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Fast low-memory streaming MLS reconstruction of point-sampled surfaces - Zurich Open Repository and Archive


Pajarola, R; Cuccuru, G; Gobbetti, E; Marton, F; Pintus, R (2009). Fast low-memory streaming MLS reconstruction of point-sampled surfaces. In: Graphics Interface, Kelowna, Canada, 25 May 2009 - 27 May 2009, 15-22.

Abstract

We present a simple and efficient method for reconstructing triangulated surfaces from massive oriented point sample datasets. The method combines streaming and parallelization, moving least-squares (MLS) projection, adaptive space
subdivision, and regularized isosurface extraction. Besides
presenting the overall design and evaluation of the system,
our contributions include methods for keeping in-core data
structures complexity purely locally output-sensitive and for exploiting both the explicit and implicit data produced by a MLS projector to produce tightly fitting regularized triangulations using a primal isosurface extractor. Our results show that the system is fast, scalable, and accurate. We are able to process models with several hundred million points in about an hour and outperform current fast streaming reconstructors in terms of geometric accuracy.

Abstract

We present a simple and efficient method for reconstructing triangulated surfaces from massive oriented point sample datasets. The method combines streaming and parallelization, moving least-squares (MLS) projection, adaptive space
subdivision, and regularized isosurface extraction. Besides
presenting the overall design and evaluation of the system,
our contributions include methods for keeping in-core data
structures complexity purely locally output-sensitive and for exploiting both the explicit and implicit data produced by a MLS projector to produce tightly fitting regularized triangulations using a primal isosurface extractor. Our results show that the system is fast, scalable, and accurate. We are able to process models with several hundred million points in about an hour and outperform current fast streaming reconstructors in terms of geometric accuracy.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:27 May 2009
Deposited On:09 Feb 2010 13:25
Last Modified:11 Aug 2017 14:19

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