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http://dx.doi.org/10.7471/ikeee.2019.23.4.1250

A Deep Learning-Based Face Mesh Data Denoising System  

Roh, Jihyun (Dept. of Computer Science, Kangwon National University)
Im, Hyeonseung (Dept. of Computer Science, Kangwon National University)
Kim, Jongmin (Dept. of Computer Science, Kangwon National University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1250-1256 More about this Journal
Abstract
Although one can easily generate real-world 3D mesh data using a 3D printer or a depth camera, the generated data inevitably includes unnecessary noise. Therefore, mesh denoising is essential to obtain intact 3D mesh data. However, conventional mathematical denoising methods require preprocessing and often eliminate some important features of the 3D mesh. To address this problem, this paper proposes a deep learning based 3D mesh denoising method. Specifically, we propose a convolution-based autoencoder model consisting of an encoder and a decoder. The convolution operation applied to the mesh data performs denoising considering the relationship between each vertex constituting the mesh data and the surrounding vertices. When the convolution is completed, a sampling operation is performed to improve the learning speed. Experimental results show that the proposed autoencoder model produces faster and higher quality denoised data than the conventional methods.
Keywords
3D mesh data; denoising; deep learning; autoencoder; convolution;
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