Evidence map›Paper›PMID 41540081›Full record

ArticleScientific reports2026

AI and AR based digital reconstruction of Liangshan Yi lacquerware.

Xin Tang, Chaobi Zhan, Changjie Tang, Sungpil Kim

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

4 authors.

Xin TangDepartment of Industrial Design, Pukyong National University, Busan, 48513, Republic of Korea. tangxin1998@pukyong.ac.kr.
Chaobi ZhanDepartment of Industrial Design, Pukyong National University, Busan, 48513, Republic of Korea.
Changjie TangDepartment of Material Engineering, Jinling Institute of Technology, Nanjing, 211169, China.
Sungpil KimDepartment of Industrial Design, Pukyong National University, Busan, 48513, Republic of Korea. skim32@pknu.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lacquering technique of Yi lacquerware, a national intangible cultural heritage object (ICHO) of China, is distinguished by its unique forms, patterns, and colors, embodying profound religious beliefs and aesthetic connotations. Currently, this technique faces challenges including disrupted heritage transmission, insufficient scientific analysis of lacquer color composition, and limited methods of digital representation, underscoring the urgent need for preservation and promotion within the broader context of sustainable cultural development. This study, based on a field investigation of Liangshan Yi Lacquerware (LYL), presents a comprehensive analysis of its visual elements and employs instrumental techniques to identify the pigments used in its coloration. Representative lacquerware pieces were selected to produce two-dimensional (2D) images, which were then used for intelligent modeling on an artificial intelligence (AI) platform and integrated into an augmented reality (AR) platform to enable a mobile interactive experience. This approach serves as an initial validation of the workflow's adaptability and potential for broader application in digital reconstruction techniques, providing both theoretical support and a practical model for transforming traditional visual language into immersive visualization, while contributing to the long-term sustainable preservation of intangible cultural heritage.

Indexed as

Cultural sustainabilityDigital reconstructionICHLacquering craftYi lacquerware

Identifiers

PMID41540081
PMCPMC12891663

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.