astaka-pe/SeMIGCN

Learning Self-Prior for Mesh Inpainting using Self-Supervised Graph Convolutional Networks [TVCG 2024]

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

Paper | arXiv

Accepted by IEEE TVCG 2024

Method Overview

Usage

Environments

python==3.10
torch==1.13.0
torch-geometric==2.2.0

Installation (Docker)

docker image build -t astaka-pe/semigcn .
docker run -itd --gpus all -p 8081:8081 --name semigcn -v .:/work astaka-pe/semigcn
docker exec -it semigcn /bin/bash

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

python3 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

python3 sgcn.py -i datasets/**/{mesh-name}   # SGCN
python3 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

You can monitor the training progress through the web viewer. (Default: http://localhost:8081)

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

python3 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

python3 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}
}