MDA
These are the official model checkpoints for the paper "Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation" (MDA).
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 4,31–5,05 GB
- Versión
- vDA3 MOG Sky Log L2
- Actualizado
- 10 oct 2026
- Fuente
- Hugging Face
Descripción de la fuente
MDA — Multi-view depth & geometry checkpoints
These are the official model checkpoints for the paper "Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation" (MDA).
📄 arXiv | 🌐 Project page
MDA is a mixture-density depth representation that predicts several depth hypotheses (with their probabilities) at every pixel instead of forcing a single depth, which largely removes the flying-point artifacts at object boundaries that plague feed-forward depth estimators. See the Citation section to cite this work.
These two checkpoints are used for multi-view geometry prediction —
spatially consistent depth and camera pose from a set of input images. They are
built on two different backbones and trained with a Mixture-of-Gaussians (MoG)
depth head and a logl2 objective.
| File | Backbone | Wrapper | model_choice.py name | Params |
|---|---|---|---|---|
DA3_MOG_Sky_LogL2.ckpt | DA3 Giant | DA3Wrapper | mda_mog_sky_l2 | ~1.36 B |
VGGT_MOG_LogL2.ckpt | VGGT-1B | VGGTWrapper | vggt_mog_l2 | ~1.16 B |
Both are PyTorch Lightning checkpoints (save_weights_only=True, Lightning 2.5.6).
State-dict keys are prefixed net.net.* because the network is wrapped by a
Lightning module — strip the prefix and load into the bare net. These are research checkpoints and are not loadable
through the standard DepthAnything3.from_pretrained HF API.
Citation
If you build on MDA, please cite:
@misc{bian2026modeling,
title = {Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation},
author = {Siyuan Bian and Congrong Xu and Jun Gao},
year = {2026},
eprint = {2606.02552},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.02552}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
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Artefactos del modelo
2 artefactos
Artefactos del modelo
2 artefactosExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: apache-2.0 library_name: pytorch tags: - depth-estimation - 3d-reconstruction - multi-view - camera-pose - gaussian-splatting - depth-anything-3 - vggt pipeline_tag: depth-estimation --- # MDA — Multi-view depth & geometry checkpoints These are the official model checkpoints for the paper **"Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation"** (MDA). 📄 [arXiv](https://arxiv.org/abs/2606.02552) | 🌐 [Project page](https://biansy000.github.io/mda-site/) MDA is a mixture-density depth representation that predicts several depth hypotheses (with their probabilities) at every pixel instead of forcing a single depth, which largely removes the *flying-point* artifacts at object boundaries that plague feed-forward depth estimators. See the [Citation](#citation) section to cite this work. These two checkpoints are used for multi-view geometry prediction — spatially consistent depth and camera pose from a set of input images. They are built on two different backbones and trained with a Mixture-of-Gaussians (MoG) depth head and a `logl2` objective. | File | Backbone | Wrapper | `model_choice.py` name | Params | |---|---|---|---|---| | [`DA3_MOG_Sky_LogL2.ckpt`](./DA3_MOG_Sky_LogL2.ckpt) | DA3 Giant | `DA3Wrapper` | `mda_mog_sky_l2` | ~1.36 B | | [`VGGT_MOG_LogL2.ckpt`](./VGGT_MOG_LogL2.ckpt) | VGGT-1B | `VGGTWrapper` | `vggt_mog_l2` | ~1.16 B | Both are PyTorch Lightning checkpoints (`save_weights_only=True`, Lightning 2.5.6). State-dict keys are prefixed `net.net.*` because the network is wrapped by a Lightning module — strip the prefix and load into the bare net. These are **research checkpoints** and are **not** loadable through the standard `DepthAnything3.from_pretrained` HF API. ## Citation If you build on **MDA**, please cite: ```bibtex @misc{bian2026modeling, title = {Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation}, author = {Siyuan Bian and Congrong Xu and Jun Gao}, year = {2026}, eprint = {2606.02552}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2606.02552} } ```
Source context: 0 downloads · 3 likes · Pipeline depth-estimation · Library pytorch · Repo sy000/MDA
