Spatio-temporal Graph-RNN for Point Cloud Prediction

• URL: http://arxiv.org/abs/2102.07482v2
• Date: Wed, 17 Feb 2021 11:43:31 GMT
• Title: Spatio-temporal Graph-RNN for Point Cloud Prediction
• Authors: Pedro Gomes, Silvia Rossi, Laura Toni
• Abstract summary: We propose an end-end learning to predict future point cloud frames. An initial layer learns novelty point clouds as geometric,temporal network features. Multiple Graph-RNN features cells each point learns points dynamics.
• Score: 10.949548440282392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an end-to-end learning network to predict future point cloud frames. 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) 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.

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