FE2E: From Editor to Dense Geometry Estimator

Jiyuan Wang^1,2, Chunyu Lin^1✉, Lei Sun^2✝, Rongying Liu¹, Mingxing Li², Lang Nie³, Kang Liao⁴, Xiangxiang Chu², Yao Zhao¹

¹Beijing Jiaotong University
²Alibaba Group
³Chongqing University of Posts and Telecommunications
⁴Nanyang Technological University
^✉Corresponding author. ^✝Project leader.

We present FE2E, a DiT-based foundation model for monocular dense geometry prediction. FE2E adapts an advanced image editing model to dense geometry tasks and achieves strong zero-shot performance on both monocular depth and normal estimation.

📢 News

• [2026-03-17]: Code and Checkpoint are available now!
• [2026-02-21]: FE2E was accepted by CVPR 2026!!! 🎉🎉🎉
• [2025-09-05]: Paper released on arXiv.

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🛠️ Setup

This codebase is prepared as an inference/evaluation release.

pip install -r requirements.txt

Recommended local layout:

FE2E/
├── pretrain/
│   ├── step1x-edit-i1258.safetensors
│   ├── step1x-edit-v1p1-official.safetensors
│   └── vae.safetensors
├── lora/
│   └── LDRN.safetensors
├── infer/
│   ├── eth3d/
│   │   └── eth3d.tar
│   └── dsine_eval/
│       ├── nyuv2/
│       └── scannet/
└── logs/

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🔥 Training

[ ] Training code will be released later.

---

🕹️ Inference

1. Prepare Model Weights

1. Download the base weights, which from the official Step1X-Edit release.
2. Download FE2E LoRA checkpoint

2. Prepare Benchmark Datasets

• Depth benchmarks follow the external evaluation data convention from Marigold.
• Normal benchmarks follow the external evaluation data convention from DSINE.

Supported depth benchmarks:

• nyu_v2,kitti,eth3d,diode,scannet

Supported normal benchmarks:

• nyuv2,scannet,ibims,sintel

3. Run Evaluation

[dataset] normal:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
MASTER_PORT=21258 \
PYTHONUNBUFFERED=1 \
python -u evaluation.py \
  --model_path ./pretrain \
  --eval_data_root ./infer \
  --output_dir ./infer/eval_verify_scannet_normal_8gpu \
  --num_gpus 8 \
  --num_samples -1 \
  --lora ./lora/LDRN.safetensors \
  --single_denoise \
  --prompt_type empty \
  --norm_type ln \
  --task_name normal \
  --normal_eval_datasets [dataset]

[dataset] depth:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
MASTER_PORT=21257 \
PYTHONUNBUFFERED=1 \
python -u evaluation.py \
  --model_path ./pretrain \
  --eval_data_root ./infer \
  --output_dir ./infer/eval_verify_eth3d_8gpu \
  --num_gpus 8 \
  --num_samples -1 \
  --lora ./lora/LDRN.safetensors \
  --single_denoise \
  --prompt_type empty \
  --norm_type ln \
  --task_name depth \
  --depth_eval_datasets [dataset]

4. Reference Logs

If you want to known the successful status, this repo includes run logs in logs/:

• logs/verify_scannet_normal_8gpu_20260317_171345.log
• logs/verify_eth3d_8gpu_20260317_172004.log

---

🎓 Citation

If you find our work useful, please cite:

@article{wang2025editor,
  title={From Editor to Dense Geometry Estimator},
  author={Wang, JiYuan and Lin, Chunyu and Sun, Lei and Liu, Rongying and Nie, Lang and Li, Mingxing and Liao, Kang and Chu, Xiangxiang and Zhao, Yao},
  journal={arXiv preprint arXiv:2509.04338},
  year={2025}
}