Surface Reconstruction Method Based on a Growing Self-Organizing Map
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Growing self-reconstruction maps
IEEE Transactions on Neural Networks
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.