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Cited by 956

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

High-speed 3D shape measurement with structured light methods: A review

Open access
Zhang, Song·2018·Optics and Lasers in Engineering

Abstract

Abstract High-speed 3D shape measurement (or imaging) has seen tremendous growths over the past decades, especially the past few years due to the improved speed of computing devices and reduced costs of hardware components. 3D shape measurement technologies have started penetrating more into our daily lives than ever before with the recent release of iPhone X that has an built-in 3D sensor for Face ID, along with prior commercial success of inexpensive commercial sensors (e.g., Microsoft Kinect). This paper overviews the primary state-of-the-art 3D shape measurement techniques based on structured light methods, especially those that could achieve high measurement speed and accuracy. The fundamental principles behind those technologies will be elucidated, experimental results will be presented to demonstrate capabilities and/or limitations for those popular techniques, and finally present our perspectives on those remaining challenges to be conquered to make advanced 3D shape measurement techniques ubiquitous.

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Crossref · 10.1016/j.optlaseng.2018.02.017

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Metadata

Authors
Zhang, S.
Venue
Optics and Lasers in Engineering
Publisher
Elsevier BV
Published
2018-07
Volume
106
Pages
119-131
ISSN
0143-8166 (ISSN-L)
OpenAlex
W2791731233
Cited by
956
References
56
FWCI
39.13
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: High-speed 3D shape measurement with structured light methods: A review. https://app.cochat.ai/papers/10.1016/j.optlaseng.2018.02.017
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Zhang, S. (2018). High-speed 3D shape measurement with structured light methods: A review. Optics and Lasers in Engineering. https://doi.org/10.1016/j.optlaseng.2018.02.017

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.