A Bayesian method for probable surface reconstruction and decimation

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...

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Bibliographic Details
Published in:ACM transactions on graphics 2006-01, Vol.25 (1), p.39-59
Main Authors: Diebel, James R., Thrun, Sebastian, Brünig, Michael
Format: Article
Language:English
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Summary: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 smooths 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.
ISSN:0730-0301
1557-7368
DOI:10.1145/1122501.1122504