 
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 379962100
Email: 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 piecewisesmooth 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 vertexinstead of its simple umbrella neighborhoodto 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
