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Surface and normal ensembles for surface reconstruction.

Yoon, M. and Lee, Y. and Lee, S. and Ivrissimtzis, I. and Seidel, H-P. (2007) 'Surface and normal ensembles for surface reconstruction.', Computer-aided design., 39 (5). pp. 408-420.


The majority of the existing techniques for surface reconstruction and the closely related problem of normal reconstruction 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. An ensemble is a statistical technique which can improve the performance of deterministic algorithms by putting them into a statistics based probabilistic setting. In this paper, we study the suitability of ensembles in normal and surface reconstruction. We experimented with a widely used normal reconstruction technique [Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. Surface reconstruction from unorganized points. Computer Graphics 1992;71–8] and Multi-level Partitions of Unity implicits for surface reconstruction [Ohtake Y, Belyaev A, Alexa M, Turk G, Seidel H-P. Multi-level partition of unity implicits. ACM Transactions on Graphics 2003;22(3):463–70], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.

Item Type:Article
Keywords:Surface reconstruction, Normal estimation, Ensemble.
Full text:Full text not available from this repository.
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Date accepted:No date available
Date deposited:No date available
Date of first online publication:May 2007
Date first made open access:No date available

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