Global triangular mesh regularization using conditional Markov random fields

Vincent Vidal 1 Christian Wolf 1 Florent Dupont 1 Guillaume Lavoué 1
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We present a global mesh optimization framework based on a Conditional Markov Random Fied (CMRF or CRF) model suited for 3D triangular meshes of arbitrary topology. The remeshing task is formulated as a Bayesian estimation problem including data attached terms measuring the fidelity to the original mesh as well as a prior favoring high quality triangles. Since the best solution for vertex relocation is strongly related to the mesh connectivity, our approach iteratively modifies the mesh structure (connectivity plus vertex addition/removal) as well as the vertex positions, which are moved according to a well-defined energy function resulting from the CMRF model. Good solutions for the proposed model are obtained by a discrete graph cut algorithm examining global combinations of local candidates. Results on various 3D meshes compare favorably to recent state-of-the-art algorithms regarding the trade-off between triangle shape improvement and surface fidelity. Applications of this work mainly consist in regularizing meshes for numerical simulations and for improving mesh rendering.
Document type :
Poster communications
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https://hal.archives-ouvertes.fr/hal-01364624
Contributor : Vincent Vidal <>
Submitted on : Monday, September 12, 2016 - 5:02:28 PM
Last modification on : Tuesday, February 26, 2019 - 2:43:36 PM

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  • HAL Id : hal-01364624, version 1

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Vincent Vidal, Christian Wolf, Florent Dupont, Guillaume Lavoué. Global triangular mesh regularization using conditional Markov random fields. Symposium on Geometry Processing, Jul 2009, Berlin, Germany. ⟨EUROGRAPHICS⟩, 2009. ⟨hal-01364624⟩

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