  Extracting surfaces from Signed Distance Fields (SDFs) can be accomplished
using traditional algorithms, such as Marching Cubes. However, since they rely
on sign flips across the surface, these algorithms cannot be used directly on
Unsigned Distance Fields (UDFs). In this work, we introduce a deep-learning
approach to taking a UDF and turning it locally into an SDF, so that it can be
effectively triangulated using existing algorithms. We show that it achieves
better accuracy in surface detection than existing methods. Furthermore it
generalizes well to unseen shapes and datasets, while being parallelizable. We
also demonstrate the flexibily of the method by using it in conjunction with
DualMeshUDF, a state of the art dual meshing method that can operate on UDFs,
improving its results and removing the need to tune its parameters.
