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Graphical Models 64, 199229 (2002) doi:10.1006/gmod.2002.0574
 

Summary: Graphical Models 64, 199229 (2002)
doi:10.1006/gmod.2002.0574
Normal Vector Voting: Crease Detection and
Curvature Estimation on Large, Noisy Meshes
D. L. Page, Y. Sun, A. F. Koschan, J. Paik, and M. A. Abidi
Imaging, Robotics, and Intelligent Systems Laboratory, University of Tennessee,
Knoxville, Tennessee 37996-2100
E-mail: davidpage@ieee.org
Received September 12, 2001; accepted May 14, 2002
This paper describes a robust method for crease detection and curvature estimation
on large, noisy triangle meshes. We assume that these meshes are approximations
of piecewise-smooth surfaces derived from range or medical imaging systems and
thus may exhibit measurement or even registration noise. The proposed algorithm,
which we call normal vector voting, uses an ensemble of triangles in the geodesic
neighborhood of a vertex--instead of its simple umbrella neighborhood--to esti-
mate the orientation and curvature of the original surface at that point. With the
orientation information, we designate a vertex as either lying on a smooth surface,
following a crease discontinuity, or having no preferred orientation. For vertices on
a smooth surface, the curvature estimation yields both principal curvatures and prin-
cipal directions while for vertices on a discontinuity we estimate only the curvature

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee

 

Collections: Computer Technologies and Information Sciences