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

Viktor Seib, Norman Link, Dietrich Paulus

2015

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.

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