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.|
|Number of pages||6|
|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
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2018 IEEE International Conference on Robotics and Automation, ICRA 2018|
|Period||5/21/18 → 5/25/18|
Bibliographical noteFunding 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
© 2018 IEEE.