Signature of Topologically Persistent Points for 3D Point Cloud Description

William J. Beksi, Nikolaos P Papanikolopoulos

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

Abstract

We present the Signature of Topologically Persistent Points (STPP), a global descriptor that encodes topological invariants of 3D point cloud data. These topological invariants include the zeroth and first homology groups and are computed using persistent homology, a method for finding the features of a topological space at different spatial resolutions. STPP is a competitive 3D point cloud descriptor when compared to the state of art and is resilient to noisy sensor data. We demonstrate experimentally on a publicly available RGB-D dataset that STPP can be used as a distinctive signature, thus allowing for 3D point cloud processing tasks such as object detection and classification.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3229-3234
Number of pages6
ISBN (Electronic)9781538630815
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
CountryAustralia
CityBrisbane
Period5/21/185/25/18

    Fingerprint

Cite this

Beksi, W. J., & Papanikolopoulos, N. P. (2018). Signature of Topologically Persistent Points for 3D Point Cloud Description. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 (pp. 3229-3234). [8460605] (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2018.8460605