License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SoCG.2022.9
URN: urn:nbn:de:0030-drops-160170
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Bakke Bjerkevik, Håvard

Tighter Bounds for Reconstruction from ε-Samples

LIPIcs-SoCG-2022-9.pdf (0.8 MB)


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 ℝ² 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.

BibTeX - Entry

  author =	{Bakke Bjerkevik, H\r{a}vard},
  title =	{{Tighter Bounds for Reconstruction from \epsilon-Samples}},
  booktitle =	{38th International Symposium on Computational Geometry (SoCG 2022)},
  pages =	{9:1--9:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-227-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{224},
  editor =	{Goaoc, Xavier and Kerber, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-160170},
  doi =		{10.4230/LIPIcs.SoCG.2022.9},
  annote =	{Keywords: Curve reconstruction, surface reconstruction, \epsilon-sampling}

Keywords: Curve reconstruction, surface reconstruction, ε-sampling
Collection: 38th International Symposium on Computational Geometry (SoCG 2022)
Issue Date: 2022
Date of publication: 01.06.2022

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