Semantic-Guided Multi-Feature Attention Aggregation Network for LiDAR-Based 3D Object Detection

The sparse and uneven distribution of point clouds in LiDAR-captured outdoor scenes poses significant challenges for 3D object detection in autonomous driving. Specifically, the imbalance between foreground and background points can degrade detection accuracy. While existing approaches attempt to address this issue through sampling or segmentation strategies, effectively retaining informative foreground points and integrating features from multiple sources remains a challenge. To tackle these issues, we propose SMA2, a semantic-guided multi-feature attention aggregation network. It consists of two key components: the Keypoint Attention Enhancement (KAE) module, which refines keypoints by leveraging semantic information through attention-based local aggregation, and the Multi-Feature Attention Aggregation (MFAA) module, which adaptively integrates keypoint, voxel, and BEV features using a keypoint-guided attention mechanism. Compared to existing fusion methods such as PV-RCNN, SMA2 provides a more flexible and context-aware feature integration strategy. Experimental results on the KITTI test set demonstrate consistent performance improvements, especially in detection accuracy for small and distant objects. Additional tests on the Waymo and DAIR-V2X-V datasets further highlight the method’s strong generalization capability across diverse environments.