17 citations · 32 across the 7 of their papers we have counts for
7 papers
DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representation
Mohamed Abdelsamad, Michael Ulrich, Bin Yang +3
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point c…
Exploiting Sparsity in Automotive Radar Object Detection Networks
Marius Lippke, Maurice Quach, Sascha Braun +4
Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fund…
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
Daniel Köhler, Maurice Quach, Michael Ulrich +3
Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. Howev…
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
Florian Drews, Di Feng, Florian Faion +3
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection. Specialized feature extractors take…
Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
Daniel Niederlöhner, Michael Ulrich, Sascha Braun +5
This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms…
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
Michael Ulrich, Sascha Braun, Daniel Köhler +4
This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object d…
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
Michael Ulrich, Claudius Gläser, Fabian Timm
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class informa…