The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.

Notes

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

Associated Lab(s) / Group(s): 3D Computer Vision Group

Associated Project(s): 3D Object Recognition

Text Reference

Andrew Johnson and Martial Hebert, "Efficient multiple model recognition in cluttered 3-D scenes," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '98), June, 1998, pp. 671 - 677.

BibTeX Reference

@inproceedings{Johnson_1998_3601,
   author = "Andrew Johnson and Martial Hebert",
   title = "Efficient multiple model recognition in cluttered 3-D scenes",
   booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '98)",
   pages = "671 - 677",
   month = "June",
   year = "1998",
}

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