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Multi-scale surface descriptors.

Gregory Cipriano1, George N Phillips, Michael Gleicher

  • 1Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. gregc@cs.wisc.edu

IEEE Transactions on Visualization and Computer Graphics
|October 17, 2009
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Summary
This summary is machine-generated.

This study introduces new multi-scale shape descriptors for surface meshes. These descriptors efficiently capture local surface geometry for improved analysis and matching, especially in protein surface studies.

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Area of Science:

  • Computer Graphics
  • Computational Geometry
  • Geometric Modeling

Background:

  • Traditional shape descriptors like curvature analyze only infinitesimal regions.
  • There is a need for multi-scale shape descriptors to characterize surface regions of varying sizes.

Purpose of the Study:

  • To develop novel multi-scale shape descriptors for surface meshes.
  • To enable robust characterization of surface regions at different scales.
  • To apply these descriptors to visualization, analysis, and matching tasks, particularly for protein surfaces.

Main Methods:

  • Fitting quadratic surfaces to local neighborhoods to capture shape statistically.
  • Utilizing simple mesh operations for efficient descriptor computation, avoiding complex parameterizations.
  • Developing a statistical approximation for further computational cost reduction.

Main Results:

  • The proposed descriptors mimic differential curvature and encode anisotropy.
  • Descriptors are computationally efficient and do not require expensive parameterizations.
  • Demonstrated applicability to surface visualization, analysis, and matching.

Conclusions:

  • The new multi-scale shape descriptors offer an efficient and effective method for characterizing surface meshes.
  • These descriptors enhance tasks in geometric analysis, particularly in the domain of protein surface analysis.