3D point cloud segmentation using topological persistence

William J. Beksi, Nikolaos P Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extract the clusters of each connected component in the dataset. We show that this technique has several advantages over state of the art methods such as the ability to provide a stable segmentation of point cloud data under noisy or poor sampling conditions and its independence of a fixed distance metric.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5046-5051
Number of pages6
Volume2016-June
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period5/16/165/21/16

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Beksi, W. J., & Papanikolopoulos, N. P. (2016). 3D point cloud segmentation using topological persistence. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (Vol. 2016-June, pp. 5046-5051). [7487710] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487710