AI RESEARCH
Medial Axis Aware Learning of Signed Distance Functions
arXiv CS.LG
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ArXi:2604.16512v1 Announce Type: cross We propose a novel variational method to compute a highly accurate global signed distance function (SDF) to a given point cloud. To this end, the jump set of the gradient of the SDF, which coincides with the medial axis of the surface, is explicitly taken into account through a higher-order variational formulation that enforces linear growth along the gradient direction away from this discontinuity set. The eikonal equation and the zero-level set of the SDF are enforced as constraints.