no code implementations • 27 Sep 2023 • Lukas Stäcker, Philipp Heidenreich, Jason Rambach, Didier Stricker
By exploiting complementary sensor information, radar and camera fusion systems have the potential to provide a highly robust and reliable perception system for advanced driver assistance systems and automated driving functions.
no code implementations • 25 May 2023 • Lukas Stäcker, Shashank Mishra, Philipp Heidenreich, Jason Rambach, Didier Stricker
Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research.
no code implementations • 18 Aug 2021 • Lukas Stäcker, Juncong Fei, Philipp Heidenreich, Frank Bonarens, Jason Rambach, Didier Stricker, Christoph Stiller
We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform.
1 code implementation • 1 Jul 2021 • Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen
At the heart of all automated driving systems is the ability to sense the surroundings, e. g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg.
no code implementations • 10 May 2021 • Juncong Fei, Kunyu Peng, Philipp Heidenreich, Frank Bieder, Christoph Stiller
The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios.
no code implementations • 25 Sep 2020 • Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, Christoph Stiller
Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information.
1 code implementation • ACL 2019 • Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alex Bondarenko, Matthias Hagen, Chris Biemann, Alex Panchenko, er
We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus.