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.