VoroMesh: Learning Watertight
Surface Meshes with Voronoi Diagrams
Nissim Maruani, Roman Klokov, Maks Ovsjanikov, Pierre Alliez, Mathieu
Desbrun
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D
surfaces remains a challenge. In particular, while polygon meshes are arguably the most common
surface representation used in geometry processing, their irregular and combinatorial structure
often make them unsuitable for learning-based applications. In this work, we present VoroMesh, a
novel and differentiable Voronoi-based representation of water- tight 3D shape surfaces. From a set
of 3D points (called generators) and their associated occupancy, we define our boundary
representation through the Voronoi diagram of the generators as the subset of Voronoi faces whose
two associated (equidistant) generators are of opposite occupancy: the resulting polygon mesh forms
a watertight approximation of the target shape’s boundary. To learn the position of the generators,
we propose a novel loss function, dubbed VoroLoss, that minimizes the distance from groundtruth
surface samples to the closest faces of the Voronoi diagram which does not require an explicit
construction of the entire Voronoi diagram. A direct optimization of the Voroloss to obtain
generators on the Thingi32 dataset demonstrates the geometric efficiency of our representation
compared to axiomatic meshing algorithms and recent learning-based mesh representations. We further
use VoroMesh in a learning-based mesh prediction task from input SDF grids on the ABC dataset, and
show comparable performance to state-of-the-art methods while guaranteeing closed output surfaces
free of self-intersections.
Proc. International Conference on Computer Vision (ICCV), 2023