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---
license: mit
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
Homepage: https://genfusion.sibowu.com/
Repository: https://github.com/Inception3D/GenFusion?tab=readme-ov-file
Paper: [GenFusion: Closing the Loop between Reconstruction and Generation via Videos](https://arxiv.org/pdf/2503.21219)
## Dataset Details
Dataset that was used for training in Genfusion paper. The dataset is mostly sourced from the [DL3DV-10k dataset](https://arxiv.org/abs/2312.16256)
### Dataset Description
A large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting.
This dataset is used to train a diffusion model to reconstruct and generate detailed 3D scenes from sparse or partial video views.
### Dataset Sources [optional]
Sourced from [DL3DV-10k dataset](https://arxiv.org/abs/2312.16256)
- **Repository:** https://github.com/DL3DV-10K/Dataset
- **Paper:** [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://arxiv.org/abs/2312.16256)
## Uses
1. 3D scene reconstruction from monocular or multi-view video
2. Generative modeling of 3D environments
3. Sparse view synthesis and completion
## Citation
Please cite the Genfusion and the DL3DV-10k paper
### Genfusion
```
@inproceedings{Wu2025GenFusion,
author = {Sibo Wu and Congrong Xu and Binbin Huang and Geiger Andreas and Anpei Chen},
title = {GenFusion: Closing the Loop between Reconstruction and Generation via Videos},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
```
### DL3DV-10k
```
@inproceedings{ling2024dl3dv,
title={Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision},
author={Ling, Lu and Sheng, Yichen and Tu, Zhi and Zhao, Wentian and Xin, Cheng and Wan, Kun and Yu, Lantao and Guo, Qianyu and Yu, Zixun and Lu, Yawen and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={22160--22169},
year={2024}
}
```
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