3-D cueing: a data filter for object recognition

Owen Carmichael, Martial Hebert

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

This paper presents a novel method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called '3D cueing,' uses shape signatures from object models as the basis for a fast, probabilistic classification system which rates scene points in terms of their likelihood of belonging to a model. This algorithm, which could be used as a front-end for any traditional 3D matching technique, is demonstrated using several models and cluttered scenes in which the model occupies between 1% and 50% of the data points.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherIEEE
Pages944-950
Number of pages7
Volume2
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1999 IEEE International Conference on Robotics and Automation, ICRA99 - Detroit, MI, USA
Duration: May 10 1999May 15 1999

Other

OtherProceedings of the 1999 IEEE International Conference on Robotics and Automation, ICRA99
CityDetroit, MI, USA
Period5/10/995/15/99

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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  • Cite this

    Carmichael, O., & Hebert, M. (1999). 3-D cueing: a data filter for object recognition. In Proceedings - IEEE International Conference on Robotics and Automation (Vol. 2, pp. 944-950). IEEE.