Zellmann, StefanWu, QiMa, Kwan-LiuWald, IngoBujack, RoxanaArchambault, DanielSchreck, Tobias2023-06-102023-06-1020231467-8659https://doi.org/10.1111/cgf.14811https://diglib.eg.org:443/handle/10.1111/cgf14811A 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.Attribution 4.0 International License10.1111/cgf.1481151-6212 pages