Reliable Rolling-guided Point Normal Filtering for Surface Texture Removal

Y. Sun, H. Chen, J. Qin, H. Li, M. Wei, H. Zong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Semantic surface decomposition (SSD) facilitates various geometry processing and product re-design tasks. Filter-based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under-fitting or over-fitting. In this paper, we propose a reliable rolling-guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC-based sub-neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling-guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state-of-the-art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.

Original languageEnglish
Pages (from-to)721-732
Number of pages12
JournalComputer Graphics Forum
Issue number7
Publication statusPublished - 1 Oct 2019


  • CCS Concepts
  • • Computing methodologies → Point-based models; Shape analysis

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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