3D region segmentation using topological persistence

William J. Beksi, Nikolaos P Papanikolopoulos

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

3 Citations (Scopus)

Abstract

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.
Pages1079-1084
Number of pages6
ISBN (Electronic)9781509037629
DOIs
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
Volume2016-November
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
CountryKorea, Republic of
CityDaejeon
Period10/9/1610/14/16

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Beksi, W. J., & Papanikolopoulos, N. P. (2016). 3D region segmentation using topological persistence. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1079-1084). [7759183] (IEEE International Conference on Intelligent Robots and Systems; Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2016.7759183

3D region segmentation using topological persistence. / Beksi, William J.; Papanikolopoulos, Nikolaos P.

IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1079-1084 7759183 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2016-November).

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

Beksi, WJ & Papanikolopoulos, NP 2016, 3D region segmentation using topological persistence. in IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems., 7759183, IEEE International Conference on Intelligent Robots and Systems, vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 1079-1084, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, Korea, Republic of, 10/9/16. https://doi.org/10.1109/IROS.2016.7759183
Beksi WJ, Papanikolopoulos NP. 3D region segmentation using topological persistence. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1079-1084. 7759183. (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2016.7759183
Beksi, William J. ; Papanikolopoulos, Nikolaos P. / 3D region segmentation using topological persistence. IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1079-1084 (IEEE International Conference on Intelligent Robots and Systems).
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