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| <h1 align="center" id="heading">FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees</h1> |
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| <p align="center"> |
| <p align="center"> |
| <b><a href="https://cg.cs.uni-bonn.de/person/m-sc-saskia-rabich">Saskia Rabich</a></b> |
| · |
| <b><a href="https://cg.cs.uni-bonn.de/person/dr-patrick-stotko">Patrick Stotko</a></b> |
| · |
| <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 · Presented at CGI 2024</h3> |
| <h3 align="center"> |
| <a href="https://doi.org/10.1007/s00371-024-03475-3">Paper</a> |
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| <a href="https://arxiv.org/abs/2310.20710">arXiv</a> |
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| <a href="https://cg.cs.uni-bonn.de/publication/rabich-2024-fpo">Project Page</a> |
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| <a href="https://github.com/SaskiaRabich/FPOplusplus">Code</a> |
| </h3> |
| <div align="center"></div> |
| </p> |
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| <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 |
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| 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. |
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| ## Citation |
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| If you find this data useful for your research, please cite FPO++ as follows: |
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| ``` |
| @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}, |
| } |
| ``` |
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| ## License |
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| This data is provided under the MIT license. |
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| ## Acknowledgements |
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| 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). |
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