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arXiv 2404.12751cs.HCcs.GR
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沉浸式分析:增强现实中X射线计算机断层扫描数据集材料检测的增强

Immersive Analysis: Enhancing Material Inspection of X-Ray Computed Tomography Datasets in Augmented Reality

Alexander Gall, Anja Heim, Patrick Weinberger, Bernhard Fröhler, Johann Kastner, Christoph Heinzl

AI总结:

本文提出一种增强现实框架,将XCT空间无损检测数据与真实物体叠加,实现现场材料分析,案例验证显示专家认可其直观性和交互性。

摘要:

这项工作介绍了一种新颖的增强现实(AR)方法,用于将材料数据与真实物体并排可视化,以促进基于空间无损检测(NDT)数据的详细材料分析,这些数据由X射线计算机断层扫描(XCT)成像生成。为此,我们引入了一个框架,利用AR设备、可视化和交互技术的潜力,无缝地探索与真实世界物体匹配的复杂初级和次级XCT数据。所提出的分析方案的总体目标是使研究人员和分析人员能够在现场和原位检查材料属性和结构。将沉浸式可视化技术与真实物理物体相结合,使得材料分析和检测的工作流程高度直观,从而能够识别异常并加速知情决策。因此,该框架产生了一种沉浸式体验,提供了更具吸引力和更自然的材料数据分析。一项关于纤维增强聚合物数据集案例研究用于验证AR框架及其新工作流程。初步结果显示专家反馈积极,特别是在改善空间数据理解和与材料样本更自然交互方面,这可能在与传统分析系统结合时具有显著潜力。

英文摘要:

This work introduces a novel Augmented Reality (AR) approach to visualize material data alongside real objects in order to facilitate detailed material analyses based on spatial non-destructive testing (NDT) data as generated in X-ray computed tomography (XCT) imaging. For this purpose, we introduce a framework that leverages the potential of AR devices, visualization and interaction techniques to seamlessly explore complex primary and secondary XCT data matched with real-world objects. The overall goal of the proposed analysis scheme is to enable researchers and analysts to inspect material properties and structures onsite and in-place. Coupling immersive visualization techniques with real physical objects allows for highly intuitive workflows in material analysis and inspection, which enables the identification of anomalies and accelerates informed decision making. As a result, this framework generates an immersive experience, which provides a more engaging and more natural analysis of material data. A case study on fiber-reinforced polymer datasets was used to validate the AR framework and its new workflow. Initial results revealed positive feedback from experts, in particular regarding improved understanding of spatial data and a more natural interaction with material samples, which may have significant potential when combined with conventional analysis systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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