A Memory Efficient Encoding for Ray Tracing Large Unstructured Data
Ingo Wald, Nate Morrical, Stefan Zellmann
Abstract
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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Install the CLIlune papers get fa82ea75-9ab8-4b10-a876-f71b28772eeeCited by top-tier papers3
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