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}
}