ArticleLight, science & applications2023
Modeling the measurement precision of Fringe Projection Profilometry.
Article in Light, science & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Towards theoretical foundation for high-precision fringe projection profilometry system design and optimization.Light, science & applications · 2026Article
- On the precision models of fringe projection profilometry: unification, simplification and connection.Light, science & applications · 2026Article
- High-Update-Rate (25 kHz) Laser Ranging with Random Noise Modulation for Fast and Precise Absolute Distance Measurement.Sensors (Basel, Switzerland) · 2025Article
- Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration.Sensors (Basel, Switzerland) · 2025Review
- Accelerating fringe projection profilometry to 100k fps at high-resolution using deep learning.Light, science & applications · 2025Article
- Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning.Light, science & applications · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Three-dimensional (3D) surface geometry provides elemental information in various sciences and precision engineering. Fringe Projection Profilometry (FPP) is one of the most powerful non-contact (thus non-destructive) and non-interferometric (thus less restrictive) 3D measurement techniques, featuring at its high precision. However, the measurement precision of FPP is currently evaluated experimentally, lacking a complete theoretical model for guidance. We propose the first complete FPP precision model chain including four stage models (camera intensity, fringe intensity, phase and 3D geometry) and two transfer models (from fringe intensity to phase and from phase to 3D geometry). The most significant contributions include the adoption of a non-Gaussian camera noise model, which, for the first time, establishes the connection between camera's electronics parameters (known in advance from the camera manufacturer) and the phase precision, and the formulation of the phase to geometry transfer, which makes the precision of the measured geometry representable in an explicit and concise form. As a result, we not only establish the full precision model of the 3D geometry to characterize the performance of an FPP system that has already been set up, but also explore the expression of the highest possible precision limit to guide the error distribution of an FPP system that is yet to build. Our theoretical models make FPP a more designable technique to meet the challenges from various measurement demands concerning different object sizes from macro to micro and requiring different measurement precisions from a few millimeters to a few micrometers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.