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Optics & Laser Technology· 2026Q1

Using a digital model for fringe projection profilometry optimisation

D. Weston, X. Kong, George S. D. Gordon, Samanta Piano

Short summary

A digital model built in Blender, accurately simulating fringe projection profilometry (FPP) systems, reduced the number of required images by 41% and improved reconstruction quality by up to 74% on real-world metrology benchmarks.

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Key points

  • A digital model in Blender accurately simulates FPP systems by matching characterization quality, gamma response, and images.
  • System characterization using Zhang's method is critical for achieving high precision in FPP.
  • Optimizing parameters like phase-shift count, camera-projector spacing, and fringe density in the digital model significantly improves reconstruction quality.
  • Optimized parameters transferred to a physical system reduced image count by 41% and mean SMCD by up to 74%.

AI-generated from the title and abstract; the full text is not read.

Abstract

Fringe projection profilometry (FPP) is a widely used technique for measuring object surface form and three-dimensional (3D) geometry, capable of delivering high-precision, high-resolution measurements when paired with suitable cameras and projectors. However, in practical deployments, identifying parameter configurations that maximise precision while satisfying real-world constraints remains challenging. To address this, an automated digital model framework was implemented in Blender, an open-source 3D software package enabling provides a ray-traced rendering environment that accurate simulation of physical systems. The physical setup was replicated in the digital model by matching characterisation quality, gamma response, and characterisation images. Accurate system characterisation using Zhang’s method Zhang (2021) [1], to obtain intrinsic and extrinsic parameters, is shown to be critical for achieving high precision. Using this digital model, systematic exploration and optimisation of key parameters were demonstrated, including phase-shift count, camera-projector spacing, and fringe density. These parameters span both system geometry (e.g., camera-projector positioning) and algorithmic choices, such as 2D phase-shifting and unwrapping methods Zuo et al. (2016) [2]. Three measurement artefacts, representative of real-world metrology scenarios, were used to benchmark the system Sims-Waterhouse et al. (2017) [3]. The symmetrical mean Chamfer distance (SMCD), computed between ground-truth and reconstructed meshes, was used to evaluate reconstruction quality. After single-objective optimisation within the digital model, transferring the parameters to the physical system reduced the number of required images per measurement by 41% (from 36 to 21). A reduction of 74.0% mean SMCD was also achieved by altering the fringe pattern stripe count. A 36.9% mean SMCD was obtained by adjusting the camera and projector spacing purely in the digital model. These results demonstrate that optimising an FPP system using a digital model can significantly improve reconstruction quality while reducing measurement effort in real-world metrology applications.

The authors' abstract, as published at the source. Optics & Laser Technology, 2026 · DOI ↗

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Field: Computer Vision and Pattern Recognition

Computer Vision and Pattern RecognitionComputer Science