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Carnegie Mellon Robotics Institute

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Abstract

Presents a 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.

Keywords

object recognition,range data, cueing, data filtering

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Owen Carmichael and Martial Hebert, "3D Cueing: A Data Filter For Object Recognition," IEEE Conference on Robotics and Automation (ICRA '99), May, 1999, pp. 944 - 950.

BibTeX Reference

@inproceedings{Carmichael_1999_2967,
   author = "Owen Carmichael and Martial Hebert",
   title = "3D Cueing: A Data Filter For Object Recognition",
   booktitle = "IEEE Conference on Robotics and Automation (ICRA '99)",
   pages = "944 - 950",
   month = "May",
   year = "1999",
   volume = "2",
}

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