Selective Guidance Normal Filter for Geometric Texture Removal

  • Mingqiang WEI*
  • , Yidan FENG
  • , Honghua CHEN
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

8 Citations (Scopus)

Abstract

There is typically a trade-off between removing the detailed appearance (i.e., geometric textures) and preserving the intrinsic properties (i.e., geometric structures) of 3D surfaces. The conventional use of mesh vertex/facet-centered patches in many filters leads to side-effects including remnant textures, improperly filtered structures, and distorted shapes. We propose a selective guidance normal filter (SGNF) which adapts the Relative Total Variation (RTV) to a maximal/minimal scheme (mmRTV). The mmRTV measures the geometric flatness of surface patches, which helps in finding adaptive patches whose boundaries are aligned with the facet being processed. The adaptive patches provide selective guidance normals, which are subsequently used for normal filtering. The filtering smooths out the geometric textures by using guidance normals estimated from patches with maximal RTV (the least flatness), and preserves the geometric structures by using normals estimated from patches with minimal RTV (the most flatness). This simple yet effective modification of the RTV makes our SGNF specialized rather than trade off between texture removal and structure preservation, which is distinct from existing mesh filters. Experiments show that our approach is visually and numerically comparable to the state-of-the-art mesh filters, in most cases. In addition, the mmRTV is generally applicable to bas-relief modeling and image texture removal.
Original languageEnglish
Pages (from-to)4469-4482
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number12
Early online date29 Jun 2020
DOIs
Publication statusPublished - 1 Dec 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1995-2012 IEEE.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities, China (No. 3082019NS2019059).

Keywords

  • bas-relief modeling
  • geometric texture removal
  • Geometry filtering
  • mmRTV
  • selective guidance

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