  In this paper, we present 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 show such a graph
representation naturally captures the geometry features while being lightweight
for both training and inference. To facilitate effective feature learning, our
network exploits both static and dynamic edge convolutions, which allow us to
learn information from both the explicit mesh structure and potential implicit
relations among unconnected neighbors. To better approximate an unknown noise
function, we introduce a cascaded optimization paradigm to progressively
regress the noise-free facet normals with multiple GCNs. GCN-Denoiser achieves
the new state-of-the-art results in multiple noise datasets, including CAD
models often containing sharp features and raw scan models with real noise
captured from different devices. We also create a new dataset called PrintData
containing 20 real scans with their corresponding ground-truth meshes for the
research community. Our code and data are available in
https://github.com/Jhonve/GCN-Denoiser.
