Gaussian-Product Subdivision Surfaces

Reinhold Preiner, Tamy Boubekeur, Michael Wimmer

Publikation: KonferenzbeitragPaperForschungBegutachtung

Abstract

Probabilistic distribution models like Gaussian mixtures have shown great potential for improving both the quality and speed of several geometric operators. This is largely due to their ability to model large fuzzy data using only a reduced set of atomic distributions, allowing for large compression rates at minimal information loss. We introduce a new surface model that utilizes these qualities of Gaussian mixtures for the definition and control of a parametric smooth surface. Our approach is based on an enriched mesh data structure, which describes the probability distribution of spatial surface locations around each vertex via a Gaussian covariance matrix. By incorporating this additional covariance information, we show how to define a smooth surface via a non-linear probabilistic subdivision operator based on products of Gaussians, which is able to capture rich details at fixed control mesh resolution. This entails new applications in surface reconstruction, modeling, and geometric compression.
Originalspracheenglisch
Seitenumfang11
PublikationsstatusVeröffentlicht - Jul 2019
VeranstaltungSIGGRAPH 2019 - Los Angeles, USA / Vereinigte Staaten
Dauer: 28 Jul 20191 Aug 2019

Konferenz

KonferenzSIGGRAPH 2019
LandUSA / Vereinigte Staaten
Zeitraum28/07/191/08/19

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Surface reconstruction
Covariance matrix
Probability distributions
Data structures

Schlagwörter

    Dies zitieren

    Preiner, R., Boubekeur, T., & Wimmer, M. (2019). Gaussian-Product Subdivision Surfaces. Beitrag in SIGGRAPH 2019, USA / Vereinigte Staaten.

    Gaussian-Product Subdivision Surfaces. / Preiner, Reinhold; Boubekeur, Tamy; Wimmer, Michael.

    2019. Beitrag in SIGGRAPH 2019, USA / Vereinigte Staaten.

    Publikation: KonferenzbeitragPaperForschungBegutachtung

    Preiner, R, Boubekeur, T & Wimmer, M 2019, 'Gaussian-Product Subdivision Surfaces' Beitrag in, USA / Vereinigte Staaten, 28/07/19 - 1/08/19, .
    Preiner R, Boubekeur T, Wimmer M. Gaussian-Product Subdivision Surfaces. 2019. Beitrag in SIGGRAPH 2019, USA / Vereinigte Staaten.
    Preiner, Reinhold ; Boubekeur, Tamy ; Wimmer, Michael. / Gaussian-Product Subdivision Surfaces. Beitrag in SIGGRAPH 2019, USA / Vereinigte Staaten.11 S.
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    AU - Boubekeur, Tamy

    AU - Wimmer, Michael

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    AB - Probabilistic distribution models like Gaussian mixtures have shown great potential for improving both the quality and speed of several geometric operators. This is largely due to their ability to model large fuzzy data using only a reduced set of atomic distributions, allowing for large compression rates at minimal information loss. We introduce a new surface model that utilizes these qualities of Gaussian mixtures for the definition and control of a parametric smooth surface. Our approach is based on an enriched mesh data structure, which describes the probability distribution of spatial surface locations around each vertex via a Gaussian covariance matrix. By incorporating this additional covariance information, we show how to define a smooth surface via a non-linear probabilistic subdivision operator based on products of Gaussians, which is able to capture rich details at fixed control mesh resolution. This entails new applications in surface reconstruction, modeling, and geometric compression.

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    KW - GMM

    KW - Subdivision Surface

    KW - Gaussian Product

    KW - Covariance Mesh

    KW - Nonlinear Subdivision

    M3 - Paper

    ER -