Enhancements to VTK enabling scientific visualization in immersive environments

O’Leary, Patrick; Jhaveri, Sankhesh; Chaudhary, Aashish; Sherman, William R.; Martin, Ken; Lonie, David; Whiting, Eric; Money, James H.; McKenzie, Sandy

doi:10.1109/vr.2017.7892246

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…For scientific visualisation, ParaView [1] and VTK [41] have been used to create high-quality large-volume isosurface, and flow data visualisation. While successful in medical and engineering applications, they do not facilitate the simple creation of information visualisations such as scatterplots, matrices, and trajectories.…”

Section: Immersive Applications and Visualisation Toolkitsmentioning

confidence: 99%

IATK: An Immersive Analytics Toolkit

Cordeil

Cunningham

Bach

et al. 2019

2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

112

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Section: Immersive Applications and Visualisation Toolkitsmentioning

confidence: 99%

IATK: An Immersive Analytics Toolkit

Cordeil

Cunningham

Bach

et al. 2019

2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

112

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“…The VTK library enables the creation of virtual and augmented reality environments. The developers themselves explain in the paper [19] which are the guidelines to follow in order to avoid problems in development. It is also a library widely used by the scientific community to visualise virtual reality simulations [20].…”

Section: Virtual Reality Integrationmentioning

confidence: 99%

Anomaly detection and virtual reality visualisation in supercomputers

Pérez

Benavent-Lledo²,

Azorín-López³

et al. 2022

Preprint

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Anomaly detection is the identification of events or observations that deviate from the expected behaviour of a given set of data. Its main application is the prediction of possible technical failures. In particular, anomaly detection on supercomputers is a difficult problem to solve due to the large scale of the systems and the large number of components. Most research works in this field employ machine learning methods and regression models in a supervised fashion, which implies the need for a large amount of labelled data to train such systems. This work proposes the use of autoencoder models, allowing the problem to be approached with semi-supervised learning techniques. Two different model training approaches are compared. The former is a model trained with data from all the nodes of a supercomputer. In the latter approach, observing significant differences between nodes, one model is trained for each node. The results are analysed by evaluating the positive and negative aspects of each approach. On the other hand, a replica of the Marconi 100 supercomputer is developed in a virtual reality environment that allows the data from each node to be visualised at the same time.

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“…Dhillon 等 [16] 在虚拟现实环境下实现了仿生俯仰板的涡尾流可视化, 支持用户分析尾流中漩涡的形成和脱落以及特定时间内的流量参数关系. O'Leary 等 [17] 在可视化工具库(visualization toolkit, VTK)的基础上, 提出了支持 HMD 的可视化方法, 使 VTK 具备沉浸式虚拟现实功能, 帮助研究人员进行沉浸式虚拟现实科学可视化研究. Lu 等 [18] 设计了基于虚拟现实和手势交互技术的气候矢量场数据感知系统, 帮助用户更有效地理解和分析数据.…”

Section: 虚拟现实流场可视化技术unclassified

Immersive Virtual Reality Collaboration Framework for Flow Field Visualization

Zheng¹,

Yang²,

Han³

et al. 2021

J Comput-Aided Design Comput Graphics

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面向流场可视化的沉浸式虚拟现实协作框架郑志文 1,2) , 杨超 2,3) , 韩永国 1) , 杨洋 4)* , 吴亚东 5) , 王昉 2,3) , 张晓蓉 1) 1) (西南科技大学计算机科学与技术学院绵阳 621010) 2) (中国空气动力研究与发展中心计算空气动力研究所绵阳 621000) 3) (空气动力学国家重点实验室绵阳 621000) 4) (四川省计算机研究院成都 610041) 5) (四川轻化工大学计算机科学与工程学院自贡 643002)

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Enhancements to VTK enabling scientific visualization in immersive environments

Cited by 16 publications

References 27 publications

IATK: An Immersive Analytics Toolkit

IATK: An Immersive Analytics Toolkit

Anomaly detection and virtual reality visualisation in supercomputers

Immersive Virtual Reality Collaboration Framework for Flow Field Visualization

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