4 papers
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Sara Hahner, Souhaib Attaiki, Jochen Garcke +1
We introduce a novel learning-based method for encoding and manipulating 3D surface meshes. Our method is specifically designed to create an interpretable embedding space for defor…
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
Sara Hahner, Felix Kerkhoff, Jochen Garcke
The underlying dynamics and patterns of 3D surface meshes deforming over time can be discovered by unsupervised learning, especially autoencoders, which calculate low-dimensional e…
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes
Sara Hahner, Jochen Garcke
The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics. But, state-of-the-art…
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
Sara Hahner, Rodrigo Iza-Teran, Jochen Garcke
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…