<div class="csl-bib-body">
<div class="csl-entry">Lenz, C., Aguinsky, L. F., Hössinger, A., & Weinbub, J. (2023). A Complementary Topographic Feature Detection Algorithm Based on Surface Curvature for Three-Dimensional Level-Set Functions. <i>Journal of Scientific Computing</i>, <i>94</i>(3), Article 71. https://doi.org/10.1007/s10915-023-02133-5</div>
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dc.identifier.issn
0885-7474
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/187436
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dc.description.abstract
The level-set method is widely used in expanding front simulations in numerous fields of computational research, such as computer graphics, physics, or microelectronics. In the latter, the level-set method is employed for topography simulations of semiconductor device fabrication processes, being driven by complicated physical and chemical models. These models tend to produce surfaces with critical points where accuracy is paramount. To efficiently increase the accuracy in regions neighboring these critical points, automatic hierarchical domain refinement is required, guided by robust feature detection. Feature detection has to be computationally efficient and sufficiently accurate to reliably detect the critical points. To that end, we present a fast parallel geometric feature detection algorithm for three-dimensional level-set functions. Our approach is based on two different, complementary curvature calculation methods of the zero level-set and an optimized feature detection parameter to detect features. For performance reasons, our algorithm can be in principal linked to different curvature calculation methods, however, as will be discussed, two particularly attractive options are available: (i) A novel extension of the standard curvature calculation method for level-set functions, and (ii) an often disregarded method for calculating the curvature due to its purported low numerical accuracy. We show, however, that the latter is still a viable option, and that our algorithm is able to reliably detect features on geometries stemming from complicated, practically relevant geometries. Our algorithm and findings are applicable to other fields of applications such as surface simplification.
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dc.description.sponsorship
Christian Doppler Forschungsgesells
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dc.language.iso
en
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dc.publisher
SPRINGER/PLENUM PUBLISHERS
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dc.relation.ispartof
Journal of Scientific Computing
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
level-set method
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dc.subject
curvature
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dc.subject
feature detection
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dc.subject
parallel algorithm
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dc.title
A Complementary Topographic Feature Detection Algorithm Based on Surface Curvature for Three-Dimensional Level-Set Functions