Learning Approximate Isometric Embeddings for Efficient Template Placement

The sphere covering problem has broad applications in science however, exact solutions are computationally infeasible on curved, high-dimensional manifolds because distances must be evaluated using a position-dependent metric. We present $\texttt{isobank}$, a normalizing flow that learns an approxim