LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
Jingyu Song Lingjun Zhao Katherine A. Skinner
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to
fill the performance gap of existing LiDAR-radar detectors. To improve the
feature extraction capabilities from these two modalities, we design an early
fusion module for joint voxel feature encoding, and a middle fusion module to
adaptively fuse feature maps via a gated network. We perform extensive
evaluation on nuScenes to demonstrate that LiRaFusion leverages the
complementary information of LiDAR and radar effectively and achieves notable
improvement over existing methods.