ENSEMBLES FOR NORMAL AND SURFACE RECONSTRUCTIONS
SCIE
SCOPUS
- Title
- ENSEMBLES FOR NORMAL AND SURFACE RECONSTRUCTIONS
- Authors
- Yoon, M; Lee, Y; Lee, S; Ivrissimtzis, I; Seidel, HP
- Date Issued
- 2006-07
- Publisher
- SPRINGER-VERLAG BERLIN
- Abstract
- The majority of the existing techniques for surface reconstruction and the closely related problem of normal estimation 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. In our previous work [1], we applied a statistical technique, called ensembles, to the problem of surface reconstruction. We showed that an ensemble can improve the performance of a deterministic algorithm by putting it into a statistics based probabilistic setting. In this paper, with several experiments, we further study the suitability of ensembles in surface reconstruction, and also apply ensembles to normal estimation. We experimented with a widely used normal estimation technique [2] and Multi-level Partitions of Unity implicits for surface reconstruction [3], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/28410
- DOI
- 10.1007/11802914_2
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 4077, no. 4077, page. 17 - 33, 2006-07
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.