  Architectures that first convert point clouds to a grid representation and
then apply convolutional neural networks achieve good performance for
radar-based object detection. However, the transfer from irregular point cloud
data to a dense grid structure is often associated with a loss of information,
due to the discretization and aggregation of points. In this paper, we propose
a novel architecture, multi-scale KPPillarsBEV, that aims to mitigate the
negative effects of grid rendering. Specifically, we propose a novel grid
rendering method, KPBEV, which leverages the descriptive power of kernel point
convolutions to improve the encoding of local point cloud contexts during grid
rendering. In addition, we propose a general multi-scale grid rendering
formulation to incorporate multi-scale feature maps into convolutional
backbones of detection networks with arbitrary grid rendering methods. We
perform extensive experiments on the nuScenes dataset and evaluate the methods
in terms of detection performance and computational complexity. The proposed
multi-scale KPPillarsBEV architecture outperforms the baseline by 5.37% and the
previous state of the art by 2.88% in Car AP4.0 (average precision for a
matching threshold of 4 meters) on the nuScenes validation set. Moreover, the
proposed single-scale KPBEV grid rendering improves the Car AP4.0 by 2.90% over
the baseline while maintaining the same inference speed.
