Presentation
21 December 2022 Structured illumination using deep learning: with applications to high-speed 3D surface imaging
Author Affiliations +
Abstract
In this presentation, I will show how the combination of deep learning and optical measurement will bring new "vitality" to the "traditional" field of structured light 3D imaging. Compared to traditional methods, deep learning shows promising performance for applications in fringe analysis, phase unwrapping, subset correlation, and error compensation. As a result, with the aid of deep learning, we have developed a series of "single-frame" high-precision unambiguous structured light techniques for high-speed, high-precision, ultrafast 3D imaging. Finally, I will introduce our recent research on Bayesian deep learning, which promises to assess the reliability of the network by explicitly quantifying uncertainty, opening a new window for the wide acceptance of artificial intelligence in the field of optical metrology.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chao Zuo "Structured illumination using deep learning: with applications to high-speed 3D surface imaging", Proc. SPIE 12319, Optical Metrology and Inspection for Industrial Applications IX, 123190P (21 December 2022); https://doi.org/10.1117/12.2643140
Advertisement
Advertisement
KEYWORDS
Stereoscopy

3D applications

3D image processing

Optical metrology

3D metrology

Structured light

Optical interferometry

Back to Top