• Funding : Artois
  • Start year :
  • 2024

This thesis focuses on deep learning, with an emphasis on learning graph representations. Graphs are widely used in many applications, providing a versatile representation for non-regular objects, including 3D meshes, as an alternative to traditional methods such as CNNs or image segmentation models like U-net. This thesis explores graph neural networks (GNNs) for modeling non-regular 3D objects, such as 3D meshes. Unlike CNNs, GNNs are designed to handle graph-type data, making them more suitable for representing 3D meshes. They have demonstrated superior performance in modeling such data, offering a promising alternative to existing methods. However, despite their effectiveness, GNNs face scalability challenges, especially with complex meshes. This thesis proposes solutions to overcome these challenges by exploring mesh-specific pooling methods and other strategies to simplify learning. It also considers approaches for constructing graphs from 3D meshes to enhance learning efficiency. In addition to the static aspect of data, this thesis addresses the application of GNNs to data with temporal patterns or features. It explores their uses in domains such as fluid simulation, weather modeling, and 3D medical imaging, as well as in physical simulation of 3D meshes. This highlights the temporal evolution of meshes in both space and time.

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