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  1. Free, publicly-accessible full text available February 25, 2025
  2. Free, publicly-accessible full text available December 1, 2024
  3. 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 virtual objects in all but the simplest settings (with position errors of greater than 10cm even on the best-performing smartphones), and underscores the need for further enhancements to pose tracking algorithms for smartphone-based markerless AR. 
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  4. null (Ed.)
    Mobile Augmented Reality (AR) provides immersive experiences by aligning virtual content (holograms) with a view of the real world. When a user places a hologram it is usually expected that like a real object, it remains in the same place. However, positional errors frequently occur due to inaccurate environment mapping and device localization, to a large extent determined by the properties of natural visual features in the scene. In this demonstration we present SceneIt, the first visual environment rating system for mobile AR based on predictions of hologram positional error magnitude. SceneIt allows users to determine if virtual content placed in their environment will drift noticeably out of position, without requiring them to place that content. It shows that the severity of positional error for a given visual environment is predictable, and that this prediction can be calculated with sufficiently high accuracy and low latency to be useful in mobile AR applications. 
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