4 papers
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik +3
Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with a…
DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies
Soshi Shimada, Vladislav Golyanik, Edgar Tretschk +2
We introduce a supervised-learning framework for non-rigid point set alignment of a new kind - Displacements on Voxels Networks (DispVoxNets) - which abstracts away from the point…
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
Edgar Tretschk, Ayush Tewari, Michael Zollhöfer +2
Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder (DEMEA) which adds a novel embedded…
Sequential Attacks on Agents for Long-Term Adversarial Goals
Edgar Tretschk, Seong Joon Oh, Mario Fritz
Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks. With the great effective…