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There is a lack of datasets for visual-inertial odometry applications in Extended Reality (XR). To the best of our knowledge, there is no dataset available that is captured from an XR headset with a human as a carrier. To bridge this gap, we present a novel pose estimation dataset --- called HoloSet --- collected using Microsoft Hololens 2, which is a state-of-the-art head mounted device for XR. Potential applications for HoloSet include visual-inertial odometry, simultaneous localization and mapping (SLAM), and additional applications in XR that leverage visual-inertial data. HoloSet captures both macro and micro movements. For macro movements, the dataset consists of more than 66,000 samples of visual, inertial, and depth camera data in a variety of environments (indoor, outdoor) and scene setups (trails, suburbs, downtown) under multiple user action scenarios (walk, jog). For micro movements, the dataset consists of more than 12,000 samples of additional articulated hand depth camera images while a user plays games that exercise fine motor skills and hand-eye coordination. We present basic visualizations and high-level statistics of the data and outline the potential research use cases for HoloSet.
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This content will become publicly available on October 21, 2025
An XR GUI for Visualizing Messages in ECS Architectures
Entity–Component–System (ECS) architectures are fundamental to many systems for developing extended reality (XR) applications. These applications often contain complex scenes and require intricately connected application logic to connect components together, making debugging and analysis difficult. Graph-based tools have been created to show actions in ECS-based scene hierarchies, but few address interactions that go beyond traditional hierarchical communication. To address this, we present an XR GUI for Mercury (a toolkit to handle cross-component ECS communication) that allows developers to view and edit relationships and interactions between scene entities in Mercury.
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- Award ID(s):
- 2037101
- PAR ID:
- 10563417
- Publisher / Repository:
- IEEE International Symposium on Mixed and Augmented Reality Adjunct 2024
- Date Published:
- ISBN:
- 979-8-3315-0691-9
- Page Range / eLocation ID:
- 640 to 641
- Subject(s) / Keyword(s):
- Visualization–Visualization systems and tools–Visualization toolkits Human–computer interaction (HCI)–Interaction paradigms–Virtual reality.
- Format(s):
- Medium: X
- Location:
- Bellevue, WA, USA
- Sponsoring Org:
- National Science Foundation
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