Abstract
An approach to visual sampling and reconstruction motivated by
concepts from numerical grid generation is presented. Adaptive meshes
that can nonuniformly sample and reconstruct intensity and range data
are presented. These meshes are dynamic models which are assembled by
interconnecting nodal masses with adjustable springs. Acting as mobile
sampling sites, the nodes observe properties of the input data, such as
intensities, depths, gradients, and curvatures. Based on these nodal
observations, the springs automatically adjust their stiffnesses so as
to distribute the available degrees of freedom of the reconstructed
model in accordance with the local complexity of the input data. The
adaptive mesh algorithm runs at interactive rates with continuous 3-D
display on a graphics workstation It is applied to the adaptive sampling
and reconstruction of images and surfaces
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
