Multi-Scale Surface Descriptors

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2009-11, Vol.15 (6), p.1201-1208
Main Authors: Cipriano, G., Phillips, G.N., Gleicher, M.
Format: Article
Language:English
Subjects:
Anisotropic magnetoresistance
Application software
Approximation
Computational efficiency
Curvature
Data analysis
Data visualization
descriptors
Matching
Mathematical analysis
Mathematics
Mesh generation
npr
Parametrization
Proteins
Shape
shape matching
stylized rendering
Surface fitting
Tasks
Visualization
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Summary:Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2009.168