no code implementations • 15 Aug 2023 • Marius Lippke, Maurice Quach, Sascha Braun, Daniel Köhler, Michael Ulrich, Bastian Bischoff, Wei Yap Tan
This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources.
no code implementations • 7 Jul 2022 • Daniel Niederlöhner, Michael Ulrich, Sascha Braun, Daniel Köhler, Florian Faion, Claudius Gläser, André Treptow, Holger Blume
Labels for the Cartesian velocities or contiguous sequences, which are expensive to obtain, are not required.
no code implementations • 3 May 2022 • Michael Ulrich, Sascha Braun, Daniel Köhler, Daniel Niederlöhner, Florian Faion, Claudius Gläser, Holger Blume
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 detection networks.