交互式多用户3D视觉分析（multimedia）

• 2020 年 2 月 15 日
• 笔记

本出版物报道了一个研究项目，我们着手探索增强现实(AR)技术在视觉数据分析方面的优势和劣势。我们开发了一个AR数据分析应用的原型，它为用户提供了一个交互式的3D界面，基于手势的控制和多用户共享体验的支持，使多人在三维空间中协同可视化、分析和操作具有高维特征的数据。我们的软件原型，叫做DataCube，运行在微软的HoloLens上。HoloLens是第一批真正的独立增强现实耳机之一，通过它，用户可以看到计算机生成的图像覆盖在用户物理环境中的真实物体上。通过手势，用户可以选择菜单选项，控制具有各种过滤和可视化功能的三维数据可视化，并在自己的环境中自由安排各种菜单和虚拟显示。共享的多用户体验允许所有参与的用户查看虚拟环境并与之交互，一个用户所做的更改将立即对其他用户可见。当用户一起参与时，他们不受同时观察物理世界的限制，因此他们也可以看到非语言的暗示，如在物理环境中其他用户的手势或面部反应。本研究项目的主要目的是研究AR接口和协同分析是否能为数据分析任务提供有效的解决方案，我们原型系统的经验证实了这一点。

原文题目：Interactive Multi-User 3D Visual Analytics in Augmented Reality

原文：This publication reports on a research project in which we set out to explore the advantages and disadvantages augmented reality (AR) technology has for visual data analytics. We developed a prototype of an AR data analytics application, which pro- vides users with an interactive 3D interface, hand gesture-based controls and multi-user support for a shared experience, enabling multiple people to collaboratively visualize, analyze and manipulate data with high dimensional features in 3D space. Our soft- ware prototype, called DataCube, runs on the Microsoft HoloLens – one of the first true stand-alone AR headsets, through which users can see computer-generated images overlaid onto real- world objects in the user’s physical environment. Using hand gestures, the users can select menu options, control the 3D data visualization with various filtering and visualization functions, and freely arrange the various menus and virtual displays in their environment. The shared multi-user experience allows all participating users to see and interact with the virtual environment, changes one user makes will become visible to the other users instantly. As users engage together they are not restricted from observing the physical world simultaneously and therefore they can also see non-verbal cues such as gesturing or facial reactions of other users in the physical environment. The main objective of this research project was to find out if AR interfaces and collaborative analysis can provide an effective solution for data analysis tasks, and our experience with our prototype system confirms this.

原文作者：Wanze Xie, Yining Liang, Janet Johnson, Andrea Mower, Samuel Burns, Colleen Chelini, Paul D Alessandro, Nadir Weibel, Jürgen P. Schulze

原文链接：https://arxiv.org/abs/2002.05305