Evidence map›Paper›PMID 39967773›Full record

ArticleNanophotonics (Berlin, Germany)2025

Collision of high-resolution wide FOV metalens cameras and vision tasks.

Shaoqi Li, Wangzhe Zhou, Yiyi Li, Zhechun Lu, Fen Zhao, Xin He, Xinpeng Jiang, Te Du, Zhaojian Zhang, Yuehua Deng and 9 more

Abstract read
In one paragraph

Article in Nanophotonics (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. TRIM: Simultaneous Thermometry, Ranging, and Imaging via a Monolithic Metalens.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Compound Meta-Optics for Advanced Optical Engineering.Sensors (Basel, Switzerland) · 2026
    Review
  3. Article
  4. 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

19 authors.

Shaoqi LiCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0009-0007-3474-2626
Wangzhe ZhouCollege of Science, National University of Defense Technology, Changsha 410073, China.
Yiyi LiCollege of Science, National University of Defense Technology, Changsha 410073, China.
Zhechun LuCollege of Science, National University of Defense Technology, Changsha 410073, China.
Fen ZhaoCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0009-0006-4349-4380
Xin HeCollege of Science, National University of Defense Technology, Changsha 410073, China.
Xinpeng JiangCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-1548-3637
Te DuCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-6946-4239
Zhaojian ZhangCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-9388-8752
Yuehua DengCollege of Science, National University of Defense Technology, Changsha 410073, China.
Shengru ZhouCollege of Science, National University of Defense Technology, Changsha 410073, China.
Hengchang NongCollege of Science, National University of Defense Technology, Changsha 410073, China.
Yang YuCollege of Science, National University of Defense Technology, Changsha 410073, China.
Zhenfu ZhangCollege of Science, National University of Defense Technology, Changsha 410073, China.
Yunxin HanCollege of Science, National University of Defense Technology, Changsha 410073, China.
Sha HuangCollege of Science, National University of Defense Technology, Changsha 410073, China.
Jiagui WuSchool of Physical Science and Technology Southwest University, Chongqing 400715, China.
Huan ChenCollege of Science, National University of Defense Technology, Changsha 410073, China.
Junbo YangCollege of Science, National University of Defense Technology, Changsha 410073, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metalenses, with their compact form factor and unique optical capabilities, hold tremendous potential for advancing computer vision applications. In this work, we propose a high-resolution, large field-of-view (FOV) metalens intelligent recognition system, combining the latest YOLO framework, aimed at supporting a range of vision tasks. Specifically, we demonstrate its effectiveness in scanning, pose recognition, and object classification. The metalens we designed to achieve a 100° FOV while operating near the diffraction limit, as confirmed by experimental results. Moreover, the metalenses weigh only 0.1 g and occupy a compact volume of 0.04 cm

Indexed as

computer visionidentificationimaginglarge field of viewmetalens

Identifiers

PMID39967773
PMCPMC11831395

What OpenQuestion holds

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LicenceCC BY
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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.