Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge

Hannes Kisner, Hannes Kisner, Hannes Kisner, Hannes Kisner, Ulrike Thomas, Ulrike Thomas, Ulrike Thomas, Ulrike Thomas

Abstract

For many applications like pose estimation it is important to obtain good segmentation results as a pre-processing step. Spectral clustering is an efficient method to achieve high quality results without a priori knowledge about the scene. Among other methods, it is either the k-means based spectral clustering approach or the bi-spectral clustering approach, which are suitable for 3D point clouds. In this paper, a new method is introduced and the results are compared to these well-known spectral clustering algorithms. When implementing the spectral clustering methods key issues are: how to define similarity, how to build the graph Laplacian and how to choose the number of clusters without any or less a-priori knowledge. The suggested spectral clustering approach is described and evaluated with 3D point clouds. The advantage of this approach is that no a-priori knowledge about the number of clusters is necessary and not even the number of clusters or the number of objects need to be known. With this approach high quality segmentation results are achieved.

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Paper Citation


in Harvard Style

Kisner H. and Thomas U. (2018). Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 315-322. DOI: 10.5220/0006549303150322


in Bibtex Style

@conference{visapp18,
author={Hannes Kisner and Ulrike Thomas},
title={Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006549303150322},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge
SN - 978-989-758-290-5
AU - Kisner H.
AU - Thomas U.
PY - 2018
SP - 315
EP - 322
DO - 10.5220/0006549303150322