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Efficient multiple model recognition in cluttered 3-D scenes

Andrew Johnson and Martial Hebert
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '98), pp. 671 - 677, June, 1998

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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.

BibTeX Reference
@conference{Johnson-1998-14700,
title = {Efficient multiple model recognition in cluttered 3-D scenes},
author = {Andrew Johnson and Martial Hebert},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '98)},
month = {June},
year = {1998},
pages = {671 - 677},
}
2017-09-13T10:49:26+00:00