Detecting planar patches in an unorganised set of points in space

Michel Bercovier*, Moshe Luzon, Elan Pavlov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Given n points in 3D, sampled from k original planes (with sampling errors), a new probabilistic method for detecting coplanar subsets of points in O(k6) steps is introduced. The planes are reconstructed with small probability of error. The algorithm reduces the problem of reconstruction to the problem of clustering in R3 and thereby produces effective results. The algorithm is significantly faster than other known algorithms in most cases.

Original languageEnglish
Pages (from-to)153-166
Number of pages14
JournalAdvances in Computational Mathematics
Volume17
Issue number1-2
DOIs
StatePublished - 2002

Keywords

  • Clustering
  • Distance
  • Hyperplanes
  • Measurement
  • Probabilistic algorithm
  • Robust-metric

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