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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

Qt and Pytorch implementation for our paper "GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks" (ACM Transactions on Graphics 2022)

We propose GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising methods that exploit hand-crafted or voxel-based representations for feature learning, our method explores the structure of a triangular mesh itself and introduces a graph representation followed by graph convolution operations in the dual space of triangles. We also create a new dataset called PrintData containing 20 real scans with their corresponding ground truths for the research community.

Denoised Results:

Interface:

Code:

Prerequisites:

  • Hardware: Personal computer with NVIDIA GPU.
  • Environments: CUDA10.0, Windows system (network training part can also be used on Linux).

Third Party Library:

Network part:

The training code and part of validation data are supplied. Network test can be run by:

cd DenoisingGCN/testSamples
unzip bunny_0_2.zip
cd ../
python datautils.py
python test.py

bunny_0_2/*.mat are sampled patches from the noisy bunny model with 0.2 level of Gaussian noise.

Denoising Interface:

Executable demo, the corresponding code, and some sampled meshes are supplied.

  • For .exe, windows platform is required and the CUDA PATH must be set in the system environment. Some .dll are required (CUDA&LibTorch: c10.dll, c10_cuda.dll, caffe2_nvrtc.dll, nvToolsExt61_1.dll, torch.dll; Qt: Qt5Core.dll, Qt5Gui.dll, Qt5OpenGL.dll, Qt5Widgets.dll).

  • For code, Visual Studio 2017 and Qt 5.12 are required.

Pre-trained models:

One version of GCN pre-trained model for synthetic models is supplied.

Dataset:

See the zipped file "PrintedDataset.zip".

Citation

If you find this useful for your research, please cite the following paper.

@article{shen2022gcndenoiser,
  title={GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks},
  author={Shen, yuefan and Fu, Hongbo and Du, Zhongshuo and Chen, Xiang and Burnaev, Evgeny and Zorin, Denis and Zhou, Kun and Zheng, Youyi},
  journal={ACM Trans. Graph.},
  volume={41},
  number={1},
  issn={0730-0301},
  numpages={14},
  year={2022}
}

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Acknowledgements

Part of this implementations is based on DGCNN and GNF.