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Mesh denoising is a critical technology in geometry processing that aims torecover high-fidelity 3D mesh models of objects from their noise-corruptedversions. In this work, we propose a learning-based normal filtering scheme formesh denoising called NormalNet, which maps the guided normal filtering (GNF)into a deep network. The scheme follows the iterative framework offiltering-based mesh denoising. During each iteration, first, the voxelizationstrategy is applied on each face in a mesh to transform the irregular localstructure into the regular volumetric representation, therefore, both thestructure and face normal information are preserved and the convolutionoperations in CNN(Convolutional Neural Network) can be easily performed.Second, instead of the guidance normal generation and the guided filtering inGNF, a deep CNN is designed, which takes the volumetric representation asinput, and outputs the learned filtered normals. At last, the vertex positionsare updated according to the filtered normals. Specifically, the iterativetraining framework is proposed, in which the generation of training data andthe network training are alternately performed, whereas the ground truthnormals are taken as the guidance normals in GNF to get the target normals.Compared to state-of-the-art works, NormalNet can effectively remove noisewhile preserving the original features and avoiding pseudo-features.

NormalNet: Learning-based Normal Filtering for Mesh Denoising