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Dynamic Objects Dataset
This dataset is proposed by NVFi, and used by FreeGave and 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:
@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}
}
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