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- pytorch_model_hub_mixin
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<a href='https://arxiv.org/abs/2412.03079'><img src='https://img.shields.io/badge/arXiv-2412.03079-b31b1b.svg'></a>
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<a href='https://igl-hkust.github.io/Align3R.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
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<a href='https://github.com/jiah-cloud/Align3R'><img src='https://img.shields.io/badge/Github-Repo-blue'></a>
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[**Align3R: Aligned Monocular Depth Estimation for Dynamic Videos**](https://arxiv.org/abs/2412.03079)
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[*Jiahao Lu*\*](https://github.com/jiah-cloud),
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[*Tianyu Huang*\*](https://scholar.google.com/citations?view_op=list_works&hl=en&user=nhbSplwAAAAJ),
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[*Peng Li*](https://scholar.google.com/citations?user=8eTLCkwAAAAJ&hl=zh-CN),
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[*Zhiyang Dou*](https://frank-zy-dou.github.io/),
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[*Cheng Lin*](https://clinplayer.github.io/),
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[*Zhiming Cui*](),
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[*Zhen Dong*](https://dongzhenwhu.github.io/index.html),
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[*Sai-Kit Yeung*](https://saikit.org/index.html),
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[*Wenping Wang*](https://scholar.google.com/citations?user=28shvv0AAAAJ&hl=en),
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[*Yuan Liu*](https://liuyuan-pal.github.io/)
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Arxiv, 2024.
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**Align3R** estimates temporally consistent video depth, dynamic point clouds, and camera poses from monocular videos.
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<video controls>
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<source src="https://igl-hkust.github.io/Align3R.github.io/static/video/converted/output_video.mp4" type="video/mp4">
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</video>
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```bibtex
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@article{lu2024align3r,
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title={Align3R: Aligned Monocular Depth Estimation for Dynamic Videos},Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung, Wenping Wang, Yuan Liu
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author={Lu, Jiahao and Huang, Tianyu and Li, Peng and Dou, Zhiyang and Lin, Cheng and Cui, Zhiming and Dong, Zhen and Yeung, Sai-Kit and Wang, Wenping and Liu,Yuan},
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journal={arXiv preprint arXiv:2412.03079},
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year={2024}
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}
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```
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### How to use
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First, [install Align3R](https://github.com/jiah-cloud/Align3R).
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To load the model:
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```python
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from dust3r.model import AsymmetricCroCo3DStereo
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import torch
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model = AsymmetricCroCo3DStereo.from_pretrained("cyun9286/Align3R_DepthAnythingV2_ViTLarge_BaseDecoder_512_dpt")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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