Geometric aware local optimization for robust primitive fitting

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dc.contributor.authorFerraris, Andreaen_US
dc.contributor.authorLeveni, Filippoen_US
dc.contributor.authorBaieri, Danieleen_US
dc.contributor.authorMaggioli, Filippoen_US
dc.contributor.authorMelzi, Simoneen_US
dc.contributor.authorMagri, Lucaen_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.accessioned2025-11-21T07:27:59Z
dc.date.available2025-11-21T07:27:59Z
dc.date.issued2025
dc.description.abstractThe decomposition of 3D point clouds into meaningful geometric primitives is a longstanding challenge in Computer Vision and Computer Graphics. While recent advances in data-driven methods and neural representations have achieved significant progress in 3D reconstruction and abstraction, traditional primitive-based representations remain invaluable for tasks requiring interpretability, compactness, and robustness. This work introduces a novel framework for primitive decomposition in 2D and 3D point clouds, designed to cope with noise, outliers, and overlapping structures. Building upon traditional RANSACbased approaches, the proposed method integrates geometric priors to enhance its effectiveness in identifying interpretable and meaningful geometric primitives within complex data. Central to our approach is a novel geometric-aware inlier refinement step, which incorporates geometric constraints such as surface completeness and normal consistency. This refinement step is formulated as an optimization problem solved through the GRAPH-CUT algorithm. This optimization process penalizes excessive surface extensions and promotes coherence in normal orientations, ensuring that the refined inlier sets closely match the geometric structures the point cloud represents. Experiments on synthetic and real-world datasets validate the robustness and accuracy of the proposed method, demonstrating its ability to outperform state-of-the-art techniques in terms of both result quality and computational efficiency.en_US
dc.description.sectionheadersGeometry Processing
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251321
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251321
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251321
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 → Computer vision; Shape representations
dc.subjectComputing methodologies → Computer vision
dc.subjectShape representations
dc.titleGeometric aware local optimization for robust primitive fittingen_US
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