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Junyi42/
MonST3R_PO-TA-S-W_ViTLarge_BaseDecoder_512_dpt

Name: MonST3R_PO-TA-S-W_ViTLarge_BaseDecoder_512_dpt
Availability: InStock

pytorch_model_hub_mixin

2410.03825

cc-by-nc-sa-4.0

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MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

bibtex

@article{zhang2024monst3r,
  author    = {Zhang, Junyi and Herrmann, Charles and Hur, Junhwa and Jampani, Varun and Darrell, Trevor and Cole, Forrester and Sun, Deqing and Yang, Ming-Hsuan},
  title     = {MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion},
  journal   = {arXiv preprint arxiv:2410.03825},
  year      = {2024}
}

Model info

GitHub page: https://github.com/junyi42/monst3r
Project page: https://monst3r-project.github.io/
Paper: https://arxiv.org/abs/2410.03825

How to use

First, install monst3r. To load the model:

python

from dust3r.model import AsymmetricCroCo3DStereo
import torch

model = AsymmetricCroCo3DStereo.from_pretrained("Junyi42/MonST3R_PO-TA-S-W_ViTLarge_BaseDecoder_512_dpt")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)