Tim Elsner, M.Sc.
Room 106
Phone: +49 241 8021808
Fax: +49 241 8022899
Email: elsner@cs.rwth-aachen.de


Intuitive Shape Editing in Latent Space

Tim Elsner, Moritz Ibing, Victor Czech, Julius Nehring-Wirxel, Leif Kobbelt

The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enables intuitive shape editing in latent space by disentangling latent sub-spaces into style variables and control points on the surface that can be manipulated independently. The key idea is adding a Lipschitz-type constraint to the loss function, i.e. bounding the change of the output shape proportionally to the change in latent space, leading to interpretable latent space representations. The control points on the surface that are part of the latent code of an object can then be freely moved, allowing for intuitive shape editing directly in latent space. We evaluate our method by comparing to state-of-the-art data-driven shape editing methods. We further demonstrate the expressiveness of our learned latent space by leveraging it for unsupervised part segmentation.

Highly accurate digital traffic recording as a basis for future mobility research: Methods and concepts of the research project HDV-Mess

Philip Trettner, Tim Elsner, Julius Nehring-Wirxel, Kersten Schuster, Leif Kobbelt

The research project HDV-Mess aims at a currently missing, but very crucial component for addressing important challenges in the field of connected and automated driving on public roads. The goal is to record traffic events at various relevant locations with high accuracy and to collect real traffic data as a basis for the development and validation of current and future sensor technologies as well as automated driving functions. For this purpose, it is necessary to develop a concept for a mobile modular system of measuring stations for highly accurate traffic data acquisition, which enables a temporary installation of a sensor and communication infrastructure at different locations. Within this paper, we first discuss the project goals before we present our traffic detection concept using mobile modular intelligent transport systems stations (ITS-Ss). We then explain the approaches for data processing of sensor raw data to refined trajectories, data communication, and data validation.

» Show BibTeX

author = {Laurent Kloeker and
Fabian Thomsen and
Lutz Eckstein and
Philip Trettner and
Tim Elsner and
Julius Nehring{-}Wirxel and
Kersten Schuster and
Leif Kobbelt and
Michael Hoesch},
title = {Highly accurate digital traffic recording as a basis for future mobility
research: Methods and concepts of the research project HDV-Mess},
journal = {CoRR},
volume = {abs/2106.04175},
year = {2021},
url = {https://arxiv.org/abs/2106.04175},
eprinttype = {arXiv},
eprint = {2106.04175},
timestamp = {Fri, 11 Jun 2021 11:04:16 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-04175.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}

Disclaimer Home Visual Computing institute RWTH Aachen University