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--- |
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license: mit |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset Card for Dataset Name |
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Homepage: https://genfusion.sibowu.com/ |
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Repository: https://github.com/Inception3D/GenFusion?tab=readme-ov-file |
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Paper: [GenFusion: Closing the Loop between Reconstruction and Generation via Videos](https://arxiv.org/pdf/2503.21219) |
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## Dataset Details |
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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) |
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### Dataset Description |
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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. |
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This dataset is used to train a diffusion model to reconstruct and generate detailed 3D scenes from sparse or partial video views. |
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### Dataset Sources [optional] |
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Sourced from [DL3DV-10k dataset](https://arxiv.org/abs/2312.16256) |
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- **Repository:** https://github.com/DL3DV-10K/Dataset |
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- **Paper:** [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://arxiv.org/abs/2312.16256) |
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## Uses |
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1. 3D scene reconstruction from monocular or multi-view video |
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2. Generative modeling of 3D environments |
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3. Sparse view synthesis and completion |
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## Citation |
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Please cite the Genfusion and the DL3DV-10k paper |
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### Genfusion |
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``` |
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@inproceedings{Wu2025GenFusion, |
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author = {Sibo Wu and Congrong Xu and Binbin Huang and Geiger Andreas and Anpei Chen}, |
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title = {GenFusion: Closing the Loop between Reconstruction and Generation via Videos}, |
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booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year = {2025} |
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} |
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``` |
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### DL3DV-10k |
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``` |
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@inproceedings{ling2024dl3dv, |
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title={Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision}, |
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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}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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pages={22160--22169}, |
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year={2024} |
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} |
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``` |
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