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New 3+1D high-resolution radar sensors are gaining importance for 3D objectdetection in the automotive domain due to their relative affordability andimproved detection compared to classic low-resolution radar sensors. Onelimitation of high-resolution radar sensors, compared to lidar sensors, is thesparsity of the generated point cloud. This sparsity could be partiallyovercome by accumulating radar point clouds of subsequent time steps. Thiscontribution analyzes limitations of accumulating radar point clouds on theView-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 deployedto investigate the influence of dynamic motion on the accumulated point cloudfor object detection. Experiments document an improved object detectionperformance by applying an ego-motion estimation and dynamic motion correctionapproach.

Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection