Evidence map›Paper›PMID 39066131›Full record

ArticleSensors (Basel, Switzerland)2024

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

Yi Cai, Mingyu Guo, Congying Wang, Xiaowei Lu, Xuanke Zeng, Yiling Sun, Yuexia Ai, Shixiang Xu, Jingzhen Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Yi CaiOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-1694-7413
Mingyu GuoOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Congying WangOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Xiaowei LuOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Xuanke ZengOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Yiling SunOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Yuexia AiOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Shixiang XuOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Jingzhen LiOptoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

Funding

National Natural Science Foundation of China 62075138, 92050203, 62275163, and 12174264Natural Science Foundation of Guangdong Province 2021A1515011909 and 2022A1515011457Shenzhen Fundamental Research Program JCYJ20190808121817100, JCYJ20190808164007485, JCYJ20210324 095213037, JCYJ20190808143419622, JSGG20191231144201722, and JCYJ20190808115601653, JSGG20211108092800001, JSGG20220831110601002 and JCYJ20190808115601653Shenzhen Key Laboratory of Photonics and Biophotonics ZDSYS20210623092006020
6 · The paper itself

Abstract

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer's benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.

Indexed as

deep learningdepth estimationfringe projection profilometrytransfer learning

Identifiers

PMID39066131
PMCPMC11280829

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.