Ensembles for Normal and Surface Reconstructions

  • Mincheol Yoon
  • Yunjin Lee
  • Seungyong Lee
  • Ioannis Ivrissimtzis
  • Hans-Peter Seidel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)


The majority of the existing techniques for surface reconstruction and the closely related problem of normal estimation are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. In our previous work [1], we applied a statistical technique, called ensembles, to the problem of surface reconstruction. We showed that an ensemble can improve the performance of a deterministic algorithm by putting it into a statistics based probabilistic setting. In this paper, with several experiments, we further study the suitability of ensembles in surface reconstruction, and also apply ensembles to normal estimation. We experimented with a widely used normal estimation technique [2] and Multi-level Partitions of Unity implicits for surface reconstruction [3], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.


Point Cloud Normal Estimation Normal Ensemble Ensemble Member Surface Reconstruction 
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 2006

Authors and Affiliations

  • Mincheol Yoon
    • 1
  • Yunjin Lee
    • 1
  • Seungyong Lee
    • 1
  • Ioannis Ivrissimtzis
    • 2
  • Hans-Peter Seidel
    • 3
  1. 1.POSTECH 
  2. 2.Coventry University 
  3. 3.MPI Informatik 

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