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Updated: Jul 1, 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

Weakly Supervised Depth Estimation for 3D Imaging with Single Camera Fringe Projection Profilometry.

Chunqian Tan¹, Wanzhong Song¹

¹College of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This summary is machine-generated.

This study introduces a weakly supervised deep learning method for faster 3D imaging using fringe projection profilometry (FPP). The novel approach significantly improves fringe pattern efficiency for accurate depth estimation.

Keywords:

depth estimation fringe projection profilometry weakly supervised learning

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Last Updated: Jul 1, 2025

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Published on: December 3, 2013

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Area of Science:

Optics and Photonics
Computer Vision
Machine Learning

Background:

Fringe projection profilometry (FPP) is a key technique for high-accuracy 3D imaging.
Increasing fringe patterns enhances accuracy but reduces measurement speed.
Conventional methods like dual-frequency FPP have limitations in fringe pattern numbers due to phase errors.

Purpose of the Study:

To develop a novel, weakly supervised deep learning method for depth estimation in single-camera FPP.
To improve measurement speed and fringe pattern efficiency compared to conventional FPP techniques.
To achieve competitive accuracy in 3D reconstruction with reduced fringe patterns.

Main Methods:

A weakly supervised deep learning network inspired by unsupervised monocular depth estimation was developed.
The network was trained to estimate depth from three frames of 64-period fringe images.
The method focuses on efficient depth estimation from limited fringe data.

Main Results:

The trained network successfully estimates depth from only three frames of 64-period fringe images.
The proposed method demonstrates at least 50% greater fringe pattern efficiency than conventional FPP.
Experimental results show accuracy comparable to supervised methods and superior performance over conventional dual-frequency FPP.

Conclusions:

Weakly supervised deep learning offers a promising approach to enhance the speed and efficiency of FPP.
The developed method achieves high-accuracy 3D reconstruction with a significantly reduced number of fringe patterns.
This advancement has the potential to broaden the applications of FPP in various fields requiring fast and accurate 3D imaging.