Surface Reconstruction with Smart Growing Cells

  • Hendrik Annuth
  • Christian-A. Bohn
Part of the Studies in Computational Intelligence book series (SCI, volume 321)


We propose Growing Cells Meshing (GCM) - a reconstruction algorithm which creates triangle meshes from clouds of arbitrary point samples registered on object surfaces. GCM is different to classical approaches in the way that it uses an artificial neural network together with an iterative learning technique to represent the triangle mesh. Based on the Growing Cell Structures (GCS) approach [3] we introduce the Smart Growing Cells (SGC) network as extension to fulfill the requirements of surface reconstruction. Our method profits from the well-know benefits entailed by neural networks, like autonomy, robustness, scalability, the ability of retrieving information from very complex data, and adaptability. On the downside, typical drawbacks like undesirable smoothing of information, inability to exactly model detailed, discontinuous data, or a vast amount of computing resources at big network sizes are overcome for the application of surface reconstruction. The GCM approach creates high-quality triangulations of billions of points in few minutes. It perfectly covers any amount and distribution of samples, holes, and inconsistent data. It discovers and represents edges, manages clusters of input sample points, and it is capable of dynamically adapting to incremental sample data.


Surface Reconstruction Reference Vector Discontinuity Modeling Vertex Valence Edge Collapse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hendrik Annuth
    • 1
  • Christian-A. Bohn
    • 1
  1. 1.Wedel University of Applied SciencesWedel, FRG

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