This repository includes the source code of the paper DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction (ECCV 2024).

Authors: Yuxin Yao, Siyu Ren, Junhui Hou, Zhi Deng, Juyong Zhang, Wenping Wang.

• Release the code.
• Release preprocessed data.

git clone https://github.com/yaoyx689/DynoSurf.git
cd DynoSurf 
conda create -n dynosurf python=3.9 
conda activate dynosurf

# install pytorch (https://pytorch.org/)
pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116

# install pytorch3d (https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md)
pip install "git+https://github.com/facebookresearch/pytorch3d.git"

# install kaolin (https://kaolin.readthedocs.io/en/latest/notes/installation.html)
pip install kaolin==0.14.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.13.0_cu116.html

# install other dependencies
pip install -r requirements.txt 

cd scripts
python run.py 

1. If the input point cloud sequence does not provide normals, put the folder of them under data_source/raw_data/, compute the normals for them and write them into data_source/[foldername]. If the normals are already provided, skip this step.

   cd process_data 
   python process_raw_data.py [folder_name]
   

2. Write the input data into a format that can be read by the code.

   python gene_json.py [folder_name]
   

3. Execute training. Change the folder_name in scripts/run.sh and run

   cd scripts 
   python run.py 
   

• Resolution: If you want to adjust the resolution of the reconstructed surface, you can adjust the parameter tet_grid_volume (around $10^{-8}$ ~ $10^{-6}$) in confs/base.conf, which controls the resolution of the generated tetrahedron. The smaller the value, the higher the resolution.

• Template Frame: If you want to specify the frame index corresponding to the template surface, please modify the template_idx in confs/base.conf.

If you find our code or paper helps, please consider citing:

@inproceedings{yao2024dynosurf,
  author    = {Yao, Yuxin and Ren, Siyu and Hou, Junhui and Deng, Zhi and Zhang, Juyong and Wang, Wenping},
  title     = {DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction},
  booktitle   = {European Conference on Computer Vision},
  year      = {2024},
}

Some of the implementary refer to DMTet and TeCH.