Neural Approximation of Generalized Voronoi Diagrams

• URL: http://arxiv.org/abs/2603.26964v1
• Date: Fri, 27 Mar 2026 20:13:13 GMT
• Title: Neural Approximation of Generalized Voronoi Diagrams
• Abstract summary: VoroFields is a framework for approximating Voronoi diagrams of finite geometric site sets in low-dimensional domains.<n>A hierarchical decomposition reduces the complexity by refining only near envelope transition strata.
• Score: 0.12744523252873352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce VoroFields, a hierarchical neural-field framework for approximating generalized Voronoi diagrams of finite geometric site sets in low-dimensional domains under arbitrary evaluable point-to-site distances. Instead of constructing the diagram combinatorially, VoroFields learns a continuous, differentiable surrogate whose maximizer structure induces the partition implicitly. The Voronoi cells correspond to maximizer regions of the field, with boundaries defined by equal responses between competing sites. A hierarchical decomposition reduces the combinatorial complexity by refining only near envelope transition strata. Experiments across site families and metrics demonstrate accurate recovery of cells and boundary geometry without shape-specific constructions.

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