Lei Li
·
Songyou Peng
·
Zehao Yu
·
Shaohui Liu
·
Rémi Pautrat
Xiaochuan Yin
·
Marc Pollefeys
Paper | Video | Project Page
EMAP enables 3D edge reconstruction from multi-view 2D edge maps.
git clone https://github.com/cvg/EMAP.git
cd EMAP
conda create -n emap python=3.8
conda activate emap
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt
Download datasets:
python scripts/download_data.py
The data is organized as follows:
<scan_id>
|-- meta_data.json # camera parameters
|-- color # images for each view
|-- 0_colors.png
|-- 1_colors.png
...
|-- edge_DexiNed # edge maps extracted from DexiNed
|-- 0_colors.png
|-- 1_colors.png
...
|-- edge_PidiNet # edge maps extracted from PidiNet
|-- 0_colors.png
|-- 1_colors.png
...
To train and extract edges on different datasets, use the following commands:
bash scripts/run_ABC.bash
bash scripts/run_Replica.bash
bash scripts/run_DTU.bash
To evaluate extracted edges on ABC-NEF_Edge dataset, use the following commands:
python src/eval/eval_ABC.py
- Training Code
- Inference Code
- Evaluation Code
- Custom Dataset Support
The majority of EMAP is licensed under a MIT License.
If you find the code useful, please consider the following BibTeX entry.
@InProceedings{li2024neural,
title={3D Neural Edge Reconstruction},
author={Li, Lei and Peng, Songyou and Yu, Zehao and Liu, Shaohui and Pautrat, R{\'e}mi and Yin, Xiaochuan and Pollefeys, Marc},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024},
}
If you encounter any issues, you can also contact Lei through lllei.li0386@gmail.com.
This project is built upon NeuralUDF, NeuS and MeshUDF. We use pretrained DexiNed and PidiNet for edge map extraction. We thank all the authors for their great work and repos.