Abstract:
Tensor voting (TV) is a method for inferring geometric structures
from sparse, irregular and possibly
noisy input. It was initially proposed by Guy and Medioni and has
been applied to several
computer vision applications. TV generates a dense output field in a
domain by dispersing information
associated with sparse input tokens. In 3-D this implies that a
surface can be generated from a set of
input data, giving tensor voting a potential application in surface
modeling. We study the tensor voting
methodology in a modeling context by implementing a simple 3-D
modeling tool. The user creates a
surface from a set of points and normals. The user may interact
with these tokens in order to modify the
surface. We describe the results of our investigation.
