The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a 3D 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, "Using spin images for efficient object recognition in cluttered 3D scenes," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, May, 1999, pp. 433 - 449.

BibTeX Reference

@article{Johnson_1999_3598,
   author = "Andrew Johnson and Martial Hebert",
   title = "Using spin images for efficient object recognition in cluttered 3D scenes",
   journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
   pages = "433 - 449",
   month = "May",
   year = "1999",
   volume = "21",
   number = "5",
}

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