Mast3r

--- tags: - image-to-3d - pytorch_model_hub_mixin - model_hub_mixin library_name: mast3r repo_url: https://github.com/naver/mast3r --- ## Grounding Image Matching in 3D with MASt3R ```bibtex @misc{mast3r_arxiv24, title={Grounding Image Matching in 3D with MASt3R}, author={Vincent Leroy and Yohann Cabon and Jerome Revaud}, year={2024}, eprint={2406.09756}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{dust3r_cvpr24, title={DUSt3R: Geometric 3D Vision Made Easy}, author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, booktitle = {CVPR}, year = {2024} } ``` # License The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](https://github.com/naver/mast3r/blob/main/LICENSE) for more information. For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. The mapfree dataset license in particular is very restrictive. For more information, check [CHECKPOINTS_NOTICE](https://github.com/naver/mast3r/blob/main/CHECKPOINTS_NOTICE). # Model info Gihub page: https://github.com/naver/mast3r/ | Modelname | Training resolutions | Head | Encoder | Decoder | |-------------|----------------------|------|---------|---------| | MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric | 512x384, 512x336, 512x288, 512x256, 512x160 | CatMLP+DPT | ViT-L | ViT-B | # How to use First, [install mast3r](https://github.com/naver/mast3r?tab=readme-ov-file#installation). To load the model: ```python from mast3r.model import AsymmetricMASt3R import torch model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ```

Source context: 7 downloads · 0 likes · Pipeline image-to-3d · Library mast3r · Repo camenduru/mast3r