  Recent works have shown the superior robustness of four-dimensional (4D)
Radar-based three-dimensional (3D) object detection in adverse weather
conditions. However, processing 4D Radar data remains a challenge due to the
large data size, which require substantial amount of memory for computing and
storage. In previous work, an online density reduction is performed on the 4D
Radar Tensor (4DRT) to reduce the data size, in which the density reduction
level is chosen arbitrarily. However, the impact of density reduction on the
detection performance and memory consumption remains largely unknown. In this
paper, we aim to address this issue by conducting extensive hyperparamter
tuning on the density reduction level. Experimental results show that
increasing the density level from 0.01% to 50% of the original 4DRT density
level proportionally improves the detection performance, at a cost of memory
consumption. However, when the density level is increased beyond 5%, only the
memory consumption increases, while the detection performance oscillates below
the peak point. In addition to the optimized density hyperparameter, we also
introduce 4D Sparse Radar Tensor (4DSRT), a new representation for 4D Radar
data with offline density reduction, leading to a significantly reduced raw
data size. An optimized development kit for training the neural networks is
also provided, which along with the utilization of 4DSRT, improves training
speed by a factor of 17.1 compared to the state-of-the-art 4DRT-based neural
networks. All codes are available at: https://github.com/kaist-avelab/K-Radar.
