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• W1990561713 abstract "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." @default.
• W1990561713 created "2016-06-24" @default.
• W1990561713 creator A5043886864 @default.
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• W1990561713 date "2011-01-01" @default.
• W1990561713 modified "2023-10-16" @default.
• W1990561713 title "Conceptual Design of Freeform Surfaces From Unstructured Point Sets Using Neural Network Regression" @default.
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• W1990561713 doi "https://doi.org/10.1115/detc2011-48324" @default.
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