README.md

<h1 align="center" id="heading">FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees</h1>

<p align="center">
    <p align="center">
		<b><a href="https://cg.cs.uni-bonn.de/person/m-sc-saskia-rabich">Saskia Rabich</a></b>
        &nbsp;·&nbsp;
        <b><a href="https://cg.cs.uni-bonn.de/person/dr-patrick-stotko">Patrick Stotko</a></b>
        &nbsp;·&nbsp;
        <b><a href="https://cg.cs.uni-bonn.de/person/prof-dr-reinhard-klein">Reinhard Klein</a></b>
    </p>
    <p align="center">
        University of Bonn
    </p>
    <h3 align="center">The Visual Computer &nbsp;·&nbsp; Presented at CGI 2024</h3>
    <h3 align="center">
        <a href="https://doi.org/10.1007/s00371-024-03475-3">Paper</a>
        &nbsp; | &nbsp;
        <a href="https://arxiv.org/abs/2310.20710">arXiv</a>
        &nbsp; | &nbsp;
        <a href="https://cg.cs.uni-bonn.de/publication/rabich-2024-fpo">Project Page</a>
		&nbsp; | &nbsp;
        <a href="https://github.com/SaskiaRabich/FPOplusplus">Code</a>
    </h3>
    <div align="center"></div>
</p>

<p align="left">
    This repository contains data used in "FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees".
</p>

## Usage

You can use this data by downloading and extracting the .zip-files into a `data` subdirectory in the root directory of the FPO++ source code.
Please refer to the GitHub repository for information on how to run the code.

## Citation

If you find this data useful for your research, please cite FPO++ as follows:

```
@article{rabich2024FPOplusplus:,
	 title = {FPO++: efficient encoding and rendering of dynamic neural radiance fields by analyzing and enhancing {Fourier} {PlenOctrees}},
	 author = {Saskia Rabich and Patrick Stotko and Reinhard Klein},
	 journal = {The Visual Computer},
	 year = {2024},
	 issn = {1432-2315},
	 doi = {10.1007/s00371-024-03475-3},
	 url = {https://doi.org/10.1007/s00371-024-03475-3},
}
```

## License

This data is provided under the MIT license.

## Acknowledgements

This work has been funded by the Federal Ministry of Education and Research under grant no. 01IS22094E WEST-AI, by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute for Machine Learning and Artificial Intelligence, and additionally by the DFG project KL 1142/11-2 (DFG Research Unit FOR 2535 Anticipating Human Behavior).