Evidence map›Paper›PMID 37631684›Full record

ArticleSensors (Basel, Switzerland)2023

Dynamic 3D Measurement without Motion Artifacts Based on Feature Compensation.

Guoce Hu, Jun Wang, Huaxia Deng, Mengchao Ma, Xiang Zhong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Precision Optical Metrology and Smart Sensing.Sensors (Basel, Switzerland) · 2024
    Article
  2. Article
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

5 authors.

Guoce HuSchool of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.ORCID 0000-0003-2893-5919
Jun WangSchool of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Huaxia DengCAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei 230027, China.
Mengchao MaSchool of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.ORCID 0000-0001-9682-7499
Xiang ZhongSchool of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.

Funding

Fundamental Research Funds for the Central Universities WK2090000039Fundamental Research Funds for the Central Universities WK2480000010National Natural Science Foundation of China 52275529Natural Science Foundation of Anhui Province 2208085ME138
6 · The paper itself

Abstract

Phase-shift profilometry (PSP) holds great promise for high-precision 3D shape measurements. However, in the case of measuring moving objects, as PSP requires multiple images to calculate the phase, the movement of the object causes artifacts in the measurement, which in turn has a significant impact on the accuracy of the 3D surface measurement. Therefore, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method as a case study. The simulation results show that the phase of the object is greatly affected when the object is in motion and that the phase shift due to motion can be effectively reduced using this method. Finally, artifact optimization was carried out by way of specific copper tube vibration experiments at a measurement frequency of 320 Hz. The experimental results prove that the method is well implemented.

Indexed as

error compensationmotion artifactsphase-shift profilometrythree-dimensional reconstruction

Identifiers

PMID37631684
PMCPMC10457782

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.