IMAGE-BASED 3D MESH DENOISING THROUGH A BLOCK MATCHING 3D CONVOLUTIONAL NEURAL NETWORK FILTERING APPROACH

Throughout the years, several works have been proposed for 3D mesh denoising. Nevertheless, despite their reconstruction quality, there are still challenges related to the preservation of the fine surface features. Motivated by the impressive results of image denoising by 3D transform-domain collaborative filtering (CF), we extend it to 3D mesh denoising. CF is also capable of revealing the finest details shared by grouped blocks while preserving at the same time the unique features of each block. A new promising approach suggests unrolling the computational pipeline of CF into a convolutional neural network (CNN) structure increasing significantly the efficiency of this solution. In this paper, we successfully extend and apply this method to 3D meshes making a transition from face normals to pixels. Extensive evaluation studies carried out using a variety of 3D meshes verify that the proposed approach achieves plausible reconstruction outputs and provides very promising results.