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Conceptual Design of Freeform Surfaces From Unstructured Point Sets Using Neural Network Regression

[+] Author Affiliations
Mehmet Ersin Yumer, Levent Burak Kara

Carnegie Mellon University, Pittsburgh, PA

Paper No. DETC2011-48324, pp. 675-685; 11 pages
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 37th Design Automation Conference, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5482-2
  • Copyright © 2011 by ASME


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 R 3 . To facilitate parametrization, the set is first embedded in R 2 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.

Copyright © 2011 by ASME



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