UV Parametrization via Topological Disk Segmentation of Surfaces

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dc.contributor.authorMaggioli, Filippoen_US
dc.contributor.authorMelzi, Simoneen_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:04Z
dc.date.available2025-11-21T07:28:04Z
dc.date.issued2025
dc.description.abstractWe present a reliable method for UV mapping that leverages a Voronoi-based decomposition of a triangulated surface mesh. Given a sparse set of sample points on the input shape, we construct the corresponding Voronoi partition and iteratively refine it to ensure that all regions are topologically equivalent to disks. The refinement proceeds in two stages: first, Voronoi cells are subdivided until disk-like topology is guaranteed; then, adjacent regions sharing substantial boundary portions are merged to reduce both their total number and perimeter-to-area ratio, while preserving disk equivalence. This topological guarantee enables straightforward and reliable UV parameterization. Our method exhibits an extremely low failure rate, making it suitable for practical use. In quantitative experiments on standard UV mapping benchmarks, we achieve performance comparable to state-of-the-art techniques. Furthermore, we analyze robustness and efficiency across different sampling densities, providing insights into the computational cost of each step of the pipeline.en_US
dc.description.sectionheadersGeometry Processing
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251323
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251323
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251323
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Machine learning; Shape analysis; Theory of computation → Computational geometry
dc.subjectComputing methodologies → Machine learning
dc.subjectShape analysis
dc.subjectTheory of computation → Computational geometry
dc.titleUV Parametrization via Topological Disk Segmentation of Surfacesen_US
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