Representation and analysis of immersive dynamic data based on vector quantization and reinforcement learning

Période : 2022-2025

The thesis addresses the issue of representation and analysis of immersive dynamic data by using vector quantization and reinforcement learning. In the LS2N/IPI team we have already an experience in Geometric Point Cloud Compression domain by using Tree-Structured Point-Lattice Vector Quantization. We plan to extend the method for the analysis and representation of the dynamic 3D contents.

Vincent Ricordel