Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

Abstract

AbstractA common way to render cell‐centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high‐quality interpolator, the memory requirements of the dual mesh data structure are significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU‐friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off‐the‐shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding a gridlet element type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state‐of‐the‐art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory‐efficient.