Home/Large Data Sets and Confusing Scenes in 3-D surface Matching and Recognition

Large Data Sets and Confusing Scenes in 3-D surface Matching and Recognition

Owen Carmichael, Daniel Huber and Martial Hebert
Conference Paper, Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling (3DIM'99), pp. 358-367, October, 1999

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In this paper, we report on recent extensions to a surface matching algorithm based on local 3-D signatures. This algorithm was previously shown to be effective in view registration of general surfaces and in object recogni-tion from 3-D model data bases. We describe extensions to the basic matching algorithm which will enable it to address several challenging, and often overlooked, problems encountered with real data. First, we describe extensions that allow us to deal with data sets with large variations in resolution and with large data sets for which computational efficiency is a major issue. The applicability of the enhanced matching algorithm is illustrated by an example application: the construction of large terrain maps and the construction of accurate 3-D models from unregistered views. Second, we describe extensions that facilitate the use of 3-D object recognition in cases in which the scene con-tains a large amount of clutter (e.g., the object occupies 1% of the scene) and in which the scene presents a high degree of confusion (e.g., the model shape is close to other shapes in the scene.) Those last two extensions involve learning recognition strategies from the descrip-tion of the model and from the performance of the recog-nition algorithm using Bayesian and memory-based learning techniques, respectively.

BibTeX Reference
@conference{Carmichael-1999-15036,
title = {Large Data Sets and Confusing Scenes in 3-D surface Matching and Recognition},
author = {Owen Carmichael and Daniel Huber and Martial Hebert},
booktitle = {Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling (3DIM'99)},
keyword = {3D, object recognition, object modeling, registration, terrain mapping},
grantID = {NSF Grant IRI-9711853 and ONR Grant N00014-95-1-0591},
month = {October},
year = {1999},
pages = {358-367},
}
2017-09-13T10:46:41+00:00