73 citations · 95 across the 7 of their papers we have counts for
8 papers
Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
Johan Vertens, Nicolai Dorka, Tim Welschehold +2
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a…
USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks
Johan Vertens, Wolfram Burgard
In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convol…
Lane Graph Estimation for Scene Understanding in Urban Driving
Jannik Zürn, Johan Vertens, Wolfram Burgard
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such…
HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images
Johan Vertens, Jannik Zürn, Wolfram Burgard
The majority of learning-based semantic segmentation methods are optimized for daytime scenarios and favorable lighting conditions. Real-world driving scenarios, however, entail ad…
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
Oier Mees, Alp Emek, Johan Vertens +1
Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they…
A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans
Alexander Schaefer, Johan Vertens, Daniel Büscher +1
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we…