SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

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 F1 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.Comment: To appear in Proceedings of IEEE ISMAR 202

Ambient Intelligence for Next-Generation AR

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.Comment: This is a preprint of a book chapter which will appear in the Springer Handbook of the Metavers

Here To Stay: A Quantitative Comparison of Virtual Object Stability in Markerless Mobile AR

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.Peer reviewe

InDepth: Real-time Depth Inpainting for Mobile Augmented Reality

Peer reviewe

EyeSyn: Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition

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.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Embedded System

Demo Abstract: Catch My Eye: Gaze-Based Activity Recognition in an Augmented Reality Art Gallery

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.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Embedded System