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Mobile augmented reality (AR) has the potential to enable immersive, natural interactions between humans and cyber-physical systems. In particular markerless AR, by not relying on fiducial markers or predefined images, provides great convenience and flexibility for users. However, unwanted virtual object movement frequently occurs in markerless smartphone AR due to inaccurate scene understanding, and resulting errors in device pose tracking. We examine the factors which may affect virtual object stability, design experiments to measure it, and conduct systematic quantitative characterizations across six different user actions and five different smartphone configurations. Our study demonstrates noticeable instances of spatial instability in virtualmore »Free, publicly-accessible full text available May 2, 2023
Environmental, User, and Social Context-Aware Augmented Reality for Supporting Personal Development and ChangeRobust pervasive context-aware augmented reality (AR) has the potential to enable a range of applications that support users in reaching their personal and professional goals. In such applications, AR can be used to deliver richer, more immersive, and more timely just in time adaptive interventions (JITAI) than conventional mobile solutions, leading to more effective support of the user. This position paper defines a research agenda centered on improving AR applications' environmental, user, and social context awareness. Specifically, we argue for two key architectural approaches that will allow pushing AR context awareness to the next level: use of wearable and Internetmore »Free, publicly-accessible full text available March 13, 2023
Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth,more »Free, publicly-accessible full text available March 1, 2023
Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR withmore »Free, publicly-accessible full text available February 1, 2023