Geometry-Driven Local Neighbourhood Based Predictors for Dynamic Mesh Compression

The task of dynamic mesh compression seeks to find a compact representation of a surface animation, while the artifacts introduced by the representation are as small as possible. In this paper, we present two geometric predictors, which are suitable for PCA-based compression schemes. The predictors exploit the knowledge about the geometrical meaning of the data, which allows a more accurate prediction, and thus a more compact representation. We also provide rate/distortion curves showing that our approach outperforms the current PCA-based compression methods by more than 20%.