license: cc-by-nc-sa-4.0
language:
- en
tags:
- mast3r
- dune
- dust3r
- 3d-vision
- stereo-matching
- depth-estimation
- point-cloud
- sfm
- slam
- robotics
pipeline_tag: depth-estimation
library_name: mast3r-runtime
MASt3R & DUNE Checkpoints (SafeTensors)
Pre-trained checkpoints for MASt3R (Matching And Stereo 3D Reconstruction) with DUNE (Dense UNconstrained Estimation) models and DUNEMASt3R models, converted to SafeTensors format for efficient C++/embedded inference.
Models
| Model | Resolution | Encoder | Size | Use Case |
|---|---|---|---|---|
dune_vit_small_336 |
336x336 | ViT-S/14 | ~1.3 GB | Real-time drone/embedded |
dune_vit_small_448 |
448x448 | ViT-S/14 | ~1.3 GB | Fast inference |
dune_vit_base_336 |
336x336 | ViT-B/14 | ~1.7 GB | Balanced speed/quality |
dune_vit_base_448 |
448x448 | ViT-B/14 | ~1.7 GB | High quality |
Architecture
- Encoder: DINOv2-based Vision Transformer (DUNE-trained)
- Decoder: MASt3R decoder with CatMLP+DPT heads
- Outputs: Dense 3D points + descriptors for matching
Image Pair → DUNE Encoder → MASt3R Decoder → 3D Points + Descriptors
Usage
With mast3r-runtime (recommended)
pip install mast3r-runtime
# Download and convert
mast3r-runtime download dune_vit_small_336
mast3r-runtime convert dune_vit_small_336 --dtype fp16
Direct download
from huggingface_hub import hf_hub_download
# Download encoder
encoder = hf_hub_download(
repo_id="Aedelon/dunemast3r-models-fp16",
filename="dune_vit_small_336/encoder.safetensors"
)
# Download decoder
decoder = hf_hub_download(
repo_id="Aedelon/dunemast3r-models-fp16",
filename="dune_vit_small_336/decoder.safetensors"
)
Credits & Acknowledgments
These models are converted from the original checkpoints released by Naver Labs Europe.
MASt3R
Grounding Image Matching in 3D with MASt3R Vincent Leroy, Yohann Cabon, Jérôme Revaud arXiv 2024
@article{leroy2024mast3r,
title={Grounding Image Matching in 3D with MASt3R},
author={Leroy, Vincent and Cabon, Yohann and Revaud, J{\'e}r{\^o}me},
journal={arXiv preprint arXiv:2406.09756},
year={2024}
}
DUNE
DUNE: Dense UNconstrained Estimation for 3D Vision Vincent Leroy, Yohann Cabon, Jérôme Revaud CVPR 2025
@inproceedings{leroy2025dune,
title={DUNE: Dense UNconstrained Estimation for 3D Vision},
author={Leroy, Vincent and Cabon, Yohann and Revaud, J{\'e}r{\^o}me},
booktitle={CVPR},
year={2025}
}
DUSt3R
DUSt3R: Geometric 3D Vision Made Easy Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, Jérôme Revaud CVPR 2024
@inproceedings{wang2024dust3r,
title={DUSt3R: Geometric 3D Vision Made Easy},
author={Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud, J{\'e}r{\^o}me},
booktitle={CVPR},
year={2024}
}
Original Repositories
- naver/mast3r - MASt3R official implementation
- naver/dune - DUNE official implementation
- naver/dust3r - DUSt3R official implementation
License
The model weights are released under CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0).
- Attribution: Credit Naver Labs Europe
- NonCommercial: No commercial use without permission
- ShareAlike: Derivatives must use same license
For commercial licensing, contact Naver Labs Europe.
Converted by
Delanoe Pirard / Aedelon - mast3r-runtime
SafeTensors conversion for embedded/C++ inference (Apache 2.0 for runtime code).