Výsledky vyhledávání - "Maruani, Nissim"

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PoNQ: a Neural QEM-based Mesh Representation

Autor: Maruani, Nissim, Ovsjanikov, Maks, Alliez, Pierre, Desbrun, Mathieu

Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work, we introduce a novel learnable mesh representation t

Externí odkaz: http://arxiv.org/abs/2403.12870

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VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams

Autor: Maruani, Nissim, Klokov, Roman, Ovsjanikov, Maks, Alliez, Pierre, Desbrun, Mathieu

In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge. In particular, while polygon meshes are arguably the most common surface representation used in geometry processing, the

Externí odkaz: http://arxiv.org/abs/2308.14616

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Feature-Preserving Offset Mesh Generation from Topology-Adapted Octrees

Autor: Zint, Daniel, Maruani, Nissim, Rouxel-Labbé, Mael, Alliez, Pierre

Publikováno v: Computer Graphics Forum
Computer Graphics Forum, 2023, Proceedings of Eurographics Symposium on Geometry Processing 2023, pp.12

International audience; We introduce a reliable method to generate offset meshes from input triangle meshes or triangle soups. Our method proceeds in two steps. The first step performs a Dual Contouring method on the offset surface, operating on an a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______165::f87b7754b755fc7cc8aec044bc542862
https://inria.hal.science/hal-04135266

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