Spherical Parametric Measurement for Continuous and Balanced Mesh Segmentation

Abstract

Mesh segmentation is an important process for building the discrete mesh structure used on the GPU to accelerate geometry processing applications. In this paper, we introduce a novel mesh segmentation method that creates balanced sub-meshes for high-performance geometry processing. The method ensures topological continuity within sub-meshes (segments) and evenly distributes the number of triangles across all sub-meshes. A new cohesion algorithm computes the chord distances between triangles in the spherical domain and re-groups the triangles into the sub-meshes based on a distance-based measurement condition. A new refinement algorithm between the neighboring sub-meshes is conducted to resolve the non-manifold issue and improve the boundary smoothness. Both algorithms are executed in a parallel fashion. In advancing the state-of-the-art, our approach achieves exactly balanced triangle counts and mitigates the non-manifold issue significantly. The algorithms require the input meshes to have a closed-manifold genus of zero, which is a constraint that is commonly associated with the concept of sphere-based parameterization. We evaluated the effectiveness of our approach in supporting two geometry processing applications. The results show that the performance is enhanced by leveraging the structure of the balanced sub-meshes from our approach.