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#### AlSub: Fully Parallel and Modular Subdivision

##### MPS-Authors
/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45789

Zayer,  Rhaleb
Computer Graphics, MPI for Informatics, Max Planck Society;

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##### Fulltext (public)

arXiv:1809.06047.pdf
(Preprint), 5MB

##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Mlakar, D., Winter, M., Seidel, H.-P., Steinberger, M., & Zayer, R. (2018). AlSub: Fully Parallel and Modular Subdivision. Retrieved from http://arxiv.org/abs/1809.06047.

Cite as: http://hdl.handle.net/21.11116/0000-0002-E5E2-C
##### Abstract
In recent years, mesh subdivision---the process of forging smooth free-form surfaces from coarse polygonal meshes---has become an indispensable production instrument. Although subdivision performance is crucial during simulation, animation and rendering, state-of-the-art approaches still rely on serial implementations for complex parts of the subdivision process. Therefore, they often fail to harness the power of modern parallel devices, like the graphics processing unit (GPU), for large parts of the algorithm and must resort to time-consuming serial preprocessing. In this paper, we show that a complete parallelization of the subdivision process for modern architectures is possible. Building on sparse matrix linear algebra, we show how to structure the complete subdivision process into a sequence of algebra operations. By restructuring and grouping these operations, we adapt the process for different use cases, such as regular subdivision of dynamic meshes, uniform subdivision for immutable topology, and feature-adaptive subdivision for efficient rendering of animated models. As the same machinery is used for all use cases, identical subdivision results are achieved in all parts of the production pipeline. As a second contribution, we show how these linear algebra formulations can effectively be translated into efficient GPU kernels. Applying our strategies to $\sqrt{3}$, Loop and Catmull-Clark subdivision shows significant speedups of our approach compared to state-of-the-art solutions, while we completely avoid serial preprocessing.