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Robust Crease Detection and Curvature Estimation of Piecewise Smooth Surfaces from Triangle Mesh Approximations Using Normal Voting
 

Summary: Robust Crease Detection and Curvature Estimation of Piecewise Smooth
Surfaces from Triangle Mesh Approximations Using Normal Voting
D. L. Page, A. Koschan, Y. Sun, J. Paik, and M. A. Abidi
Imaging, Robotics, and Intelligent Systems Laboratory
The University of Tennessee
Knoxville, TN 37996
page@iristown.engr.utk.edu
Abstract
In this paper, we describe a robust method for the es-
timation of curvature on a triangle mesh, where this mesh
is a discrete approximation of a piecewise smooth surface.
The proposed method avoids the computationally expensive
process of surface fitting and instead employs normal vot-
ing to achieve robust results. This method detects crease
discontinuities on the surface to improve estimates near
those creases. Using a voting scheme, the algorithm es-
timates both principal curvatures and principal directions
for smooth patches. The entire process requires one user
parameter--the voting neighborhood size, which is a func-
tion of sampling density, feature size, and measurement

  

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

 

Collections: Computer Technologies and Information Sciences