Learning-based 3D imaging from single structured-light image

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models Pub Date : 2023-04-01 DOI:10.1016/j.gmod.2023.101171

Andrew-Hieu Nguyen , Olivia Rees , Zhaoyang Wang

引用次数: 5

Abstract

Integrating structured-light technique with deep learning for single-shot 3D imaging has recently gained enormous attention due to its unprecedented robustness. This paper presents an innovative technique of supervised learning-based 3D imaging from a single grayscale structured-light image. The proposed approach uses a single-input, double-output convolutional neural network to transform a regular fringe-pattern image into two intermediate quantities which facilitate the subsequent 3D image reconstruction with high accuracy. A few experiments have been conducted to demonstrate the validity and robustness of the proposed technique.

Abstract Image

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基于学习的单结构光图像三维成像

将结构光技术与深度学习相结合用于单次3D成像最近因其前所未有的鲁棒性而受到极大关注。本文提出了一种创新的基于监督学习的单灰度结构光图像三维成像技术。所提出的方法使用单输入、双输出卷积神经网络将规则条纹图案图像转换为两个中间量，这有助于后续高精度的3D图像重建。已经进行了一些实验来证明所提出的技术的有效性和鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.