Gomes, Pedro;
Toni, Laura;
Rossi, Silvia;
(2022)
Explaining Hierarchical Features in Dynamic Point Cloud Processing.
In:
Proceedings of the Picture Coding Symposium (PCS) 2022.
(pp. pp. 67-71).
Institute of Electrical and Electronics Engineers (IEEE): San Jose, CA, USA.
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Abstract
This paper aims at bringing some light and understanding to the field of deep learning for dynamic point cloud processing. Specifically, we focus on the hierarchical features learning aspect, with the ultimate goal of understanding which features are learned at the different stages of the process and what their meaning is. Last, we bring clarity on how hierarchical components of the network affect the learned features and their importance for a successful learning model. This study is conducted for point cloud prediction tasks, useful for predicting coding applications.
| Type: | Proceedings paper |
|---|---|
| Title: | Explaining Hierarchical Features in Dynamic Point Cloud Processing |
| Event: | Picture Coding Symposium (PCS) 2022 |
| Location: | San Jose, CA, USA |
| Dates: | 7th-9th December 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/PCS56426.2022.10018000 |
| Publisher version: | https://doi.org/10.1109/PCS56426.2022.10018000 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | dynamic point clouds, hierarchical learning, explanability, prediction |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156555 |
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