Unsupervised 3D Shape Parsing with Primitive Correspondence

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dc.contributor.authorZhao, Tianshuen_US
dc.contributor.authorGuan, Yanranen_US
dc.contributor.authorKaick, Oliver vanen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:04:09Z
dc.date.available2025-10-07T06:04:09Z
dc.date.issued2025
dc.description.abstract3D shape parsing, the process of analyzing and breaking down a 3D shape into components or parts, has become an important task in computer graphics and vision. Approaches for shape parsing include segmentation and approximation methods. Approximation methods often represent shapes with a set of primitives fit to the shapes, such as cuboids, cylinders, or superquadrics. However, existing approximation methods typically rely on a large number of initial primitives and aim to maximize their coverage of the target shape, without accounting for correspondences among the primitives. In this paper, we introduce a novel 3D shape approximation method that integrates reconstruction and correspondence into a single objective, providing approximations that are consistent across the input set of shapes. Our method is unsupervised but also supports supervised learning. Experimental results demonstrate that integrating correspondences into the fitting process not only provides consistent correspondences across a set of input shapes, but also improves approximation quality when using a small number of primitives. Moreover, although correspondences are estimated in an unsupervised manner, our method effectively leverages this knowledge, leading to improved approximations.en_US
dc.description.sectionheadersShape Extraction or Editing
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251287
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251287
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251287
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 → Shape modeling
dc.subjectComputing methodologies → Shape modeling
dc.titleUnsupervised 3D Shape Parsing with Primitive Correspondenceen_US
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