1 citations · 1 across the 7 of their papers we have counts for
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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…
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…
A Compact Spectral Descriptor for Shape Deformations
Skylar Sible, Rodrigo Iza-Teran, Jochen Garcke +2
Modern product design in the engineering domain is increasingly driven by computational analysis including finite-element based simulation, computational optimization, and modern d…