Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space

Viktor Seib, Norman Link, Dietrich Paulus

Abstract

Recently, different adaptations of Implicit Shape Models (ISM) for 3D shape classification have been presented. In this paper we propose a new method with a continuous voting space and keypoint extraction by uniform sampling. We evaluate different sets of typical parameters involved in the ISM algorithm and compare the proposed algorithm on a large public dataset with state of the art approaches.

References

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Paper Citation


in Harvard Style

Seib V., Link N. and Paulus D. (2015). Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 33-43. DOI: 10.5220/0005290700330043


in Bibtex Style

@conference{visapp15,
author={Viktor Seib and Norman Link and Dietrich Paulus},
title={Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={33-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005290700330043},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space
SN - 978-989-758-090-1
AU - Seib V.
AU - Link N.
AU - Paulus D.
PY - 2015
SP - 33
EP - 43
DO - 10.5220/0005290700330043