Persistence-based Interest Point Detection for 3D Deformable Surface

Xupeng Wang, Ferdous Sohel, Mohammed Bennamoun, Yulan Guo, Hang Lei

Abstract

Several approaches for interest point detection on rigid shapes have been proposed, but few are available for non-rigid shapes. It is a very challenging task due to the presence of the large degrees of local deformations. This paper presents a novel method called persistence-based heat kernel signature (pHKS). It consists of two steps: scalar field construction and interest point detection. We propose to use the heat kernel signature function at a moderately small time scale to construct the scalar field. It has the advantage of being stable under various transformations. Based on the predefined scalar field, a 0-dimensional persistence diagram is computed, and the local geometric and global structural information of the shape are captured at the same time. Points with local maxima and high persistence are selected as interest points. We perform a comprehensive evaluation on two popular datasets (i.e., PHOTOMESH and Interest Points Dataset) to show the effectiveness of our method. Compared with existing techniques, our interest point detector achieves a superior performance in terms of repeatability and distinctiveness.

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Paper Citation


in Harvard Style

Wang X., Sohel F., Bennamoun M., Guo Y. and Lei H. (2017). Persistence-based Interest Point Detection for 3D Deformable Surface . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017) ISBN 978-989-758-224-0, pages 58-69. DOI: 10.5220/0006093800580069


in Bibtex Style

@conference{grapp17,
author={Xupeng Wang and Ferdous Sohel and Mohammed Bennamoun and Yulan Guo and Hang Lei},
title={Persistence-based Interest Point Detection for 3D Deformable Surface},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)},
year={2017},
pages={58-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006093800580069},
isbn={978-989-758-224-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)
TI - Persistence-based Interest Point Detection for 3D Deformable Surface
SN - 978-989-758-224-0
AU - Wang X.
AU - Sohel F.
AU - Bennamoun M.
AU - Guo Y.
AU - Lei H.
PY - 2017
SP - 58
EP - 69
DO - 10.5220/0006093800580069