Web: http://arxiv.org/abs/2205.02111

May 5, 2022, 1:10 a.m. | Michael Ulrich, Sascha Braun, Daniel Köhler, Daniel Niederlöhner, Florian Faion, Claudius Gläser, Holger Blume

cs.CV updates on arXiv.org arxiv.org

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. Purely grid-based
detection models operate on a bird's-eye-view (BEV) projection of the input
point cloud. These approaches suffer from a loss of detailed information
through the discrete grid resolution. This applies in particular to radar
object detection, where relatively coarse grid resolutions are commonly used to
account for the sparsity of radar point clouds. In …

arxiv automotive cv detection hybrid networks radar

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