Before revision:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks. To remedy this problem, we propose an iterative framework to generate enough face-normal pairs, based on which a convolutional neural networks (CNNs) based scheme is designed for guidance normal learning. Moreover, to facilitate the 3D convolution operation in CNNs, for each face in mesh, we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form.
After revision:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet 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 mesh, we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form.