SurfaceVoronoi: Efficiently Computing Voronoi Diagrams over Mesh Surfaces with Arbitrary Distance Solvers

TL;DR

This paper introduces an efficient method for computing Voronoi diagrams on mesh surfaces using arbitrary geodesic distance solvers, supporting various diagram types and handling complex surface models.

Contribution

It presents a novel algorithm that accurately computes surface Voronoi diagrams with arbitrary distances, including Euclidean, and extends to various diagram variants.

Findings

Supports arbitrary geodesic distance solvers.

Runs faster than traditional RVD algorithms.

Validates approximation accuracy through extensive experiments.

Abstract

In this paper, we propose to compute Voronoi diagrams over mesh surfaces driven by an arbitrary geodesic distance solver, assuming that the input is a triangle mesh as well as a collection of sites $P=\{p_i\}_{i=1}^m$ on the surface. We propose two key techniques to solve this problem. First, as the partition is determined by minimizing the $m$ distance fields, each of which rooted at a source site, we suggest keeping one or more distance triples, for each triangle, that may help determine the Voronoi bisectors when one uses a mark-and-sweep geodesic algorithm to predict the multi-source distance field. Second, rather than keep the distance itself at a mesh vertex, we use the squared distance to characterize the linear change of distance field restricted in a triangle, which is proved to induce an exact VD when the base surface reduces to a planar triangle mesh. Specially, our algorithm also supports the Euclidean distance, which can handle thin-sheet models (e.g. leaf) and runs faster than the traditional restricted Voronoi diagram~(RVD) algorithm. It is very extensible to deal with various variants of surface-based Voronoi diagrams including (1)surface-based power diagram, (2)constrained Voronoi diagram with curve-type breaklines, and (3)curve-type generators. We conduct extensive experimental results to validate the ability to approximate the exact VD in different distance-driven scenarios.