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Predicting point cloud over time
1 February 2021
Proposing an end-to-end learning network to predict future frames in a point cloud sequence
Funder N/A
Amount N/A
Project website LASP
Research topics volumetric video | point cloud prediction | mixed reality
Description
Volumetric video (VV), which allows 3D representation of real-world scenes and objects to be visualized from any viewpoint or viewing direction, is an emergent digital media for mixed reality systems. As such, VV enables novel formats of creative storytelling, which are immersive, interactive, and include real-world content. VV also enables novel forms of immersive real-time communication also known as telepresence.
However, the higher level of immersiveness and realism offered to VV comes with many new open challenges, such as processing of point cloud content (one possible representation of VV). This has motivated intense research toward PC processing, with strong focus on static PCs, leaving the dynamic PC processing usually overlooked. In this project, we focus on dynamic PC processing and specifically on the prediction of point cloud sequence.
Outputs
P. Gomes, S. Rossi, L. Toni, "Spatio-Temporal Graph-RNN for Point Cloud Prediction", IEEE ICIP 2021 https://arxiv.org/abs/2102.07482