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Deep Neural Networks for Image-Based Dietary Assessment

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Deep neural networks for single shot structured light profilometry.

Sam Van der Jeught, Joris J J Dirckx

|June 30, 2019

Summary

This study introduces a deep learning method for single-shot structured light profilometry, enabling faster and more robust 3D measurements. The approach accurately extracts height data from fringe patterns, bypassing traditional processing steps.

Area of Science:

Optical Metrology
Computer Vision
Artificial Intelligence

Background:

Single-shot structured light profilometry offers advantages in speed and robustness over multi-shot methods.
Traditional techniques require complex preprocessing steps like background masking, noise reduction, and phase unwrapping.

Purpose of the Study:

To develop a novel, deep learning-based approach for extracting 3D height information from single deformed fringe patterns.
To demonstrate the efficacy of a fully convolutional neural network in 3D optical metrology.

Main Methods:

A fully convolutional neural network was trained on simulated height maps and corresponding fringe patterns.
The network learns to directly map fringe patterns to height information.

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Main Results:

The deep learning model accurately reconstructs full-field height information from unseen fringe patterns.
The network implicitly learns preprocessing steps, simplifying the overall measurement pipeline.

Conclusions:

Deep learning offers a powerful and efficient alternative for single-shot structured light profilometry.
This method enhances measurement speed and robustness while reducing the need for manual data processing.