Evidence map›Paper›PMID 40293081›Full record

ArticleSensors (Basel, Switzerland)2025

An Efficient 3D Measurement Method for Shiny Surfaces Based on Fringe Projection Profilometry.

Hao Wei, Hongru Li, Xuan Li, Sha Wang, Guoliang Deng, Shouhuan Zhou

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hao WeiCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Hongru LiCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Xuan LiCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Sha WangCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Guoliang DengCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Shouhuan ZhouCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Funding

Fundamental Research Funds for the Central Universities SCU2023D056Postdoctoral Research and Development Fund of Sichuan University 2023SCU12008Sichuan Province Science and Technology Support Program 2021YFG0323Sichuan University SCU241059
6 · The paper itself

Abstract

Fringe projection profilometry (FPP) is a widely employed technique owing to its rapid speed and high accuracy. However, when FPP is utilized to measure shiny surfaces, the fringes tend to be saturated or too dark, which significantly compromises the accuracy of the 3D measurement. To overcome this challenge, this paper proposes an efficient method for the 3D measurement of shiny surfaces based on FPP. Firstly, polarizers are employed to alleviate fringe saturation by leveraging the polarization property of specular reflection. Although polarizers reduce fringe intensity, a deep learning method is utilized to enhance the quality of fringes, especially in low-contrast regions, thereby improving measurement accuracy. Furthermore, to accelerate measurement efficiency, a dual-frequency complementary decoding method is introduced, requiring only two auxiliary fringes for accurate fringe order determination, thereby achieving high-efficiency and high-dynamic-range 3D measurement. The effectiveness and feasibility of the proposed method are validated through a series of experimental results.

Indexed as

complementary decodingfringe projection profilometryhigh dynamic rangespecular reflectionthree-dimensional measurement

Identifiers

PMID40293081
PMCPMC11945723

What OpenQuestion holds

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Registered trials

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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.