  This paper presents a deep normal filtering network, called DNF-Net, for mesh
denoising. To better capture local geometry, our network processes the mesh in
terms of local patches extracted from the mesh. Overall, DNF-Net is an
end-to-end network that takes patches of facet normals as inputs and directly
outputs the corresponding denoised facet normals of the patches. In this way,
we can reconstruct the geometry from the denoised normals with feature
preservation. Besides the overall network architecture, our contributions
include a novel multi-scale feature embedding unit, a residual learning
strategy to remove noise, and a deeply-supervised joint loss function. Compared
with the recent data-driven works on mesh denoising, DNF-Net does not require
manual input to extract features and better utilizes the training data to
enhance its denoising performance. Finally, we present comprehensive
experiments to evaluate our method and demonstrate its superiority over the
state of the art on both synthetic and real-scanned meshes.
