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base_model: Ruicheng/moge-2-vitl-normal
library_name: pytorch
license: cc-by-nc-4.0
pipeline_tag: depth-estimation
tags:
- depth-estimation
- dermatology
- 3d-reconstruction
- medical-imaging
- skin-lesion
- moge
---
# DermDepth: Monocular Metric Scale 3D Reconstruction for Dermatology
This repository contains model checkpoints from the paper [DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology](https://huggingface.co/papers/2607.13010) (Carrión & Norouzi, MICCAI 2026).
## Checkpoints
| Filename | Training data | Notes |
|---|---|---|
| `DermDepth_Synth.pt` | D-Synth only | "DermDepth_S" in the paper. Synthetic-only baseline. |
| `DermDepth_Synth_SKINL2_WoundsDB.pt` | D-Synth → SKINL2 + WoundsDB | Intermediate stage (before DDI pseudo-GT). |
| `DermDepth_Synth_SKINL2_WoundsDB_DDI.pt` | D-Synth → SKINL2 + WoundsDB → DDI pseudo-GT | **Best model. Corresponds to "DermDepth" in the paper.** |
| `DermDepth_Synth_Normals.pt` | D-Synth, normal-head emphasis | Trained normal-head model. |
## Key results (held-out test sets, paper Table 1)
| Method | SKINL2 Scale | WoundsDB Scale | DDI Ratio | Fitzpatrick Disparity |
|---|:---:|:---:|:---:|:---:|
| MoGe-2 (baseline) | 16.10× | 0.62× | 81.0× | 10.90 |
| DermDepth_Synth | 1.11× | 0.28× | 9.2× | 1.70 |
| **DermDepth (best)** | **0.87×** | **0.91×** | **1.95×** | **1.02** |
Scale ratio target is 1.0×. See the paper for full benchmarks and SI-δ₁ details.
## Usage
These checkpoints are designed to be loaded by [MoGe-2](https://github.com/microsoft/MoGe), modified per the [DermDepth code repository](https://github.com/hectorcarrion/dermdepth). The repo contains end-to-end training, inference, and evaluation scripts.
Quick download:
```python
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(
repo_id="hcarrion/DermDepth",
filename="DermDepth_Synth_SKINL2_WoundsDB_DDI.pt",
)
```
## Citation
```bibtex
@inproceedings{carrion2026dermdepth,
title = {DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology},
author = {Carri{\'o}n, H{\'e}ctor and Norouzi, Narges},
booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year = {2026}
}
```
## License
CC BY-NC 4.0 (research / non-commercial use). The base MoGe-2 weights remain under their original license — see [the MoGe repository](https://github.com/microsoft/MoGe) for details.
## Related Resources
- **Code & training scripts**: https://github.com/hectorcarrion/dermdepth
- **D-Synth dataset (training data)**: https://huggingface.co/datasets/hcarrion/D-Synth
- **Base model**: https://huggingface.co/Ruicheng/moge-2-vitl-normal |