A Memory Efficient Encoding for Ray Tracing Large Unstructured Data
Ingo Wald, Nate Morrical, Stefan Zellmann
摘要
In theory, efficient and high-quality rendering of unstructured data should greatly benefit from modern GPUs, but in practice, GPUs are often limited by the large amount of memory that large meshes require for element representation and for sample reconstruction acceleration structures. We describe a memory-optimized encoding for large unstructured meshes that efficiently encodes both the unstructured mesh and corresponding sample reconstruction acceleration structure, while still allowing for fast random-access sampling as required for rendering. We demonstrate that for large data our encoding allows for rendering even the 2.9 billion element Mars Lander on a single off-the-shelf GPU-and the largest 6.3 billion version on a pair of such GPUs.
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- Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric GridsNate Morrical, Alper Sahistan, Ugur Güdükbay, Ingo Wald 等IEEE VIS 2022 · 被引用 15 次
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- GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data AnalysisGuoxi Liu, Thomas Randall, Rong Ge, Federico IuricichIEEE VIS 2025 · 被引用 1 次
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