IPFNet: Implicit Primitive Fitting for Robust Point Cloud Segmentation

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dc.contributor.authorZhou, Shengdien_US
dc.contributor.authorZan, Xiaoqiangen_US
dc.contributor.authorZhou, Binen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.date.accessioned2025-10-07T05:01:31Z
dc.date.available2025-10-07T05:01:31Z
dc.date.issued2025
dc.description.abstractThe segmentation and fitting of geometric primitives from point clouds is a widely adopted approach for modelling the underlying geometric structure of objects in reverse engineering and numerous graphics applications. Existing methods either overlook the role of geometric information in assisting segmentation or incorporate reconstruction losses without leveraging modern neural implicit field representations, leading to limited robustness against noise and weak expressive power in reconstruction. We propose a point cloud segmentation and fitting framework based on neural implicit representations, fully leveraging neural implicit fields' expressive power and robustness. The key idea is the unification of geometric representation within a neural implicit field framework, enabling seamless integration of geometric loss for improved performance. In contrast to previous approaches that focus solely on clustering in the feature embedding space, our method enhances instance segmentation through semanticaware point embeddings and simultaneously improves semantic predictions via instance-level feature fusion. Furthermore, we incorporate 3D-specific cues such as spatial dimensions and geometric connectivity, which are uniquely informative in the 3D domain. Extensive experiments and comparisons against previous methods demonstrate our robustness and superiority.en_US
dc.description.number7
dc.description.sectionheadersCreating and Processing Point Clouds
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70231
dc.identifier.issn1467-8659
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70231
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70231
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Point-based models; Parametric curve and surface models
dc.subjectComputing methodologies → Point
dc.subjectbased models
dc.subjectParametric curve and surface models
dc.titleIPFNet: Implicit Primitive Fitting for Robust Point Cloud Segmentationen_US
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