Learning Self-Prior for Mesh Inpainting using Self-Supervised Graph Convolutional Networks

Paper | arXiv

Accepted by IEEE TVCG 2024

Method Overview

Usage

Environments

python==3.7
pytorch==1.7.0
torch-geometric==1.7.1

If you prefer to run in a newer environment, please refer to another branch.

Installation (Conda)

git clone https://github.com/astaka-pe/SeMIGCN
cd SeMIGCN
conda env create -f environment.yml
conda activate semigcn

Preperation

• Unzip datasets.zip
• Sample meshes will be placed in datasets/
• Put your own mesh in a new arbitrary folder as:
  • Deficient mesh: datasets/**/{mesh-name}/{mesh-name}_original.obj
  • Ground truth: datasets/**/{mesh-name}/{mesh-name}_gt.obj
• The deficient and the ground truth meshes need not share a same connectivity but their scales must be shared

Preprocess

• Specify the path of the deficient mesh
• Create initial mesh and smoothed mesh

python preprocess/prepare.py -i datasets/**/{mesh-name}/{mesh-name}_original.obj

• options

  • -r {float}: Target length of remeshing. The higher the coarser, the lower the finer. default=0.6.

• Computation time: 30 sec

Training

python sgcn.py -i datasets/**/{mesh-name}   # SGCN
python mgcn.py -i datasets/**/{mesh-name}   # MGCN

• options
  • -CAD: For a CAD model
  • -real: For a real scan
  • -cache: For using cache files (for faster computation)
  • -mu : Weight for refinement

Evaluation

• Create datasets/**/{mesh-name}/comparison and put meshes for evaluation
  • A deficient mesh datasets/**/{mesh-name}/comparison/original.obj and a ground truth mesh datasets/**/{mesh-name}/comparison/gt.obj are needed for evaluation

python check/batch_dist_check.py -i datasets/**/{mesh-name}

• options
  • -real: For a real scan

Refinement (Option)

• If you want to perform only refinement, run

python refinement.py \\
    -src datasets/**/{mesh-name}/{mesh-name}_initial/obj \\
    -dst datasets/**/{mesh-name}/output/**/100_step/.obj \\     # SGCN
    # -dst datasets/**/{mesh-name}/output/**/100_step_0.obj \\    # MGCN
    -vm datasets/**/{mesh-name}/{mesh-name}_vmask.json \\
    -ref {arbitrary-output-filename}.obj \\

• option
  • -mu: Weight for refinement
    • Choose a weight so that the remaining vertex positions of the initial mesh and the shape of missing regions of the output mesh are saved

Run other competitive methods

Please refer to tinymesh.

Citation

@article{hattori2024semigcn,
  title={Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks},
  author={Hattori, Shota and Yatagawa, Tatsuya and Ohtake, Yutaka and Suzuki, Hiromasa},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2024},
  publisher={IEEE}
}