Paolini, GabrieleGuiducci, NiccolòTortorici, ClaudioBerretti, StefanoBanterle, FrancescoCaggianese, GiuseppeCapece, NicolaErra, UgoLupinetti, KatiaManfredi, Gilda2023-11-122023-11-122023978-3-03868-235-62617-4855https://doi.org/10.2312/stag.20231298https://diglib.eg.org:443/handle/10.2312/stag20231298In the context of geometric deep learning, the classification of relief patterns involves recognizing the surface characteristics of a 3D object, regardless of its global shape. State-of-the-art methods leverage powerful 2D deep learning image-based techniques by converting local patches of the surface into a texture image. However, their effectiveness is guaranteed only when the mesh is simple enough to allow this projection onto a 2D subspace. Therefore, developing deep learning techniques that can work directly on manifolds represents an interesting line of research for addressing these challenges. The objective of our paper is to extend and enhance the architecture described in a recent GNN approach for a relief pattern classifier through the introduction of a new sampling tecnhique for meshes. In their method, local mesh structures, referred to as SpiderPatches, are connected to form the nodes of a graph, called MeshGraph, that captures global structures of the mesh. These two data structures are then fed into a bi-level architecture based on Graph Attention Networks. The MeshGraph construction proves important in ensuring optimal classification results. By the proposed subsampling process, we tackle the problem of fine-tuning multiple hyperparameters inherent the MeshGraph by defining a graph structure that is aware of the mesh geometric details. We demonstrate that the graph constructed using this approach robustly captures the relief patterns on the surface, obviating the need for data augmentation during training. The resulting network is robust, easily customizable, and shows comparable performance to recent methods, all while operating directly on 3D data.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Neural networks; Object identificationComputing methodologiesNeural networksObject identification10.2312/stag.2023129893-1019 pages