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:

Pytroch C++ 1.2.0 , Eigen, Flann and OpenMesh at runtime.
Pytorch 1.2.0, numpy, Scipy 1.4.1 and tensorbordx 1.13 (>python3.5) in training stage.

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}
}

Waiting for updating...

Acknowledgements

Part of this implementations is based on DGCNN and GNF.

zouhongwei/GCN-Denoiser