20 citations · 50 across the 10 of their papers we have counts for
7 papers · 1 filter
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Felicia Ruppel, Florian Faion, Claudius Gläser +1
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention…
Transformers for Object Detection in Large Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field o…
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…
Transformers for Multi-Object Tracking on Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and…
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…
Understanding the Domain Gap in LiDAR Object Detection Networks
Jasmine Richter, Florian Faion, Di Feng +3
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it…