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

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Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep

Andrew-Hieu Nguyen^1,2, Khanh L Ly³, Van Khanh Lam⁴

¹Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study introduces a novel 3D shape reconstruction method using structured light and deep learning. It accurately determines object depth from a single image, advancing applications in robotics and virtual reality.

Keywords:

convolutional neural network deep learning fringe-to-phase transformation single-shot imaging three-dimensional image acquisition three-dimensional sensing

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Area of Science:

Computer Vision
3D Reconstruction
Artificial Intelligence

Background:

Three-dimensional (3D) shape acquisition from single images is crucial for fields like medical imaging, robotics, and virtual reality.
Existing methods often require multiple images or complex setups, limiting their application scope.
Robust and efficient single-shot 3D reconstruction remains a significant challenge.

Purpose of the Study:

To develop a robust 3D shape reconstruction approach from a single structured-light image.
To integrate structured-light techniques with deep learning for enhanced accuracy and efficiency.
To provide a versatile tool for various scientific and engineering applications.

Main Methods:

A novel single-input dual-output deep neural network was designed.
The network transforms a single structured-light image into intermediate fringe patterns and a coarse phase map.
Ground-truth data was generated using a conventional fringe projection technique for training.

Main Results:

The proposed approach accurately determines unwrapped phase distributions containing depth information.
The method demonstrated robustness in experimental assessments.
The system successfully reconstructs 3D shapes from single-shot structured-light images.

Conclusions:

The integrated structured-light and deep learning approach offers a robust solution for single-shot 3D shape acquisition.
This technique significantly advances the capabilities for 3D reconstruction in demanding applications.
The method presents a promising tool for scientific research and engineering.