Efficient coding of repeated mesh connected components via connectivity skip and prediction refinement

Repeated connected components exist in meshes, especially in digitally created contents (DCC). These connected components share the same connectivity and are typically related by a rigid transformation (rotation and/or translation). Encoding the same connectivity multiple times is redundant. In addition, the existence of rigid transform among repeated connected components can be exploited to better predict vertex positions. In this paper, we first propose to concurrently encode mesh connected components which share the same connectivity. The input mesh is examined and connected components with the same connectivity are grouped together. Then, for each group, the shared connectivity is coded followed by coding positions of all the vertices within the group. Secondly, a prediction refinement scheme is proposed in which the residue of an already encoded vertex improves the prediction of its corresponding vertex in another connected component within the same group.