
Showcased excellent generalization and robustness in dynamic reconstruction and varied imaging conditions.

Conclusions:

TTFDNet offers a state-of-the-art solution for accurate and robust single-frame depth estimation.
The proposed network is suitable for real-world applications in manufacturing, automation, and computer vision.
Transfer learning approach effectively leverages transformer capabilities even with small datasets.