2 research outputs found
Spatio-temporal Graph-RNN for Point Cloud Prediction
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