Dynamic Mesh Processing on the GPU
Ahmed H. Mahmoud, Serban D. Porumbescu, John D. Owens
Abstract
We present a system for dynamic triangle mesh processing entirely on the GPU. Our system features an efficient data structure that enables rapid updates to mesh connectivity and attributes. By partitioning the mesh into small patches, we process all dynamic updates for each patch within the GPU's fast shared memory. This approach leverages speculative processing for conflict handling, minimizing rollback costs, maximizing parallelism, and reducing locking overhead. Additionally, we introduce a new programming model for dynamic mesh processing. This model provides concise semantics for dynamic updates, abstracting away concerns about conflicting updates during parallel execution. At the core of our model is the cavity operator , a general mesh update operator that facilitates any dynamic operation by removing a set of mesh elements and inserting new ones into the resulting void. We applied our system to various GPU applications, including isotropic remeshing, surface tracking, mesh decimation, and Delaunay edge flips. On large inputs, our system achieves an order-of-magnitude speedup compared to multi-threaded CPU solutions and is more than two orders of magnitude faster than state-of-the-art single-threaded CPU solutions. Furthermore, our data structure outperforms state-of-the-art GPU static data structures in terms of both speed and memory efficiency.
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Install the CLIlune papers fulltext 5bd5b06f-e5f0-4eca-a705-7331403a15abCited by top-tier papers2
- Iskra: A System for Inverse Geometry ProcessingAna Dodik, Ahmed H. Mahmoud, Justin SolomonSIGGRAPH 2026
- Locality-Aware Automatic Differentiation on the GPU for Mesh-Based ComputationsAhmed H. Mahmoud, Rahul Goel, Jonathan Ragan-Kelley, Justin SolomonSIGGRAPH 2026
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