2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | SS-MRII.7 | ||
| Paper Title | SPATIO-TEMPORAL GRAPH-RNN FOR POINT CLOUD PREDICTION | ||
| Authors | Pedro Gomes, Silvia Rossi, Laura Toni, University College London, United Kingdom | ||
| Session | SS-MRII: Special Session: Models and representations for Immersive Imaging | ||
| Location | Area A | ||
| Session Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
| Presentation Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
| Presentation | Poster | ||
| Topic | Special Sessions: Models and Representations for Immersive Imaging | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | In this paper, we propose an end-to-end learning network to predict future frames in a point cloud sequence. As main novelty, an initial layer learns topological information of point clouds as geometric 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) by processing each point jointly with the spatio-temporal neighbouring 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 demonstrate that our method outperforms baseline ones that neglect geometry features information. | ||