| --- |
| license: cc-by-nc-sa-4.0 |
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| |
| # NVIDIA Dynamic Scenes Dataset - Extrapolation Type |
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|
| This dataset is proposed by [Novel View Synthesis](https://gorokee.github.io/jsyoon/dynamic_synth/). |
| [NVFi](https://github.com/vLAR-group/NVFi) selected skating and truck scenes from it, and rearrange the scenes to enable future extrapolation evaluation, |
| and it's 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: |
| ``` |
| Dynamic Scenes |
| | - data |
| | | - Skating: data for Skating scene |
| | | | - 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 |
| | | - Truck: data for Truck scene |
| ``` |
|
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|
|
| ## Citation |
| If you find this dataset helpful, please consider cite: |
| ```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} |
| } |
| ``` |
|
|
| Also please cite the original data contributors: |
| ```bibtex |
| @article{yoon2020dynamic, |
| title={Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera}, |
| author={Yoon, Jae Shin and Kim, Kihwan and Gallo, Orazio and Park, Hyun Soo and Kautz, Jan}, |
| booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| month={June}, |
| year={2020} |
| } |
| } |
| ``` |
|
|
