ARFlow: A Framework for Simplifying AR Experimentation Workflow
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@inproceedings{zhao2024arflow,
author = {Zhao, Yiqin and Guo, Tian},
title = {Demo: ARFlow: A Framework for Simplifying AR Experimentation Workflow},
year = {2024},
isbn = {9798400704970},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3638550.3643617},
doi = {10.1145/3638550.3643617},
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.},
booktitle = {Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications},
pages = {154},
numpages = {1},
location = {<conf-loc>, <city>San Diego</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},
series = {HOTMOBILE '24}
}This work was supported in part by NSF Grants #2105564 and #2236987, and a VMware grant.