Robust Mesh Denoising via Triple Sparsity

Sensors
Saishang ZhongZheng Liu

Abstract

Mesh denoising is to recover high quality meshes from noisy inputs scanned from the real world. It is a crucial step in geometry processing, computer vision, computer-aided design, etc. Yet, state-of-the-art denoising methods still fall short of handling meshes containing both sharp features and fine details. Besides, some of the methods usually introduce undesired staircase effects in smoothly curved regions. These issues become more severe when a mesh is corrupted by various kinds of noise, including Gaussian, impulsive, and mixed Gaussian⁻impulsive noise. In this paper, we present a novel optimization method for robustly denoising the mesh. The proposed method is based on a triple sparsity prior: a double sparse prior on first order and second order variations of the face normal field and a sparse prior on the residual face normal field. Numerically, we develop an efficient algorithm based on variable-splitting and augmented Lagrange method to solve the problem. The proposed method can not only effectively recover various features (including sharp features, fine details, smoothly curved regions, etc), but also be robust against different kinds of noise. We testify effectiveness of the proposed method on synthetic meshes and ...Continue Reading

References

Jun 30, 2006·IEEE Transactions on Visualization and Computer Graphics·Charlie C L Wang
Jul 12, 2007·IEEE Transactions on Visualization and Computer Graphics·Xianfang SunFrank Langbein
Dec 22, 2010·IEEE Transactions on Visualization and Computer Graphics·Youyi ZhengChiew-Lan Tai
Jun 18, 2011·Journal of Molecular Graphics & Modelling·You XuYun Tang
Sep 12, 2015·IEEE Transactions on Visualization and Computer Graphics· Huayan Zhang Jiansong Deng
Nov 20, 2015·IEEE Transactions on Visualization and Computer Graphics·Xuequan LuWenzhi Chen
Jul 12, 2018·IEEE Transactions on Visualization and Computer Graphics·Sunil Kumar YadavKonrad Polthier

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Citations

Jan 14, 2021·Sensors·Mingqiang GuoZheng Liu

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Software Mentioned

Eigen
Kinect
Google Project Tango
Intel RealSense
Math Kernel Library ( MKL
Taucs
Xtion Pro

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