Algorithms in Geometric Deep learning and 3D AI: Theoretical Survey

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dc.contributor.authorKatturu, Vaibhaven_US
dc.contributor.authorThind, Parampuneet Kauren_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.accessioned2025-11-21T07:28:10Z
dc.date.available2025-11-21T07:28:10Z
dc.date.issued2025
dc.description.abstractThe study of shapes and geometric representations has long been central to Artificial Intelligence (AI). Early neural networks were limited to Euclidean domains such as images and sequences. The first extensions to non-Euclidean structures appeared in the 1990s and 2000s with recursive neural networks for hierarchical data and random walk-based graph methods. A major step forward came with spectral graph convolutional networks, which introduced convolution in the Fourier domain but faced scalability issues. Spatial methods later enabled more practical graph neural networks (GNNs). In parallel, 3D vision advanced with point cloud models such as PointNet and DGCNN, and mesh-based approaches like Geodesic CNN and MeshCNN, driving progress in classification, segmentation, and reconstruction. As algorithms in geometric deep learning and 3D AI expand, the field has grown both powerful and complex. This paper categorizes major algorithmic families, surveys key datasets across Euclidean and non-Euclidean domains, and highlights emerging advances and open research challenges.en_US
dc.description.sectionheadersLearning-based Algorithms
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251326
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251326
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251326
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 → Feature selection; Neural networks; Rule learning; Latent variable models; Mixture models
dc.subjectComputing methodologies → Feature selection
dc.subjectNeural networks
dc.subjectRule learning
dc.subjectLatent variable models
dc.subjectMixture models
dc.titleAlgorithms in Geometric Deep learning and 3D AI: Theoretical Surveyen_US
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Italian Chapter Conference 2025 - Smart Tools and Apps in Graphics