Min-cut based segmentation of point clouds

Aleksey Golovinskiy, Thomas Funkhouser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

171 Scopus citations

Abstract

We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our system on an outdoor urban scan, quantitatively evaluate our algorithm on a test set of about 1000 objects, and compare to several alternative approaches.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages39-46
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: Sep 27 2009Oct 4 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Other

Other2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
CountryJapan
CityKyoto
Period9/27/0910/4/09

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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