Medial Axis Aware Learning of Signed Distance Functions
Researchers have developed a new variational method for accurately computing signed distance functions (SDFs) from point clouds. This approach explicitly incorporates the medial axis, which is the jump set of the SDF's gradient, by using a higher-order variational formulation. The method employs a phase field approximation to implicitly describe the medial axis and uses neural networks to approximate both the SDF and the phase field, demonstrating improved accuracy compared to existing methods. AI