Algorithms in Geometric Deep learning and 3D AI: Theoretical Survey
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The 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.
Description
CCS Concepts: Computing methodologies → Feature selection; Neural networks; Rule learning; Latent variable models; Mixture models
@inproceedings{10.2312:stag.20251326,
booktitle = {Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference},
editor = {Comino Trinidad, Marc and Mancinelli, Claudio and Maggioli, Filippo and Romanengo, Chiara and Cabiddu, Daniela and Giorgi, Daniela},
title = {{Algorithms in Geometric Deep learning and 3D AI: Theoretical Survey}},
author = {Katturu, Vaibhav and Thind, Parampuneet Kaur},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {2617-4855},
ISBN = {978-3-03868-296-7},
DOI = {10.2312/stag.20251326}
}