---
extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  Job title:
    type: select
    options:
      - Student
      - Research Graduate
      - AI researcher
      - AI developer/engineer
      - Reporter
      - Other
  geo: ip_location
  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: >-
  The information you provide will be collected, stored, processed and shared in
  accordance with the [Meta Privacy
  Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
extra_gated_heading: "Please be sure to provide your full legal name, date of birth, and full organization name with all corporate identifiers. Avoid the use of acronyms and special characters. Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate."
language:
  - en
pipeline_tag: image-to-image
library_name: pytorch
tags:
  - meta-ai
  - meta-pytorch
  - surface-normal-estimation
  - normal-map
  - hyden
license: fair-noncommercial-research-license
---

# HyDen MoGe v2 Surface Normal

HyDen is a hybrid dual-path vision encoder for high-resolution monocular
geometry that pairs a low-resolution transformer branch for global context
with a full-resolution CNN branch for fine detail, delivering
state-of-the-art accuracy at a fraction of the inference cost of competing
methods. This checkpoint is the HyDen variant of MoGe-v2 for surface normal
estimation.

Paper: *Hyden: A Hybrid Dual-Path Encoder for Monocular Geometry of
High-resolution Images* (ICLR 2026).
Code, loader, and inference: **https://github.com/facebookresearch/metadepth**

## Model details

- Task: monocular surface normal estimation
- Output: unit-length per-pixel surface normals
- Weights precision: FP32
- Checkpoint: `hyden_mogev2_surface_normal_vitl_fp32_f1076635715.pth`
- License: FAIR Noncommercial Research License

## Usage

Loader, preprocessing, and inference live in the MetaDepth repository:
**https://github.com/facebookresearch/metadepth**

Download the checkpoint directly from this page, or via the
`huggingface_hub` API:

```python
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="facebook/hyden-mogev2-surface-normal",
    filename="hyden_mogev2_surface_normal_vitl_fp32_f1076635715.pth",
)
```

## Citation

```bibtex
@inproceedings{zhang2026hyden,
  title     = {Hyden: A Hybrid Dual-Path Encoder for Monocular Geometry of High-resolution Images},
  author    = {Zaiwei Zhang and Marc Mapeke and Wei Ye and Rakesh Ranjan and JQ Huang},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year      = {2026},
  url       = {https://openreview.net/forum?id=2eL6yXLCh8}
}
```

## Contact

Open an issue on [facebookresearch/metadepth](https://github.com/facebookresearch/metadepth/issues).