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Browse files- DA3_MOG_Sky_LogL2.ckpt +3 -0
- README.md +57 -0
- VGGT_MOG_LogL2.ckpt +3 -0
DA3_MOG_Sky_LogL2.ckpt
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oid sha256:d442e856a6be8832aac72a0f17293ae98af75a5bd8d21adfbe337bd915e41d83
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README.md
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---
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license: apache-2.0
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library_name: pytorch
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tags:
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- depth-estimation
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- 3d-reconstruction
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- multi-view
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- camera-pose
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- gaussian-splatting
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- depth-anything-3
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- vggt
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pipeline_tag: depth-estimation
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---
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# MDA — Multi-view depth & geometry checkpoints
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These are the official model checkpoints for the paper
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**"Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation"** (MDA).
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📄 [arXiv](https://arxiv.org/abs/2606.02552) | 🌐 [Project page](https://biansy000.github.io/mda-site/)
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MDA is a mixture-density depth representation that predicts several depth
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hypotheses (with their probabilities) at every pixel instead of forcing a single
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depth, which largely removes the *flying-point* artifacts at object boundaries
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that plague feed-forward depth estimators. See the [Citation](#citation) section
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to cite this work.
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These two checkpoints are used for multi-view geometry prediction —
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spatially consistent depth and camera pose from a set of input images. They are
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built on two different backbones and trained with a Mixture-of-Gaussians (MoG)
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depth head and a `logl2` objective.
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| File | Backbone | Wrapper | `model_choice.py` name | Params |
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|---|---|---|---|---|
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| [`DA3_MOG_Sky_LogL2.ckpt`](./DA3_MOG_Sky_LogL2.ckpt) | DA3 Giant | `DA3Wrapper` | `mda_mog_sky_l2` | ~1.36 B |
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| [`VGGT_MOG_LogL2.ckpt`](./VGGT_MOG_LogL2.ckpt) | VGGT-1B | `VGGTWrapper` | `vggt_mog_l2` | ~1.16 B |
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Both are PyTorch Lightning checkpoints (`save_weights_only=True`, Lightning 2.5.6).
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State-dict keys are prefixed `net.net.*` because the network is wrapped by a
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Lightning module — strip the prefix and load into the bare net. These are **research checkpoints** and are **not** loadable
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through the standard `DepthAnything3.from_pretrained` HF API.
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## Citation
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If you build on **MDA**, please cite:
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```bibtex
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@misc{bian2026modeling,
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title = {Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation},
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author = {Siyuan Bian and Congrong Xu and Jun Gao},
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year = {2026},
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eprint = {2606.02552},
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archivePrefix = {arXiv},
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primaryClass = {cs.CV},
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url = {https://arxiv.org/abs/2606.02552}
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}
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```
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VGGT_MOG_LogL2.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:d82e60abcf9f26dca7ac0056d5f5db893e6e6827f0dd7e242bcff93c079a4177
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size 4632792671
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