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Surface and normal ensembles for surface reconstruction

Yoon, Mincheol; Lee, Yunjin; Lee, Seungyong; Ivrissimtzis, Ioannis; Seidel, Hans-Peter


Mincheol Yoon

Yunjin Lee

Seungyong Lee

Hans-Peter Seidel


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.


Yoon, M., Lee, Y., Lee, S., Ivrissimtzis, I., & Seidel, H. (2007). Surface and normal ensembles for surface reconstruction. Computer-Aided Design, 39(5), 408-420.

Journal Article Type Article
Publication Date May 1, 2007
Deposit Date Jun 6, 2007
Journal Computer-Aided Design
Print ISSN 0010-4485
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 39
Issue 5
Pages 408-420
Keywords Surface reconstruction, Normal estimation, Ensemble.