A Bayesian Method for Probable Surface Reconstruction and Decimation

Jamies Diebel, Sebastian Thrun, and Michael Bruening

We present a Bayesian technique for the reconstruction and subsequent decimation of 3D surface models from noisy sensor data. The method uses oriented probabilistic models of the measurement noise, and combines them with feature-enhancing prior probabilities over 3D surfaces. When applied to surface reconstruction, the method simultaneously smoothes noisy regions while enhancing features, such as corners. When applied to surface decimation, it finds models that closely approximate the original mesh when rendered. The method is applied in the context of computer animation, where it finds decimations that minimize the visual error even under nonrigid deformations.

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Bibtex Entry:

@ARTICLE{Diebel05,
  AUTHOR        = {J. Diebel and S. Thrun and M. Bruening},
  TITLE         = {A Bayesian Method for Probable Surface Reconstruction and Decimation},
  JOURNAL	= {ACM Transactions on Graphics},
  YEAR	= {2006},
  VOLUME	= {25},
  NUMBER	= {1}
}