Subdivision-Specialized Linear Algebra Kernels for Static and Dynamic Mesh Connectivity on the GPU

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dc.contributor.authorMlakar, Danielen_US
dc.contributor.authorWinter, Martinen_US
dc.contributor.authorStadlbauer, Pascalen_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.authorSteinberger, Markusen_US
dc.contributor.authorZayer, Rhaleben_US
dc.contributor.editorPanozzo, Daniele and Assarsson, Ulfen_US
dc.date.accessioned2020-05-24T12:52:18Z
dc.date.available2020-05-24T12:52:18Z
dc.date.issued2020
dc.description.abstractSubdivision surfaces have become an invaluable asset in production environments. While progress over the last years has allowed the use of graphics hardware to meet performance demands during animation and rendering, high-performance is limited to immutable mesh connectivity scenarios. Motivated by recent progress in mesh data structures, we show how the complete Catmull-Clark subdivision scheme can be abstracted in the language of linear algebra. While this high-level formulation allows for a fully parallel implementation with significant performance gains, the underlying algebraic operations require further specialization for modern parallel hardware. Integrating domain knowledge about the mesh matrix data structure, we replace costly general linear algebra operations like matrix-matrix multiplication by specialized kernels. By further considering innate properties of Catmull-Clark subdivision, like the quad-only structure after refinement, we achieve an additional order of magnitude in performance and significantly reduce memory footprints. Our approach can be adapted seamlessly for different use cases, such as regular subdivision of dynamic meshes, fast evaluation for immutable topology and feature-adaptive subdivision for efficient rendering of animated models. In this way, patchwork solutions are avoided in favor of a streamlined solution with consistent performance gains throughout the production pipeline. The versatility of the sparse matrix linear algebra abstraction underlying our work is further demonstrated by extension to other schemes such as √3 and Loop subdivision.en_US
dc.description.number2
dc.description.sectionheadersMeshes and Subdivision
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.13934
dc.identifier.issn1467-8659
dc.identifier.pages335-349
dc.identifier.urihttps://doi.org/10.1111/cgf.13934
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13934
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputing methodologies → Shape modeling
dc.subjectMassively parallel algorithms
dc.titleSubdivision-Specialized Linear Algebra Kernels for Static and Dynamic Mesh Connectivity on the GPUen_US
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