In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types of task-detrimental virtual content: obstruction attacks, in which virtual content prevents users from seeing real-world objects, and information manipulation attacks, in which virtual content interferes with users' ability to accurately interpret real-world information. We provide a mathematical framework to characterize these attacks and create a custom open-source dataset for attack evaluation. To address these attacks, we introduce ViDDAR (Vision language model-based Task-Detrimental content Detector for Augmented Reality), a comprehensive full-reference system that leverages Vision Language Models (VLMs) and advanced deep learning techniques to monitor and evaluate virtual content in AR environments, employing a user-edge-cloud architecture to balance performance with low latency. To the best of our knowledge, ViDDAR is the first system to employ VLMs for detecting task-detrimental content in AR settings. Our evaluation results demonstrate that ViDDAR effectively understands complex scenes and detects task-detrimental content, achieving up to 92.15% obstruction detection accuracy with a detection latency of 533 ms, and an 82.46% information manipulation content detection accuracy with a latency of 9.62 s.

Augmented reality (AR) platforms now support persistent, markerless experiences, in which virtual content appears in the same place relative to the real world, across multiple devices and sessions. However, optimizing environments for these experiences remains challenging; virtual content stability is determined by the performance of device pose tracking, which depends on recognizable environment features, but environment texture can impair human perception of virtual content. Low-contrast 'invisible textures' have recently been proposed as a solution, but may result in poor tracking performance when combined with dynamic device motion. Here, we examine the use of invisible textures in detail, starting with the first evaluation in a realistic AR scenario. We then consider scenarios with more dynamic device motion, and conduct extensive game engine-based experiments to develop a method for optimizing invisible textures. For texture optimization in real environments, we introduce MoMAR, the first system to analyze motion data from multiple AR users, which generates guidance using situated visualizations. We show that MoMAR can be deployed while maintaining an average frame rate > 59fps, for five different devices. We demonstrate the use of MoMAR in a realistic case study; our optimized environment texture allowed users to complete a task significantly faster (p=0.003) than a complex texture.

In this work, we introduce SEESys, the first system to provide online pose error estimation for Simultaneous Localization and Mapping (SLAM). Unlike prior offline error estimation approaches, the SEESys framework efficiently collects real-time system features and delivers accurate pose error magnitude estimates with low latency. This enables real-time quality-of-service information for downstream applications. To achieve this goal, we develop a SLAM system run-time status monitor (RTS monitor) that performs feature collection with minimal overhead, along with a multi-modality attention-based Deep SLAM Error Estimator (DeepSEE) for error estimation. We train and evaluate SEESys using both public SLAM benchmarks and a diverse set of synthetic datasets, achieving an RMSE of 0.235 cm of pose error estimation, which is 15.8% lower than the baseline. Additionally, we conduct a case study showcasing SEESys in a real-world scenario, where it is applied to a real-time audio error advisory system for human operators of a SLAM-enabled device. The results demonstrate that SEESys provides error estimates with an average end-to-end latency of 37.3 ms, and the audio error advisory reduces pose tracking error by 25%.

In Augmented Reality (AR), improper virtual content placement can obstruct real-world elements, causing confusion and degrading the experience. To address this, we present LOBSTAR (Language model-based OBSTruction detection for Augmented Reality), the first system leveraging a vision language model (VLM) to detect key objects and prevent obstructions in AR. We evaluated LOBSTAR using both real-world and virtual-scene images and developed a mobile app for AR content obstruction detection. Our results demonstrate that LOBSTAR effectively understands scenes and detects obstructive content with well-designed VLM prompts, achieving up to 96% accuracy and a detection latency of 580ms on a mobile app.

3D object detection (OD) is a crucial element in scene understanding. However, most existing 3D OD models have been tailored to work with light detection and ranging (LiDAR) and RGB-D point cloud data, leaving their performance on commonly available visual-inertial simultaneous localization and mapping (VI-SLAM) point clouds unexamined. In this paper, we create and release two datasets: VIP500, 4772 VI-SLAM point clouds covering 500 different object and environment configurations, and VIP500-D, an accompanying set of 20 RGB-D point clouds for the object classes and shapes in VIP500. We then use these datasets to quantify the differences between VI-SLAM point clouds and dense RGB-D point clouds, as well as the discrepancies between VI-SLAM point clouds generated with different object and environment characteristics. Finally, we evaluate the performance of three leading OD models on the diverse data in our VIP500 dataset, revealing the promise of OD models trained on VI-SLAM data; we examine the extent to which both object and environment characteristics impact performance, along with the underlying causes.

Augmented reality (AR) simulations in virtual reality (VR) provide fully controlled conditions, fewer hardware limitations than on AR devices, and convenient and safe access to diverse settings. However, for VR to be a fully realistic simulator for optical see-through (OST) AR, it must replicate the effects of environmental context on virtual content appearance and system performance. Here we examine one example of this, the perceived transparency of virtual content under varying environment illuminance, and conduct a user study (N=8) to identify the discrepancy between AR and a standard VR simulation. Our results show that for virtual content designed to be transparent, perceived transparency in AR is greatly reduced at low levels of illuminance, but remains consistently high across all illuminance levels tested in VR. This illustrates the impact of environment properties on the efficacy of AR simulations in VR, and motivates the development of context-aware simulations that more closely replicate AR experiences.

Simultaneous localization and mapping (SLAM) takes in sensor data, e.g., camera frames, and estimates the user's trajectory while creating a map of the surrounding environment. However, existing SLAM evaluation methods are not reference-free, requiring ground-truth trajectories collected from external systems that are infeasible for most scenarios. In this demo, we present Deep SLAM Error Estimator (DeepSEE), a framework that collects features from a standard visual SLAM pipeline as multivariate time series and uses an attention-based neural network to estimate the tracking error at run time. We evaluate DeepSEE in a game engine-based virtual environment, which generates the visual input for DeepSEE and provides the ground-truth trajectory. Demo participants can navigate the virtual environment to create their own trajectories and view the online pose error estimation. This demo showcases how DeepSEE can act as a quality-of-service indicator for downstream applications.

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average FI score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next, we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.

Mobile augmented reality (AR) has a wide range of promising applications, but its efficacy is subject to the impact of environment texture on both machine and human perception. Performance of the machine perception algorithm underlying accurate positioning of virtual content, visual-inertial SLAM (VI-SLAM), is known to degrade in low-texture conditions, but there is a lack of data in realistic scenarios. We address this through extensive experiments using a game engine-based emulator, with 112 textures and over 5000 trials. Conversely, human task performance and response times in AR have been shown to increase in environments perceived as textured. We investigate and provide encouraging evidence for invisible textures, which result in good VI-SLAM performance with minimal impact on human perception of virtual content. This arises from fundamental differences between VI-SLAM-based machine perception, and human perception as described by the contrast sensitivity function. Our insights open up exciting possibilities for deploying ambient IoT devices that display invisible textures, as part of systems which automatically optimize AR environments.

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.

In order to provide high quality augmented reality (AR) experiences, environmental conditions such as light level must be conducive to a high level of virtual content stability, accurate eye tracking and good virtual content visibility. This is challenging due to the dynamic nature of environmental conditions and AR application requirements. In this poster we present the first automatic environment optimization system for AR which adapts to both environment lighting and texture. First we conduct experiments that demonstrate the effect of light level on virtual object stability and eye tracking. We then detail our edge computing system architecture which uses IoT devices to sense and adjust environment conditions, and lays the foundation for future environment-aware AR applications.

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.

The personalization of augmented reality (AR) experiences based on environmental and user context is key to unlocking their full potential. The recent addition of eye tracking to AR headsets provides a convenient method for detecting user context, but complex analysis of raw gaze data is required to detect where a user's attention and thoughts truly lie. In this demo we present Catch My Eye, the first system to incorporate deep neural network (DNN)-based activity recognition from user gaze into a realistic mobile AR app. We develop an edge computing-based architecture to offload context computation from resource-constrained AR devices, and present a working example of content adaptation based on user context, for the scenario of a virtual art gallery. It shows that user activities can be accurately recognized and employed with sufficiently low latency for practical AR applications.

Demand is growing for markerless augmented reality (AR) experiences, but designers of the real-world spaces that host them still have to rely on inexact, qualitative guidelines on the visual environment to try and facilitate accurate pose tracking. Furthermore, the need for visual texture to support markerless AR is often at odds with human aesthetic preferences, and understanding how to balance these competing requirements is challenging due to the siloed nature of the relevant research areas. To address this, we present an integrated design methodology for AR spaces, that incorporates both tracking and human factors into the design process. On the tracking side, we develop the first VI-SLAM evaluation technique that combines the flexibility and control of virtual environments with real inertial data. We use it to perform systematic, quantitative experiments on the effect of visual texture on pose estimation accuracy; through 2000 trials in 20 environments, we reveal the impact of both texture complexity and edge strength. On the human side, we show how virtual reality (VR) can be used to evaluate user satisfaction with environments, and highlight how this can be tailored to AR research and use cases. Finally, we demonstrate our integrated design methodology with a case study on AR museum design, in which we conduct both VI-SLAM evaluations and a VR-based user study of four different museum environments.

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, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.

Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from dif-ferent measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorpo-rated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.

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, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.

Meditation, a mental and physical exercise which helps to focus attention and reduce stress has gained more popularity in recent years. However, meditation requires a concerted effort and regular practice. To explore the feasibility of using Augmented Reality(AR) Devices to assist in meditating, we recruited ten subjects to perform a five-minute meditation task integrated into AR devices. Heart Rate, Heart Rate Variability, and skin conductance response(SCR) are analyzed based on an Electrocardiogram(ECG), Electrodermal activity to monitor the physiological changes during and after a meditation session. Additionally, participants filled out surveys containing the Perceived Stress Questionnaire (PSQ), a clinically validated survey designed to evaluate stress levels before and after meditation to analyze the change in stress levels. Finally, we found significant differences in Heart Rate and Mean SCR Recovery Time for participants between the three study procedure periods (before, during, and after guided meditation).

In order for markerless augmented reality (AR) to reach its full potential in educational settings it must be able to adapt to the wide range of possible environments, devices and user cognitive states that affect learning outcomes. Shared educational spaces, such as classrooms, art galleries, museums, teaching hospitals and wildlife centers present enticing opportunities in terms of specialized AR applications, existing infrastructure to leverage, and large numbers of AR sessions to gather data on. In this work we make feasible the challenging concept of context-aware AR through the use of a `local expert', which learns the optimal configuration of AR algorithms and virtual content for the specific educational space and use case for which it is implemented. To compute insights from multiple AR devices, enable timely responses to fast-changing user cognitive states, and ensure the security of sensitive user data, we propose an edge-computing architecture, in which storage and computation related to our local expert is performed on a server on the same local area network as the mobile AR devices.