|
1 | | -# ARFlow Server |
| 1 | +# ARFlow |
2 | 2 |
|
3 | | -To setup ARFlow, you should first deploy the ARFlow server on your developer desktop. |
| 3 | +[ARFlow: A Framework for Simplifying AR Experimentation Workflow](https://doi.org/10.1145/3638550.3643617) |
4 | 4 |
|
5 | | -## ARFlow Serer |
| 5 | +[Paper](https://doi.org/10.1145/3638550.3643617) | [BibTeX](#bibtex) | [Project Page](https://cake.wpi.edu/ARFlow/) | [Video](https://youtu.be/mml8YrCgfTk) |
6 | 6 |
|
7 | | -The ARFlow server can be simply installed via `pip`: |
| 7 | +Watch our demo video: |
8 | 8 |
|
9 | | -```bash |
10 | | -pip install arflow |
11 | | -``` |
12 | | - |
13 | | -Next, you may integrate ARFlow with your own research prototype via the Python API: |
| 9 | +[](https://youtu.be/mml8YrCgfTk) |
14 | 10 |
|
15 | | -```python |
16 | | -"""A simple example of extending the ARFlow server.""" |
17 | | -import arflow |
| 11 | +## Get Started |
18 | 12 |
|
| 13 | +Please refer to the individual [server](./python/README.md) and [client](./unity/README.md) installation guides. |
19 | 14 |
|
20 | | -class CustomService(arflow.ARFlowService): |
21 | | - def on_frame_received(self, frame: arflow.DataFrameRequest): |
22 | | - """Called when a frame is received.""" |
23 | | - print("Frame received!") |
| 15 | +## Citation |
24 | 16 |
|
| 17 | +Please add the following citation in your publication if you used our code for your research project. |
25 | 18 |
|
26 | | -def main(): |
27 | | - arflow.create_server(CustomService, port=8500, path_to_save="./") |
| 19 | +```bibtex |
| 20 | +@inproceedings{zhao2024arflow, |
| 21 | +author = {Zhao, Yiqin and Guo, Tian}, |
| 22 | +title = {Demo: ARFlow: A Framework for Simplifying AR Experimentation Workflow}, |
| 23 | +year = {2024}, |
| 24 | +isbn = {9798400704970}, |
| 25 | +publisher = {Association for Computing Machinery}, |
| 26 | +address = {New York, NY, USA}, |
| 27 | +url = {https://doi.org/10.1145/3638550.3643617}, |
| 28 | +doi = {10.1145/3638550.3643617}, |
| 29 | +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.}, |
| 30 | +booktitle = {Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications}, |
| 31 | +pages = {154}, |
| 32 | +numpages = {1}, |
| 33 | +location = {<conf-loc>, <city>San Diego</city>, <state>CA</state>, <country>USA</country>, </conf-loc>}, |
| 34 | +series = {HOTMOBILE '24} |
| 35 | +} |
| 36 | +``` |
28 | 37 |
|
| 38 | +## Acknowledgement |
29 | 39 |
|
30 | | -if __name__ == "__main__": |
31 | | - main() |
32 | | -``` |
| 40 | +This work was supported in part by NSF Grants #2105564 and #2236987, and a VMware grant. |
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