“MCMC-based Multiview Reconstruction of Piecewise Smooth Subdivision
Curves with a Variable Number of Control Points”
by
M. Kaess,
R. Zboinski, and
F. Dellaert.
In Eur. Conf. on Computer Vision, ECCV, (Prague, Czech Republic),
May 2004, pp. 329-341. Acceptance ratio 34.2% (190 of 555).

Abstract

We investigate the automated reconstruction of piecewise smooth 3D curves,
using subdivision curves as a simple but flexible curve representation.
This representation allows tagging corners to model non-smooth features
along otherwise smooth curves. We present a reversible jump Markov chain
Monte Carlo approach which obtains an approximate posterior distribution
over the number of control points and tags. In a Rao-Blackwellization
scheme, we integrate out the control point locations, reducing the
variance of the resulting sampler. We apply this general methodology to
the reconstruction of piecewise smooth curves from multiple calibrated
views, in which the object is segmented from the background using a Markov
random field approach. Results are shown for multiple images of two pot
shards as would be encountered in archaeological applications.

@inproceedings{Kaess04eccv,
author = {M. Kaess and R. Zboinski and F. Dellaert},
title = {{MCMC}-based Multiview Reconstruction of Piecewise Smooth
Subdivision Curves with a Variable Number of Control Points},
booktitle = {Eur. Conf. on Computer Vision, ECCV},
series = {Lecture Notes in Computer Science},
volume = {3023},
pages = {329-341},
publisher = {Springer},
address = {Prague, Czech Republic},
month = {May},
year = {2004},
isbn = {3-540-21982-X},
note = {Acceptance ratio 34.2% (190 of 555)}
}