RXMesh

Abstract

We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons , thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure RXMesh : Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications---mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.

Fast and robust parallel simplification algorithm for triangular mesh

“…The ideal mesh partitioning is to make the number of faces of each sub-mesh equal so that the load of each mesh in the computation is approximated and the efficiency of the algorithm can be improved as much as possible [15]. We use a parallelizable and efficient mesh partitioning algorithm from RXMesh [16]. Given a graph 𝓖 = (𝐕, 𝐄) containing 3D vertices 𝐕 and nonnegative weighted edges 𝐄.…”