Feature-preserving mesh denoising via normal guided quadric error metrics

Jinze Yu, Mingqiang Wei, Jing Qin, Jianhuang Wu, Pheng Ann Heng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

While modern optical and laser 3D scanners can generate high accuracy mesh models, to largely avoid their introducing noise which prohibits practical applications still results in high cost. Thus, optimizing noisy meshes while preserving their geometric details is necessary for production, which still remains as challenging work. In this paper we propose a novel and efficient two-stage feature-preserving mesh denoising framework which can remove noise while preserving fine features of a surface mesh. We improve the capability of feature preservation of our vertex updating scheme by employing an extension of the quadric error metrics (QEM), which can track and minimize updating errors and hence well preserve the overall shape as well as detailed features of a mesh. We further leverage vertex normals to guide the vertex updating process, as the normal field of a mesh reflects the geometry of the underlying surface. In addition, to obtain a more accurate normal field to guide vertex updating, we develop an improved normal filter by integrating advantages of existing filters. Compared with traditional gradient descent based schemes, our method performs better on challenging regions with rich geometric features. Moreover, a local entropy metric is proposed to measure stability of a mesh and the effectiveness of vertex updating algorithms. Qualitative and quantitative experiments demonstrate that our approach can effectively remove noise from noisy meshes while preserving or recovering geometrical features of original objects.
Original languageEnglish
Pages (from-to)57-68
Number of pages12
JournalOptics and Lasers in Engineering
Volume62
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Bilateral normal filtering
  • Local entropy metric
  • Mesh denoising
  • Normal guided quadric error metrics
  • Vertex updating

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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