Mesh Autoencoder

Mesh autoencoders are deep learning models designed to learn efficient representations of 3D surface meshes, aiming to encode complex shapes into lower-dimensional latent spaces for tasks like shape manipulation, reconstruction, and analysis. Current research emphasizes unsupervised learning approaches, often employing spectral techniques or novel graph structures within the autoencoder architecture to handle variations in mesh connectivity and achieve better generalization across diverse shape categories. These advancements are significantly impacting fields like medical imaging (e.g., craniofacial analysis and surgical planning) and computer graphics (e.g., realistic 3D model generation and manipulation), enabling more robust and interpretable analysis of complex 3D data.