GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

Qt and Pytorch implementation for GCN-Denoiser

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. New simplified version has been updated

• For .exe, windows platform is required and the CUDA PATH must be set in the system environment. Some important .dll have been supplied ( Unzip dlls.zip firstly).

• 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.

Acknowledgements

Part of this implementations is based on DGCNN and GNF.

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