https://github.com/mikelovesolivia/fcnr

FCNR: Fast Compressive Neural Representation of Visualization Images
https://github.com/mikelovesolivia/fcnr

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FCNR: Fast Compressive Neural Representation of Visualization Images

• Host: GitHub
• URL: https://github.com/mikelovesolivia/fcnr
• Owner: mikelovesolivia
• License: mit
• Created: 2024-07-01T07:38:07.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-24T03:20:03.000Z (6 months ago)
• Last Synced: 2024-10-17T00:58:17.789Z (3 months ago)
• Language: Python
• Homepage:
• Size: 2.15 MB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# FCNR: Fast Compressive Neural Representation of Visualization Images

### [Paper](http://arxiv.org/abs/2407.16369)

Yunfei Lu, Pengfei Gu, Chaoli Wang

This is the official pytorch implementation for the paper "FCNR: Fast Compressive Neural Representation of Visualization Images". 

![image](./figures/overview.png "Overview")



  

  

  



## Get Started

Set up a conda environment with all dependencies with Python 3.9:

```

pip install -r requirements.txt

```

## Data 

You can generate customized visualization images with different viewpoints and timesteps on your own dataset via volume or isosurface rendering. Here is a link to download the vortex dataset (direct volume rendering images included) we use: vortex. 

## Training & Inference

Specify ``, `` and `` to start training and inferencing:

```

python train.py   --config ./configs/

```

An example of the configuration file we use is `./configs/cfg.json`. You can follow it to implement on your customized dataset.

## Results

Here is a comparison between the results of FCNR and existing baselines:



## Citation

```

@article{lu2024fcnr,

  title={{FCNR}: Fast Compressive Neural Representation of Visualization Images},

  author={Lu, Yunfei, Gu, Pengfei, and Wang, Chaoli},

  booktitle={Proceedings of IEEE VIS Conference (Short Papers)},

  year={2024},

  note={Accepted}

}

```