互動式多用戶3D視覺分析（multimedia）
- 2020 年 2 月 15 日
- 筆記
本出版物報道了一個研究項目，我們著手探索增強現實(AR)技術在視覺數據分析方面的優勢和劣勢。我們開發了一個AR數據分析應用的原型，它為用戶提供了一個互動式的3D介面，基於手勢的控制和多用戶共享體驗的支援，使多人在三維空間中協同可視化、分析和操作具有高維特徵的數據。我們的軟體原型，叫做DataCube，運行在微軟的HoloLens上。HoloLens是第一批真正的獨立增強現實耳機之一，通過它，用戶可以看到電腦生成的影像覆蓋在用戶物理環境中的真實物體上。通過手勢，用戶可以選擇菜單選項，控制具有各種過濾和可視化功能的三維數據可視化，並在自己的環境中自由安排各種菜單和虛擬顯示。共享的多用戶體驗允許所有參與的用戶查看虛擬環境並與之交互，一個用戶所做的更改將立即對其他用戶可見。當用戶一起參與時，他們不受同時觀察物理世界的限制，因此他們也可以看到非語言的暗示，如在物理環境中其他用戶的手勢或面部反應。本研究項目的主要目的是研究AR介面和協同分析是否能為數據分析任務提供有效的解決方案，我們原型系統的經驗證實了這一點。
原文題目：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
原文鏈接：https://arxiv.org/abs/2002.05305
