  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.
