Distance-ranked connectivity compression of triangle meshes
We present a new, single-rate method for compressing the connectivity information of a connected 2-manifold triangle mesh with or without boundary. Traditional compression schemes interleave geometry and connectivity coding, and are thus typically unable to utilize information from vertices (mesh regions) they have not yet processed. With the advent of competitive point cloud compression schemes, it has become feasible to develop separate connectivity encoding schemes that can exploit complete, global vertex position information to improve performance. Our scheme demonstrates the utility of this separation of vertex and connectivity coding. By traversing the mesh edges in a consistent fashion, and using global vertex information, we can predict the position of the vertex that completes the unprocessed triangle attached to a given edge. We then rank the vertices in the neighborhood of this predicted position by their Euclidean distance. The distance rank of the correct closing vertex is stored. Typically, these rank values are small, and the set of rank values thus possesses low entropy and compresses very well. The sequence of rank values is all that is required to represent the mesh connectivity-no special split or merge codes are necessary. Results indicate improvements over traditional valence-based schemes for more regular triangulations. Highly irregular triangulations or those containing a large number of slivers are not well modelled by our current set of predictors and may yield poorer connectivity compression rates than those provided by the best valence-based schemes.
connectivity geometric compression triangle prediction