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Found 4 papers, 2 papers with code
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner.
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
To study the deformation patterns of unseen shapes by transfer learning, we want to train an autoencoder that can analyze new surface meshes without training a new network.
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes
The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics.
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape.
