Sparse Statistical Shape Modelling

The novel feature of the proposed approach is the application of a symmetric Dirichlet prior on the mixture coefficients to enforce sparsity and search over a continuous space for the optimal number of Gaussian components, to address the common issue of over or under-fitting.

Additionally, we provide code to reconstruct surfaces from the unstructured point sets generated, following SSM training.