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/

翻译：我们提出了VoroLight，一个基于Voronoi网格化的可微分三维形状重建框架。该方法能够直接从多样化输入（包括图像、隐式形状水平集场、点云和网格）生成平滑、水密且拓扑一致的体积网格。VoroLight分三个阶段运行：首先通过可微分Voronoi公式初始化表面，随后通过多边形面球训练阶段优化表面质量，最后复用可微分Voronoi公式，结合内部生成点进行体积优化。项目页面：https://jiayinlu19960224.github.io/vorolight/