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Abstract

In this paper, we propose to combine Kazhdan's FFT-based approach to surface
reconstruction from oriented points with adaptive subdivision and partition of
unity blending techniques. This removes the main drawback of the FFT-based
approach which is a high memory consumption for geometrically complex datasets.
This allows us to achieve a higher reconstruction accuracy compared with the
original global approach. Furthermore, our reconstruction process is guided by
a global error control accomplished by computing the Hausdorff distance of
selected input samples to intermediate reconstructions. The advantages of our
surface reconstruction method include also a more robust surface restoration in
regions where the surface folds back to itself.