  Mesh denoising is a critical technology in geometry processing that aims to
recover high-fidelity 3D mesh models of objects from their noise-corrupted
versions. In this work, we propose a learning-based normal filtering scheme for
mesh denoising called NormalNet, which maps the guided normal filtering (GNF)
into a deep network. The scheme follows the iterative framework of
filtering-based mesh denoising. During each iteration, first, the voxelization
strategy is applied on each face in a mesh to transform the irregular local
structure into the regular volumetric representation, therefore, both the
structure and face normal information are preserved and the convolution
operations in CNN(Convolutional Neural Network) can be easily performed.
Second, instead of the guidance normal generation and the guided filtering in
GNF, a deep CNN is designed, which takes the volumetric representation as
input, and outputs the learned filtered normals. At last, the vertex positions
are updated according to the filtered normals. Specifically, the iterative
training framework is proposed, in which the generation of training data and
the network training are alternately performed, whereas the ground truth
normals are taken as the guidance normals in GNF to get the target normals.
Compared to state-of-the-art works, NormalNet can effectively remove noise
while preserving the original features and avoiding pseudo-features.
