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PoNQ: a Neural QEM-based Mesh Representation
March 20, 2024, 4:46 a.m. | Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r.t. the underlying shape, which we denote PoNQ. A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. …
abstract applications arxiv cs.cv error geometry mesh meshes metrics nature novel polygon processing representation sample set standard through type work
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