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 |
| Externally published | Yes |
| 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/Territory | Australia |
| City | Brisbane |
| Period | 5/21/18 → 5/25/18 |
Bibliographical note
Funding Information:This material is based upon work supported by the National Science Foundation through grants #CNS-1338042, #IIS-1427014, #CNS-1439728, #CNS-1531330 and #CNS- 1544887
Publisher Copyright:
© 2018 IEEE.