  Focusing on the strength of 4D (4-Dimensional) radar, research about robust
3D object detection networks in adverse weather conditions has gained
attention. To train such networks, datasets that contain large amounts of 4D
radar data and ground truth labels are essential. However, the existing 4D
radar datasets (e.g., K-Radar) lack sufficient sensor data and labels, which
hinders the advancement in this research domain. Furthermore, enlarging the 4D
radar datasets requires a time-consuming and expensive manual labeling process.
To address these issues, we propose the auto-labeling method of 4D radar tensor
(4DRT) in the K-Radar dataset. The proposed method initially trains a
LiDAR-based object detection network (LODN) using calibrated LiDAR point cloud
(LPC). The trained LODN then automatically generates ground truth labels (i.e.,
auto-labels, ALs) of the K-Radar train dataset without human intervention. The
generated ALs are used to train the 4D radar-based object detection network
(4DRODN), Radar Tensor Network with Height (RTNH). The experimental results
demonstrate that RTNH trained with ALs has achieved a similar detection
performance to the original RTNH which is trained with manually annotated
ground truth labels, thereby verifying the effectiveness of the proposed
auto-labeling method. All relevant codes will be soon available at the
following GitHub project: https://github.com/kaist-avelab/K-Radar
