Investigating the Characteristics and Performance of Augmented Reality Applications on Head-Mounted Displays: A Study of the Hololens Application Store
March 13, 2023 Β· Declared Dead Β· π 2023 IEEE International Conference on Communications Workshops (ICC Workshops)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pubudu Wijesooriya, Sheikh Muhammad Farjad, Nikolaos Stergiou, Spyridon Mastorakis
arXiv ID
2303.07523
Category
cs.MM: Multimedia
Cross-listed
cs.PF,
cs.SE
Citations
2
Venue
2023 IEEE International Conference on Communications Workshops (ICC Workshops)
Last Checked
3 months ago
Abstract
Augmented Reality (AR) based on Head-Mounted Displays (HMDs) has gained significant traction over the recent years. Nevertheless, it remains unclear what AR HMD-based applications have been developed over the years and what their system performance is when they are run on HMDs. In this paper, we aim to shed light into this direction. Our study focuses on the applications available on the Microsoft Hololens application store given the wide use of the Hololens headset. Our study has two major parts: (i) we collect metadata about the applications available on the Microsoft Hololens application store to understand their characteristics (e.g., categories, pricing, permissions requested, hardware and software compatibility); and (ii) we interact with these applications while running on a Hololens 2 headset and collect data about systems-related metrics (e.g., memory and storage usage, time spent on CPU and GPU related operations) to investigate the systems performance of applications. Our study has resulted in several interesting findings, which we share with the research community.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted