Mesh denoising guided by patch normal co-filtering via kernel low-rank recovery

Mingqiang Wei, Jin Huang, Xingyu Xie, Ligang Liu, Jun Wang, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)

Abstract

Mesh denoising is a classical, yet not well-solved problem in digital geometry processing. The challenge arises from noise removal with the minimal disturbance of surface intrinsic properties (e.g., sharp features and shallow details). We propose a new patch normal co-filter (PcFilter) for mesh denoising. It is inspired by the geometry statistics which show that surface patches with similar intrinsic properties exist on the underlying surface of a noisy mesh. We model the PcFilter as a low-rank matrix recovery problem of similar-patch collaboration, aiming at removing different levels of noise, yet preserving various surface features. We generalize our model to pursue the low-rank matrix recovery in the kernel space for handling the nonlinear structure contained in the data. By making use of the block coordinate descent minimization and the specifics of a proximal based coordinate descent method, we optimize the nonlinear and nonconvex objective function efficiently. The detailed quantitative and qualitative results on synthetic and real data show that the PcFilter competes favorably with the state-of-the-art methods in surface accuracy and noise-robustness.

Original languageEnglish
Article number2865363
Pages (from-to)2910-2926
Number of pages17
JournalIEEE Transactions on Visualization and Computer Graphics
Volume25
Issue number10
DOIs
Publication statusPublished - 11 Aug 2019

Keywords

  • Kernel low-rank recovery
  • Patch normal co-filtering
  • Self-similarity
  • Terms—Mesh denoising

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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