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 contrast, point-based models are not affected by this problem as they continuously process point clouds. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a following convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance and orientation estimates over networks from previous literature.

翻译：本文介绍了将网格和点基处理相结合的新型混合结构,以改善雷达物体探测网络的探测性能和定向估计,纯粹以网格为基础的探测模型在输入点云的鸟眼视图(BEV)投射中运行。这些方法因通过离散网格分辨率丢失详细信息而受损。这特别适用于雷达物体探测,雷达物体探测通常使用相对粗糙的网格分辨率来计算雷达点云的广度。相反,点基模型由于不断处理点云而不受这一问题的影响。但是,它们一般显示的探测性能比基于网格的方法要差。我们表明,基于点的模型可以在形成网格之前利用精确的相对位置来提取周边特征。这对后继的革命性探测骨干有重大好处。在公共网格网格数据库实验中,我们混合结构的探测性能和定向估计在以往文献的网络上得到了改进。