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Advancing 3D surface imaging: single-axis structured light illumination plenoptic camera with machine learning
Optics Express
|August 13, 2025
Summary
We developed a novel single-axis 3D structured light illumination (SLI) plenoptic camera. This 3D imaging system overcomes surface texture limitations and enhances resolution and range using machine learning algorithms.
Area of Science:
- Optics and Photonics
- 3D Imaging Technologies
- Machine Learning Applications
Background:
- Structured light illumination (SLI) is a 3D surface imaging technique.
- Traditional SLI often requires off-axis projection for depth measurement.
- Plenoptic imaging (PI) offers single-axis 3D imaging but relies on surface texture.
Purpose of the Study:
- To present a single-axis 3D SLI plenoptic camera.
- To combine single-axis imaging benefits with surface-texture independence.
- To explore machine learning for enhanced 3D SLI performance.
Main Methods:
- Developed a coaxial SLI 3D plenoptic camera.
- Implemented traditional feature tracking algorithms.
- Integrated machine learning (ML) algorithms for data processing.
Main Results:
- Achieved depth sensitivity down to 0.2% dz/z0.
- Demonstrated independence from surface texture.
- Showcased enhanced resolution and measurement range via ML.
Conclusions:
- The single-axis SLI 3D plenoptic camera is suitable for in-situ topographical measurements.
- This technology is effective in challenging environments like high temperatures or constrained geometries.
- The system performs well on objects lacking trackable surface texture.
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