3D region segmentation using topological persistence

William J. Beksi, Nikolaos Papanikolopoulos

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

11 Scopus citations


A 'region' is an important concept in interpreting 3D point cloud data since regions may correspond to objects in a scene. To correctly interpret 3D point cloud data, we need to partition the dataset into regions that correspond to objects or parts of an object. In this paper, we present a region growing approach that combines global (topological) and local (color, surface normal) information to segment 3D point cloud data. Using ideas from persistent homology theory, our algorithm grows a simplicial complex representation of the point cloud dataset. At each step in the growth process we compute the zeroth homology group of the complex, which corresponds to the number of connected components, and use color and surface normal statistics to build regions. Lastly, we extract out the segmented regions of the dataset. We show that this method provides a stable segmentation of point cloud data in the presence of noise and poorly sampled data, thus providing advantages over contemporary region-based segmentation techniques.

Original languageEnglish (US)
Title of host publicationIROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509037629
StatePublished - Nov 28 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
Duration: Oct 9 2016Oct 14 2016

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


Other2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #IIS-1017344, #IIP-1332133, #IIS-1427014, #IIP-1432957, #OISE-1551059, #CNS-1514626, #CNS-1531330, and #CNS-1544887.

Publisher Copyright:
© 2016 IEEE.

Copyright 2017 Elsevier B.V., All rights reserved.


Dive into the research topics of '3D region segmentation using topological persistence'. Together they form a unique fingerprint.

Cite this