Temporal Axial Attention For Lidar-Based 3d Object Detection In Autonomous Driving

3D object detection is a core problem of the perception systems of autonomous vehicles. Despite recent progress in the field, the temporal aspect of LiDAR data has not been fully explored in current state-of-the-art detectors. This work proposes a modified CenterPoint architecture that uses temporal axial attention to exploit the sequential nature of autonomous driving data for 3D object detection. The last ten LiDAR sweeps are split into three groups of frames, and the axial attention transformer block captures both spatial and temporal dependencies among the features extracted from each group. Our proposal is evaluated using the nuScenes dataset. With this novel approach, we obtain an average mAP improvement of 3.8 and 2.3 points over the original CenterPoint in the fine/coarse pillar settings, respectively.