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Authors: Paul Rosenthal 1 ; Vladimir Molchanov 2 and Lars Linsen 3

Affiliations: 1 University of Rostock, Germany ; 2 Jacobs University, Germany ; 3 Jacobs University and Westfälische Wilhelms-Universität Münster, Germany

ISBN: 978-989-758-289-9

Keyword(s): Surface Extraction, Isosurfaces, Level Sets, Unstructured Point-based Volume Data.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Scientific Visualization ; Spatial Data Visualization ; Volume Visualization

Abstract: PDE-based methods like level-set methods are a valuable and well-established approach in visualization to extract surfaces from volume data. We propose a novel method for the efficient computation of a signed-distance function to a surface in point-cloud representation and embed this method into a framework for PDE-based surface extraction from point-based volume data. This enables us to develop a fast level-set approach for extracting smooth isosurfaces from data with highly varying point density. The level-set method operates just locally in a narrow band around the zero-level set. It relies on the explicit representation of the zero-level set and the fast generation of a signed-distance function to it. A level-set step is executed in the narrow band utilizing the properties and derivatives of the signed-distance function. The zero-level set is extracted after each level-set step using direct isosurface extraction from point-based volume data. In contrast to existing methods for uns tructured data which operate on implicit representations, our approach can use any starting surface for the level-set approach. Since for most applications a rough estimate of the desired surface can be obtained quickly, the overall level-set process can be shortened significantly. Additionally, we avoid the computational overhead and numerical difficulties of PDE-based reinitialization. Still, our approach achieves equivalent quality, flexibility, and robustness as existing methods for point-based volume data. (More)

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Paper citation in several formats:
Rosenthal P., Molchanov V. and Linsen L. (2018). SmoothIsoPoints: Making PDE-based Surface Extraction from Point-based Volume Data Fast.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and ApplicationsISBN 978-989-758-289-9, pages 17-28. DOI: 10.5220/0006537200170028

@conference{ivapp18,
author={Paul Rosenthal and Vladimir Molchanov and Lars Linsen},
title={SmoothIsoPoints: Making PDE-based Surface Extraction from Point-based Volume Data Fast},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
year={2018},
pages={17-28},
doi={10.5220/0006537200170028},
isbn={978-989-758-289-9},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
TI - SmoothIsoPoints: Making PDE-based Surface Extraction from Point-based Volume Data Fast
SN - 978-989-758-289-9
AU - Rosenthal P.
AU - Molchanov V.
AU - Linsen L.
PY - 2018
SP - 17
EP - 28
DO - 10.5220/0006537200170028

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