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Abstract:
The majority of the existing techniques for surface reconstruction and the
closely related problem of normal estimation are deterministic. Their main
advantages are the speed and, given a reasonably good initial input, the high
quality of the reconstructed surfaces. Nevertheless, their deterministic nature
may hinder them from effectively handling incomplete data with noise and
outliers. In our previous work [1], we applied a statistical technique, called
ensembles, to the problem of surface reconstruction. We showed that an ensemble
can improve the performance of a deterministic algorithm by putting it into a
statistics based probabilistic setting. In this paper, with several
experiments, we further study the suitability of ensembles in surface
reconstruction, and also apply ensembles to normal estimation. We experimented
with a widely used normal estimation technique [2] and Multi-level Partitions
of Unity implicits for surface reconstruction [3], showing that normal and
surface ensembles can successfully be combined to handle noisy point sets.
