世界太大了,无法下载:世界规模增强现实的3D模型检索

Yi-Zhen Tsai, James Luo, Yunshu Wang, Jiasi Chen
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引用次数: 0

摘要

世界规模增强现实(AR)是增强现实的一种形式,用户可以在现实世界中移动,在特定位置观看3D模型并与之交互。然而,考虑到世界规模AR的地理规模,在设备上预先获取和存储大量高质量的3D模型是不可实现的。例如,从一个城市的所有店面下载并存储3D广告到一个设备上是不可能的。因此,一个关键的挑战是决定哪些远程存储的3D模型应该从边缘服务器提取到AR设备上,以便在显示器上及时渲染它们,同时具有高视觉质量。在这项工作中,我们提出了一个3D模型检索框架,该框架可以智能地决定何时提取哪种质量的3D模型。优化决策是基于质量压缩权衡、网络带宽以及AR用户接下来可能查看的3D模型的预测。为了支持我们的框架,我们收集了AR用户玩世界规模AR游戏的真实世界痕迹,并用它来驱动我们的模拟和预测模块。我们的研究结果表明,与在用户固定距离内预取模型的基线方法相比,所提出的框架可以实现更高的3D模型视觉质量,同时错过更少的显示截止日期(减少20%),浪费更少的字节(减少10倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The World is Too Big to Download: 3D Model Retrieval for World-Scale Augmented Reality
World-scale augmented reality (AR) is a form of AR where users move around the real world, viewing and interacting with 3D models at specific locations. However, given the geographical scale of world-scale AR, pre-fetching and storing numerous high-quality 3D models locally on the device is infeasible. For example, it would be impossible to download and store 3D ads from all the storefronts in a city onto a single device. A key challenge is thus deciding which remotely-stored 3D models should be fetched onto the AR device from an edge server, in order to render them in a timely fashion - yet with high visual quality - on the display. In this work, we propose a 3D model retrieval framework that makes intelligent decisions of which quality of 3D models to fetch, and when. The optimization decision is based on quality-compression tradeoffs, network bandwidth, and predictions of which 3D models the AR user is likely to view next. To support our framework, we collect real-world traces of AR users playing a world-scale AR game, and use this to drive our simulation and prediction modules. Our results show that the proposed framework can achieve higher visual quality of the 3D models while missing fewer display deadlines (by 20%) and wasting fewer bytes (by 10x), compared to a baseline approach of pre-fetching models within a fixed distance of the user.
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