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 language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3229-3234 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538630815 |
| DOIs | |
| State | Published - Sep 10 2018 |
| Event | 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia Duration: May 21 2018 → May 25 2018 |
Publication series
| Name | Proceedings - IEEE International Conference on Robotics and Automation |
|---|---|
| ISSN (Print) | 1050-4729 |
Conference
| Conference | 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 5/21/18 → 5/25/18 |
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Cite this
Signature of Topologically Persistent Points for 3D Point Cloud Description. / Beksi, William J.; Papanikolopoulos, Nikolaos P.
2018 IEEE International Conference on Robotics and Automation, ICRA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3229-3234 8460605 (Proceedings - IEEE International Conference on Robotics and Automation).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Signature of Topologically Persistent Points for 3D Point Cloud Description
AU - Beksi, William J.
AU - Papanikolopoulos, Nikolaos P
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85063128142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063128142&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2018.8460605
DO - 10.1109/ICRA.2018.8460605
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3229
EP - 3234
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -