EFNet: enhancing feature information for 3D object detection in LiDAR point clouds

With the development of autonomous driving, there has been considerable attention on 3D object detection using LiDAR. Pillar-based LiDAR point cloud detection algorithms are extensively employed in the industry due to their simple structure and high real-time performance. Nevertheless, the pillar-based detection network suffers from significant loss of 3D coordinate information during the feature degradation and extraction process. In the paper, we introduce a novel framework with high performance, termed EFNet. The EFNet uses the Enhancing Pillar Feature Module (EPFM) to provide more accurate representations of features from two directions: pillar internal space and pillar external space. Additionally, the Head Up Module (HUM) is utilized in the detection head to integrate multi-scale information and enhance the network's information perception ability. The EFNet achieves impressive results on the nuScenes datasets, namely, 53.3% NDS and 42.4% mAP. Compared to the baseline PointPillars, EFNet improves 8% NDS and 11.9% mAP. The results demonstrate that the proposed framework can effectively improve the network's accuracy while ensuring deployability.