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使用HoloLens的增强现实交互式分子图形

Interactive Molecular Graphics for Augmented Reality Using HoloLens.

作者信息

Müller Christoph, Krone Michael, Huber Markus, Biener Verena, Herr Dominik, Koch Steffen, Reina Guido, Weiskopf Daniel, Ertl Thomas

机构信息

Visualisation Research Centre (VISUS), University of Stuttgart, 70569 Stuttgart, Germany.

Institute for Visualisation and Interactive Systems (VIS), University of Stuttgart, 70569 Stuttgart, Germany.

出版信息

J Integr Bioinform. 2018 Jun 13;15(2):20180005. doi: 10.1515/jib-2018-0005.

Abstract

Immersive technologies like stereo rendering, virtual reality, or augmented reality (AR) are often used in the field of molecular visualisation. Modern, comparably lightweight and affordable AR headsets like Microsoft's HoloLens open up new possibilities for immersive analytics in molecular visualisation. A crucial factor for a comprehensive analysis of molecular data in AR is the rendering speed. HoloLens, however, has limited hardware capabilities due to requirements like battery life, fanless cooling and weight. Consequently, insights from best practises for powerful desktop hardware may not be transferable. Therefore, we evaluate the capabilities of the HoloLens hardware for modern, GPU-enabled, high-quality rendering methods for the space-filling model commonly used in molecular visualisation. We also assess the scalability for large molecular data sets. Based on the results, we discuss ideas and possibilities for immersive molecular analytics. Besides more obvious benefits like the stereoscopic rendering offered by the device, this specifically includes natural user interfaces that use physical navigation instead of the traditional virtual one. Furthermore, we consider different scenarios for such an immersive system, ranging from educational use to collaborative scenarios.

摘要

诸如立体渲染、虚拟现实或增强现实(AR)等沉浸式技术常用于分子可视化领域。像微软的HoloLens这样现代、相对轻便且价格实惠的AR头戴设备为分子可视化中的沉浸式分析开辟了新的可能性。AR中对分子数据进行全面分析的一个关键因素是渲染速度。然而,由于诸如电池续航、无风扇散热和重量等要求,HoloLens的硬件能力有限。因此,强大桌面硬件的最佳实践经验可能无法适用。所以,我们评估了HoloLens硬件对于分子可视化中常用的空间填充模型的现代、支持GPU的高质量渲染方法的能力。我们还评估了大型分子数据集的可扩展性。基于这些结果,我们讨论了沉浸式分子分析的思路和可能性。除了该设备提供的立体渲染等更明显的优势外,这特别包括使用物理导航而非传统虚拟导航的自然用户界面。此外,我们考虑了这种沉浸式系统的不同场景,从教育用途到协作场景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37e1/6167047/106da95f73d1/jib-15-20180005-g001.jpg

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