Yiqin Zhao
Tian Guo
ABSTRACT
The recent advancement in computer vision and XR hardware has ignited the community's interest in AR systems research. Similar to traditional systems research, the evaluation of AR systems involves capturing real-world data with AR hardware and iteratively evaluating the targeted system designs [1]. However, it is challenging to conduct scalable and reproducible AR experimentation [2] due to two key reasons. First, there is a lack of integrated framework support in real-world data capturing, which makes it a time-consuming process. Second, AR data often exhibits characteristics, including temporal and spatial variations, and is in a multi-modal format, which makes it difficult to conduct controlled evaluations.
- A. Ganj, Y. Zhao, F. Galbiati, and T. Guo. Toward Scalable and Controllable AR Experimentation. In Proceedings of the 1st ACM Workshop on Mobile Immersive Computing, Networking, and Systems, 2023.Google Scholar
Digital Library
- B. Han, P. Pathak, S. Chen, and L.-F. C. Yu. CoMIC: A Collaborative Mobile Immersive Computing Infrastructure for Conducting Multi-user XR Research. IEEE Network, 2022.Google Scholar
- Y. Zhao and T. Guo. Xihe: A 3D Vision-Based Lighting Estimation Framework for Mobile Augmented Reality. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys'21, 2021.Google ScholarDigital Library
Index Terms
- Demo: ARFlow: A Framework for Simplifying AR Experimentation Workflow
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