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dunemast3r-models-fp16

Depth Estimation
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dunemast3r-models-fp16
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---
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)

```bash
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

```python
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

```bibtex
@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

```bibtex
@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

```bibtex
@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](https://github.com/naver/mast3r) - MASt3R official implementation
- [naver/dune](https://github.com/naver/dune) - DUNE official implementation
- [naver/dust3r](https://github.com/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](https://europe.naverlabs.com/).

## Converted by

[Delanoe Pirard / Aedelon](https://github.com/aedelon) - [mast3r-runtime](https://github.com/aedelon/mast3r-runtime)

SafeTensors conversion for embedded/C++ inference (Apache 2.0 for runtime code).