Surface and normal ensembles for surface reconstruction

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

Abstract

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.

Citation

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. https://doi.org/10.1016/j.cad.2007.02.008

• 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
• DOI: https://doi.org/10.1016/j.cad.2007.02.008
• Keywords: Surface reconstruction, Normal estimation, Ensemble.
• Public URL: https://durham-repository.worktribe.com/output/1564743