VoroLight: Learning Quality Volumetric Voronoi Meshes from General Inputs

Jiayin Lu; Ying Jiang; Yin Yang; Chenfanfu Jiang

Paper

VoroLight: Learning Quality Volumetric Voronoi Meshes from General Inputs

Abstract

We present VoroLight, a differentiable framework for 3D shape reconstruction based on Voronoi meshing. Our approach generates smooth, watertight surfaces and topologically consistent volumetric meshes directly from diverse inputs, including images, implicit shape level-set fields, point clouds and meshes. VoroLight operates in three stages: it first initializes a surface using a differentiable Voronoi formulation, then refines surface quality through a polygon-face sphere training stage, and finally reuses the differentiable Voronoi formulation for volumetric optimization with additional interior generator points. Project page: https://jiayinlu19960224.github.io/vorolight/