@article {IOPORT.06075521,
    author       = {Yumer, Mehmet Ersin and Kara, Levent Burak},
    title        = {Surface creation on unstructured point sets using neural networks.},
    year         = {2012},
    journal      = {CAD. Computer-Aided Design},
    volume       = {44},
    number       = {7},
    issn         = {0010-4485},
    pages        = {644-656},
    publisher    = {Elsevier Science, Oxford},
    doi          = {10.1016/j.cad.2012.03.002},
    abstract     = {Summary: We present a new point set surfacing method based on a data-driven mapping between the parametric and geometric spaces. Our approach takes as input an unstructured and possibly noisy point set representing a two-manifold in $R^{3}$. To facilitate parameterization, the set is first embedded in $R^{2}$ using neighborhood-preserving locally linear embedding. A learning algorithm is then trained to learn a mapping between the embedded two-dimensional (2D) coordinates and the corresponding three-dimensional (3D) space coordinates. The trained learner is then used to generate a tessellation spanning the parametric space, thereby producing a surface in the geometric space. This approach enables the surfacing of noisy and non-uniformly distributed point sets. We discuss the advantages of the proposed method in relation to existing methods, and show its utility on a number of test models, as well as its applications to modeling in virtual reality environments.},
    identifier   = {06075521},
}