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 Arxiv, 2024.
Align3R estimates temporally consistent video depth, dynamic point clouds, and camera poses from monocular videos.
@article{lu2024align3r,
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
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},
journal={arXiv preprint arXiv:2412.03079},
year={2024}
}
How to use
First, install Align3R. To load the model:
from dust3r.model import AsymmetricCroCo3DStereo
import torch
model = AsymmetricCroCo3DStereo.from_pretrained("cyun9286/Align3R_DepthAnythingV2_ViTLarge_BaseDecoder_512_dpt")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)