Efficient multiple model recognition in cluttered 3-D scenes - Robotics Institute Carnegie Mellon University

Efficient multiple model recognition in cluttered 3-D scenes

Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 671 - 677, June, 1998

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

@conference{Johnson-1998-14700,
author = {Andrew Johnson and Martial Hebert},
title = {Efficient multiple model recognition in cluttered 3-D scenes},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {1998},
month = {June},
pages = {671 - 677},
}