Tighter Bounds for Reconstruction from ε-Samples

Havard Bakke Bjerkevik

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

We show that reconstructing a curve in R d for d = 2 from a 0.66-sample is always possible using an algorithm similar to the classical NN-Crust algorithm. Previously, this was only known to be possible for 0.47-samples in R 2 and 1 3 -samples in R d for d = 3. In addition, we show that there is not always a unique way to reconstruct a curve from a 0.72-sample; this was previously only known for 1-samples. We also extend this non-uniqueness result to hypersurfaces in all higher dimensions.

Originalspracheenglisch
Titel38th International Symposium on Computational Geometry (SoCG 2022)
Redakteure/-innenXavier Goaoc, Michael Kerber
Herausgeber (Verlag)Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Seiten9:1-9:17
ISBN (elektronisch) 978-3-95977-227-3
DOIs
PublikationsstatusVeröffentlicht - 1 Juni 2022
Veranstaltung38th International Symposium on Computational Geometry: SoCG 2022 - Berlin, Germany, Berlin, Deutschland
Dauer: 7 Juni 202210 Juni 2022
https://www.inf.fu-berlin.de/inst/ag-ti/socg22/socg.html

Publikationsreihe

NameLeibniz International Proceedings in Informatics, LIPIcs
Band224
ISSN (Print)1868-8969

Konferenz

Konferenz38th International Symposium on Computational Geometry
KurztitelSoCG 2022
Land/GebietDeutschland
OrtBerlin
Zeitraum7/06/2210/06/22
Internetadresse

ASJC Scopus subject areas

  • Software

Fields of Expertise

  • Information, Communication & Computing

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