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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:
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
@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.