New 3+1D high-resolution radar sensors are gaining importance for 3D object detection in the automotive domain due to their relative affordability and improved detection compared to classic low-resolution radar sensors. One limitation of high-resolution radar sensors, compared to lidar sensors, is the sparsity of the generated point cloud. This sparsity could be partially overcome by accumulating radar point clouds of subsequent time steps. This contribution analyzes limitations of accumulating radar point clouds on the View-of-Delft dataset. By employing different ego-motion estimation approaches, the dataset's inherent constraints, and possible solutions are analyzed. Additionally, a learning-based instance motion estimation approach is deployed to investigate the influence of dynamic motion on the accumulated point cloud for object detection. Experiments document an improved object detection performance by applying an ego-motion estimation and dynamic motion correction approach.

翻译：新型3+1D高分辨率雷达传感器因其相对低廉的成本和相较于传统低分辨率雷达传感器更优的检测性能，在汽车领域的3D目标检测中日益受到重视。然而，与激光雷达传感器相比，高分辨率雷达传感器的局限性在于其生成的点云较为稀疏。这种稀疏性可通过累积连续时间步的雷达点云得到部分缓解。本研究基于View-of-Delft数据集，分析了累积雷达点云时存在的局限性。通过采用不同的自车运动估计方法，本文探讨了该数据集固有的约束条件及可能的解决方案。此外，本文还部署了一种基于学习的实例运动估计方法，以研究动态运动对用于目标检测的累积点云的影响。实验结果表明，应用自车运动估计与动态运动校正方法能够有效提升目标检测性能。