Estimating Gaussian Curvatures from 3D Meshes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

16 Scopus Citations
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  • Jingliang Peng
  • Qing Li
  • C. C. Jay Kuo
  • Manli Zhou


Original languageEnglish
Pages (from-to)270-280
Journal / PublicationProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 2003
Externally publishedYes


TitleHuman Vision and Electronic Imaging VIII
PlaceUnited States
CitySanta Clara, CA
Period21 - 24 January 2003


A new approach to estimate the surface curvatures from 3D triangular mesh surfaces with Gaussian curvature's geometry interpretation is proposed in this work. Unlike previous work, the proposed method does not use local surface fitting, partial derivative computation, or oriented normal vector recovery. Instead, the Gaussian curvature is estimated at a vertex as the area of its small neighborhood under the Gaussian map divided by the area of that neighborhood. The proposed approach can handle vertices with the zero Gaussian curvature uniformly without localizing them as a separate process. The performance is further improved with the local Bezier curve approximation and subdivision. The effectiveness of the proposed approach for meshes with a large range of coarseness is demonstrated by experiments. The application of the proposed method to 3D surface segmentation and 3D mesh feature extraction is also discussed.

Research Area(s)

  • 3D feature extraction, 3D mesh, 3D surface segmentation, Differential geometry, Gaussian curvature

Citation Format(s)