基于跨域学习的自适应结构光三维表面成像（激光光子学，19(12)/2025）

IF 9.8 1区物理与天体物理 Q1 OPTICS

Laser & Photonics Reviews Pub Date : 2025-06-17 DOI:10.1002/lpor.202570047

Xinsheng Li, Shijie Feng, Wenwu Chen, Ziheng Jin, Qian Chen, Chao Zuo

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引用次数: 0

摘要

本文由冯世杰、陈茜、左超等人（论文号：2401609）提出了一种用于自适应结构光三维成像的跨域学习框架；它增强了跨不同系统和环境的泛化。该方法采用了专家混合架构，显著提高了传统专家和通才dnn的性能，并推进了强大的人工智能驱动光学计量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Adaptive Structured-Light 3D Surface Imaging with Cross-Domain Learning (Laser Photonics Rev. 19(12)/2025)

Enhancing Structured-Light 3D Imaging

A cross-domain learning framework for adaptive structured-light 3D imaging is proposed by Shijie Feng, Qian Chen, Chao Zuo, and co-workers in article number 2401609; it enhances generalization across diverse systems and environments. The method incorporates a mixture-of-experts architecture, significantly improving performance over traditional specialist and generalist DNNs, and advancing robust AI-driven optical metrology.

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来源期刊

Laser & Photonics Reviews 物理-光学

CiteScore

14.20

自引率

5.50%

发文量

314

审稿时长

2 months

期刊介绍： Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications. As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics. The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.