F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

F-Hash is a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization.

Github Page, ArXiv, Publishers' Version

Demo video can be found here.

1. Packages

pip install vtk torch tqdm

2. Directory tree

The following directoies need to be manually created:

F-Hash (Source code)
├── data
│   └── argon_bubble  (Raw dataset: .dat)
│       ├── argon_128x128x256  (Keyframe: .vtk and .bin)
│       │   └── feature_local (Training data: .bin; Meta data: .txt, .json)
│       └── argon_128x128x256_predict
│           └── f_hash
│               └── timestep_index (Prediction of time step during training: .vtk)
└── models
    └── argon_128x128x256
        └── f_hash (saved checkpoints during training)

3. Download the data

Download the Argon Bubble toy dataset, put all the extracted frames under F-Hash/data/argon_bubble folder in above directory tree.

python download_data.py

4. Run

Coreset Selection

python coreset.py

Training

python train.py

Testing

python test.py

TODO

• Coreset selection
• F-Hash input encoding
• Training
• Testing
• Adaptive Ray Marching (ARM)

Citing F-Hash

If you use it in your research, we would appreciate a citation via

@ARTICLE{sun2025fhash,
  author={Sun, Jianxin and Lenz, David and Yu, Hongfeng and Peterka, Tom},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding}, 
  year={2026},
  volume={32},
  number={1},
  pages={396-406},
  keywords={Encoding;Data visualization;Training;Convergence;Rendering (computer graphics);Data models;Superresolution;Hash functions;Computational modeling;Neural radiance field;Time-varying volume;volume visualization;input encoding;deep learning},
  doi={10.1109/TVCG.2025.3634812}
}

License

F-Hash is distributed under the terms of the BSD-3 license.