---
license: cc-by-nc-sa-4.0
---

# Dynamic Objects Dataset

This dataset is proposed by [NVFi](https://github.com/vLAR-group/NVFi), and used by [FreeGave](https://github.com/vLAR-group/FreeGave) and [TRACE](https://github.com/vLAR-group/TRACE).

## Structure
The structure of the dataset is as:
```
DynObjects
| - data
| | - fallingball
| | | - train: serves as training data
| | | - val: used for evaluating novel view interpolation
| | | - test: used for evaluating future extrapolation
| | | - transforms_train.json: camera poses and other meta informations for training set
| | | - transforms_val.json: camera poses and other meta informations for novel view interpolation task
| | | - transforms_test.json: camera poses and other meta informations for future extrapolation task
| | | - points3d.ply: randomly initialized points for 3D Gaussians
| | - bat
| | - telescope
| | - fan
| | - whale
| | - shark
```

## Citation
If you find this dataset helpful, please consider citing:
```bibtex
@article{li2023nvfi,
  title={NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos}, 
  author={Jinxi Li and Ziyang Song and Bo Yang},
  year={2023},
  journal={NeurIPS}
}
```