Name
Abstract | ||
|---|---|---|
In this paper, we propose an end-to-end learning network aim at predicting future PC frames, based on point-based RNN network. As main novelty, an initial layer learns topological information of point clouds as geometric features and then uses the learned features to form representative spatio-temporal neighborhoods. This module is followed by multiple Graph-RNN cells. Each cell learns points dynamics (i.e., RNN states) processing each point jointly with the spatio-temporal neighboring points. We tested the network performance with a MINST dataset of moving digits, a synthetic human bodies motions and JPEG dynamic bodies datasets. Simulation results demonstrated that our method outperforms baseline ones that neglect geometry |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICIP42928.2021.9506084 | ICIP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Gomes | 1 | 0 | 1.35 |
| Silvia Rossi | 2 | 105 | 19.81 |
| Laura Toni | 3 | 15 | 6.70 |