TL;DR of the paper, generated automatically.

New paper on learning signed distance functions that handles the medial axis directly.

• Input: unoriented point clouds, using neural nets for the SDF and a phase field.
• Medial axis: a phase field implicitly marks it, enforced as a jump set.
• Results: the paper says it's more accurate near and globally than other approaches.