Abstract
The rapid solution of surface interpolation and other
regularization problems on massively parallel architectures is an
important problem within computer vision. Fast relaxation algorithms can
be used to integrate sparse data, resolve ambiguities in optic flow
fields, and guide stereo matching algorithms. In the present paper, an
alternative to multigrid relaxation which is much easier to implement is
presented. This approach uses conjugate-gradient descent in conjunction
with a hierarchical (multiresolution) set of basis functions. The
resulting algorithm uses a pyramid to smooth the residual vector before
the new direction is computed. Simulation results show the speed and its
dependence on the choice of interpolator, the number of smoothing
levels, and other factors. Also discussed is relationship of this
approach to other multiresolution relaxation and representation schemes
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