This paper presents a new point set surfacing method that employs neural networks for regression. Our technique takes as input unstructured and possibly noisy point sets representing two-manifolds in R3. To facilitate parametrization, the set is first embedded in R2 using neighborhood preserving locally linear embedding. A neural network is then constructed and trained that learns a mapping between the embedded 2D parametric coordinates and the corresponding 3D space coordinates. The trained network is then used to generate a tessellation that spans the parametric space, thereby producing a surface in the original space. This approach enables the surfacing of noisy and non-uniformly distributed point sets, and can be applied to open or closed surfaces. We show the utility of the proposed method on a number of test models, as well as its application to freeform surface creation in virtual reality environments.

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